Sharif Digital Repository

River water quality monitoring using traditional water sampling and laboratory analyses is expensive and time-consuming. The application of artificial neural network (ANN) models to simulate water quality parameters is cost-effective, quick, and reliable. This study provides two methods of mathematical and ANN modeling to simulate and forecast five important river water quality indicators (DO, TDS, SAR, BOD5, HCO3) correlated with variables such as EC, temperature, and pH which can be measured easily and almost with no cost. The mathematical method is based on polynomial fitting with least square method and the neural network model was developed using a feed-forward algorithm. The 35 years’... 

Neural Networks (FFNNs) that are trained with the Levenberg-Marquardt back-propagation algorithm were used. Models were evaluated using mean squared error (MSE) and coefficient of determination (R2) as statistical performance parameters. In traffic noise modelling, the noise level at a receptor position due to the source of traffic emission is modelled as a function of the traffic conditions, road gradient, road dimensions, speed and height of buildings around the road. The curse of dimensionality problems is caused by the large number of input variables in the ANN model. The Hybrid Genetic Algorithm-Gamma Test (GA-GT) as a data pre-processing method for determining adequate model inputs was... 

CO2 sequestration in geological formations, such as aquifers, is known to be the best short-term solution to CO2 mitigation. Accurate description of CO2 solubility in brine is important for evaluating the capacity of aquifers to sequester CO2. Currently, EOS-based models are widely used in reservoir compositional simulators for this purpose. However, most of these models involve complex and iterative calculations which take too much time in case of large-scale flow simulation of geological CO2 storage. In this study, a neural network model is presented for prediction of CO2 solubility in brine which is highly accurate with less computational overhead  

This paper introduces a new procedure to forecast the future O/D demand. It is a hybrid of logit and Fratar model. The hybrid model has the long run, policy sensitive, characteristic of a logit model, calibrated at sector-level with little/no zero O/D cells. This feature, joint with a Fratar-type operation at zonal level within a sector, gives a better performance to this model than either of the two types of the models alone. The performance of the hybrid model is contrasted with a neural network model, and shows encouraging results in a real case  

Text processing, as one of the main issues in the field of artificial intelligence, has received a lot of attention in recent decades. Numerous methods and algorithms are proposed to address the task of semantic textual similarity which is one of the sub-branches of text processing. Due to the special features of the Persian language and its non-standard writing system, finding semantic similarity is an even more challenging task in Persian. On the other hand, producing a proper corpus that can be used for training a model for finding semantic similarities, is of great importance. In this study, the main purpose is to propose a method for measuring the semantic similarity between short... 

The authors studied the efficiency and accuracy of neural network model for prediction of permeability as a key parameter in reservoir characterization. So, some multilayer perceptron (MLP) neural network models with different learning algorithms of Levenberg-Margnardt, back propagation, improved back propagation (IBP), and quick propagation with three layers and different node numbers (3, 4, 5, 6, 7) in the middle layer have been presented. These models have been obtained by 630 permeability data from one of offshore reservoirs located in Saudi Arabia. The accuracy of models was studied by comparing the obtained results of each model with experimental data. So, the neural network with IBP... 

A neural network (ANN) model was developed to predict the densification of composite powders in a rigid die under uniaxial compaction. Al-SiC powder mixtures with various reinforcement volume fractions (0-30%) and particle sizes (50 nm to 40 μm) were prepared and their compressibility was studied in a wide range of compaction pressure up to 400 MPa. The experimental results were used to train a back propagation (BP) learning algorithm with two hidden layers. A sigmoid transfer function was developed and found to be suitable for analyzing the compressibility of composite powders with the least error. The trained model was used to study the effect of reinforcement particle size and volume... 

Abstract: The hot deformation behavior of coarse-grained (CG), ultrafine-grained (UFG), and oxide dispersion-strengthened (ODS) AA6063 is experimentally recognized though carrying out compression tests at different temperatures (300–450 °C) and strain rates (0.01–1 s−1). Microstructural studies conducted by TEM and EBSD indicate that dynamic softening mechanisms including dynamic recovery and dynamic recrystallization become operative in all the investigated materials depending on the regime of deformation. Moreover, the high temperature flow behavior is considerably influenced by the initial grain structure and the presence of reinforcement particles. The constitutive and artificial neural... 

In this study, a two-layer feed-forward neural network was constructed and applied to determine a mapping associating mix design and testing factors of cement-nano silica (NS)-rice husk ash ternary blended concrete samples with their performance in conductance to the water absorption properties. To generate data for the neural network model (NNM), a total of 174 field cores from 58 different mixes at three ages were tested in the laboratory for each of percentage, velocity and coefficient of water absorption and mix volumetric properties. The significant factors (six items) that affect the permeability properties of ternary blended concrete were identified by experimental studies which were:... 

