Sharif Digital Repository

Nondestructive tests and evaluations are robust techniques for inspecting different attributes of concrete configuration. However, most nondestructive techniques focused on an aspect of concrete configuration based on comparison to other samples. In this paper, an innovative inverse analysis technique was developed to inspect different attributes of concrete configuration simultaneously. The methodology was based on the scattering feature of the ultrasonic waves during propagation in heterogeneous media. The transition matrix method was employed to determine the scattered wavefield. This method considers the shape of objects, unlike most other numerical methods. Furthermore, a novel... 

User churn is an important issue in online services that threatens the health and profitability of services. Most of the previous works on churn prediction convert the problem into a binary classification task where the users are labeled as churned and non-churned. More recently, some works have tried to convert the user churn prediction problem into the prediction of user return time. In this approach which is more realistic in real world online services, at each time-step the model predicts the user return time instead of predicting a churn label. However, the previous works in this category suffer from lack of generality and require high computational complexity. In this paper, we... 

This work develops a systematic uncertainty quantification framework to assess the reliability of prediction delivered by physics-based material models in the presence of incomplete measurement data and modeling error. The framework consists of global sensitivity analysis, Bayesian inference, and forward propagation of uncertainty through the computational model. The implementation of this framework on a new multiphase model of novel porous silica aerogel materials is demonstrated to predict the thermomechanical performances of a building envelope insulation component. The uncertainty analyses rely on sampling methods, including Markov-chain Monte Carlo and a mixed finite element solution of... 

Rapid assessment of structural safety and performance right after the occurrence of significant earthquake shaking is crucial for building owners and decision-makers to make informed risk management decisions. Hence, it is vital to develop online and pseudo-online health monitoring methods to quantify the health of the building right after significant earthquake shaking. Many Bayesian inference–based methods have been developed in the past which allow the users to estimate the unknown states and parameters. However, one of the most challenging part of the Bayesian inference–based methods is the determination of the parameter noise covariance matrix. It is especially difficult when the number... 

Rapid assessment of structural safety and performance right after the occurrence of significant earthquake shaking is crucial for building owners and decision-makers to make informed risk management decisions. Hence, it is vital to develop online and pseudo-online health monitoring methods to quantify the health of the building right after significant earthquake shaking. Many Bayesian inference–based methods have been developed in the past which allow the users to estimate the unknown states and parameters. However, one of the most challenging part of the Bayesian inference–based methods is the determination of the parameter noise covariance matrix. It is especially difficult when the number... 

In this paper, we consider the problem of scheduling the power of a sensor when transmitting over an unknown Gilbert-Elliott (GE) channel for remote state estimation. The sensor supports two power modes, namely low power and high power, which are to be selected for transmission over the channel in order to minimize a cost on the error covariance, while satisfying the energy constraints. The remote estimator provides error-free acknowledgement/negative-acknowledgement (ACK/NACK) messages to the sensor only when low power is utilized. We first consider the Partially Observable Markov Decision Process (POMDP) problem for the case of known GE channels and derive conditions for optimality of a... 

A Takagi-Sugeno adaptive neuro-fuzzy inference system (TSFIS) model is developed and applied to a dataset of wellhead flow-test data for the Resalat oil field located offshore southern Iran, the objective is to assist in the prediction and control of multi-phase flow rates of oil and gas through the wellhead chokes. For this purpose, 182 test data points (Appendix 1) related to the Resalat field are evaluated. In order to predict production flow rate (QL) expressed as stock-tank barrels per day (STB/D), this dataset includes four selected input variables: upstream pressure (Pwh); wellhead choke sizes (D64); gas to liquid ratio (GLR); and, base solids and water including some water-soluble... 

Poisson Factorization (PF) is the gold standard framework for recommendation systems with implicit feedback whose variants show state-of-the-art performance on real-world recommendation tasks. However, they do not explicitly take into account the temporal behavior of users which is essential to recommend the right item to the right user at the right time. In this paper, we introduce Recurrent Poisson Factorization (RPF) framework that generalizes the classical PF methods by utilizing a Poisson process for modeling the implicit feedback. RPF treats time as a natural constituent of the model, and takes important factors for recommendation into consideration to provide a rich family of... 