An artificial neural network (ANN) model was used to predict the weight loss of MgO-C composites at different temperatures and graphite contents. The general idea of ANN modeling was presented and after that the empirical weight loss data were used for both model verification and assessment of the oxidation rate predictions. The model was proved to have an astounding power in predicting kinetic parameters of the oxidation process. Graphite oxidation was, for example, found to be controlled by alternative diffusion steps. Plotting the Arrhenius law curves for graphite oxidation indicated a distinguishable slope change at a critical temperature which is related to the graphite content. This... 

In this work, the modified Flory-Huggins coupled with the free-volume concept and the artificial neural network models were used to obtain the osmotic pressure of aqueous poly(ethylene glycol) solutions. In the artificial neural network, the osmotic pressure of aqueous poly(ethylene glycol) solutions depends on temperature, molecular weight and the mole fractions of poly(ethylene glycol) in aqueous solution. The network topology is optimized and the (3-1-1) architecture is found using optimization of an objective function with batch back propagation (BBP) method for 134 experimental data points. The results obtained from the neural network in obtaining of the osmotic pressure of aqueous... 

Embryogenesis, regeneration and cell differentiation in microbiological entities are influenced by mechanical forces. Therefore, development of mechanical properties of these materials is important. Neural network technique is a useful method which can be used to obtain cell deformation by the means of force-geometric deformation data or vice versa. Prior to insertion in the needle injection process, deformation and geometry of cell under external point-load is a key element to understand the interaction between cell and needle. In this paper the goal is the prediction of cell membrane deformation under a certain force, and to visually estimate the force of indentation on the membrane from... 

An Artificial Neural Network (ANN) model has been designed for predicting the effects of alloying elements (Fe, Si, Al, Mn) on the recrystallization behavior and microstructural changes of 70/30 brass. The model introduced here considers the content of alloying elements, temperature, and time of recrystallization as inputs while percent of recrystallization is presented as output. It is shown that the designed model is able to predict the effect of alloying elements well. It is also shown that all alloying elements strongly affect the recrytallization kinetics, and all slow down the recrystallization process. The effect of alloying elements on the activation energy for recrystallization has... 

Statistical bias correction methods are inferred relationships between inputs and outputs. The constructed functions are based on available observations, which are limited in time and space. This study investigates the ability of regression models (linear and nonlinear) to regionalize a domain by defining a minimum number of training pixels necessary to achieve a good level of bias correction performance. Linear regression is used to divide northern South America into five regions. To correct the biases of temperature and precipitation, an artificial neural network (ANN) model was trained with selected pixels within each region and then used to reproduce bias-corrected temperature and... 

Context: During the development process of a software program, regression testing is used to ensure that the correct behavior of the software is retained after updates to the source code. This regression testing becomes costly over time as the number of test cases increases and it makes sense to prioritize test cases in order to execute fault-detecting test cases as soon as possible. There are many coverage-based test case prioritization (TCP) methods that only use the code coverage data to prioritize test cases. By incorporating the fault-proneness estimations of code units into the coverage-based TCP methods, we can improve such techniques. Objective: In this paper, we aim to propose an... 

Taxonomy learning is one of the major steps in ontology learning process. Manual construction of taxonomies is a time-consuming and cumbersome task. Recently many researchers have focused on automatic taxonomy learning, but still quality of generated taxonomies is not satisfactory. In this paper we have proposed a new compound similarity measure. This measure is based on both knowledge poor and knowledge rich approaches to find word similarity. We also used Machine Learning Technique (Neural Network model) for combination of several similarity methods. We have compared our method with simple syntactic similarity measure. Our measure considerably improves the precision and recall of automatic... 

Truck accidents are an issue of concern due to their severity. Logit modeling and Neural Network modeling are performed to investigate factors such as vehicle, roadway, environment and driver characteristics that can potentially contribute to the severity of truck accidents. The objective of this study is to present models that can predict the severity of truck accidents and to identify the important factors causing these accidents. Comparison between neural networks and logit modeling are made using vehicle crash data on two-lane rural highways in Iran. A variety of variables related to roadways, vehicles, environment and drivers, such as, driver fatigue, head-on collision and lack of... 

Abstract: In the present study, a comprehensive investigation was accomplished on shot peening of AISI 316 L steel with a wide range of Almen intensity and surface coverage. Various experiments were performed to characterize the microstructure and mechanical properties of the peened specimens. For the modeling of the process, artificial neural network was used and the obtained experimental results were employed as data-set to develop the network. Modeling results have remarkable agreement with the experiments and then parametric analysis were applied based on the predicted values of the model. Graphic Abstract: [Figure not available: see fulltext.]. © 2019, The Korean Institute of Metals and... 

The problem of controlling an all-thruster spacecraft in the coupled translational-rotational motion in presence of actuators fault and/or failure is investigated in this paper. the nonlinear model predictive control approach is used because of its ability to predict the future behavior of the system. The fault/failure of the thrusters changes the mapping between the commanded forces to the thrusters and actual force/torque generated by the thruster system. Thus, the basic six degree-of-freedom kinetic equations are separated from this mapping and a set of neural networks are trained off-line to learn the kinetic equations. Then, two neural networks are attached to these trained networks in...