Spinal posture including thorax/pelvis orientations as well as loads on the intervertebral discs are crucial parameters in biomechanical models and ergonomics to evaluate the risk of low back injury. In vivo measurement of spinal posture toward estimation of spine loads requires the common motion capture techniques and laboratory instruments that are costly and time-consuming. Hence, a closed loop algorithm including an artificial neural network (ANN) and fuzzy logic is proposed here to predict the L5–S1 segment loads and thorax/pelvis orientations in various 3D reaching activities. Two parts namely a fuzzy logic strategy and an ANN from this algorithm; the former, developed based on the... 

Using natural draft dry air cooling systems in the power plant cycle is one of the proposed solutions for less water consumption. But the wind blowing will cause decreasement of cooling system performance in the power plants that work with the Rankin cycle. Therefore, it is important to know the right amount of wind speed to make the right decision to prevent reducing generating power or provide the right solution to improve the performance of the power plant cooling system. There are many methods of calibration of sensors in the world. But using optimization techniques or stochastic methods that do not require physical facilities and additional costs is almost a new approach. Therefore, in... 

Active learning method (ALM) is a powerful fuzzy-based soft computing methodology suitable for various applications such as function modeling, control systems, clustering and classification. Despite considerable advantages, the main computational engine of ALM, ink drop spread (IDS), is memory-intensive, which imposes significant area overheads in the hardware realization of the ALM for real-time applications. In this paper, we propose a compressed model for ALM which greatly alleviates the storage limitations. The proposed approach employs a distinct inference algorithm, enabling a significant reduction in memory utilization from O(N2) to O(2N) for a multi-input single-output (MISO) system.... 

Online social networks, World Wide Web, media, and technological networks, and other types of so-called information networks are ubiquitous nowadays. These information networks are inherently heterogeneous and dynamic. They are heterogeneous as they consist of multi-Typed objects and relations, and they are dynamic as they are constantly evolving over time. One of the challenging issues in such heterogeneous and dynamic environments is to forecast those relationships in the network that will appear in the future. In this article, we try to solve the problem of continuous-Time relationship prediction in dynamic and heterogeneous information networks. This implies predicting the time it takes... 

One of the most prevalent problems in drilling industry is lost circulation which causes intense increase in drilling expenditure as well as operational obstacles such as well instability and blowout. The aim of this research is to develop smart systems for estimating amount of lost circulation making able to use appropriate prevention and remediation methods. To obtain this aim, a large data set were collected from 61 recently drilled wells in Marun oil field in Iran to be used for developing relevant models. After that, using the extracted data set consisting of 1900 data subset, intelligent prediction models including decision tree (DT), adaptive neuro-fuzzy inference systems (ANFIS),... 

A new time-domain hierarchical Bayesian framework is proposed to improve the performance of Bayesian methods in terms of reliability and robustness of estimates particularly for uncertainty quantification and propagation in structural dynamics. The proposed framework provides a reliable approach to account for the variability of the inference results observed when using different data sets. The proposed formulation is compared with a state-of-the-art Bayesian approach using numerical and experimental examples. The results indicate that the hierarchical Bayesian framework provides a more realistic account of the uncertainties, whereas the non-hierarchical Bayesian approach severely... 

A statistical algorithm is introduced to improve the false inference of active loci, in the population in which members are admixture. The algorithm uses an advanced clustering algorithm based on a Bayesian approach. The proposed algorithm simultaneously infers the hidden structure of the population. In this regard, the Monte Carlo Markov Chain (MCMC) algorithm has been used to evaluate the posterior probability distribution of the model parameters. The proposed algorithm is implemented in a bundle, and then its performance is widely evaluated in a number of artificial databases. The accuracy of the clustering algorithm is compared with the STRUCTURE method based on certain criterion. © 2019... 

In the open-pit mines and civil projects, drilling and blasting is the most common method for rock fragmentation aims. This article proposes a new hybrid forecasting model based on firefly algorithm, as an algorithm optimizer, combined with the adaptive neuro-fuzzy inference system for estimating the fragmentation. In this regard, 72 datasets were collected from Shur river dam region, and the required parameters were measured. Using the different input parameters, six hybrid models were constructed. In these models, 58 and 14 data were used for training and testing, respectively. The proposed hybrid models were then evaluated in accordance with statistical criteria such as coefficient of... 

Multiple human 3D pose estimation is a challenging task. It is mainly because of large variations in the scale and pose of humans, fast motions, multiple persons in the scene, and arbitrary number of visible body parts due to occlusion or truncation. Some of these ambiguities can be resolved by using multiview images. This is due to the fact that more evidences of body parts would be available in multiple views. In this work, a novel method for multiple human 3D pose estimation using evidences in multiview images is proposed. The proposed method utilizes a fully connected pairwise conditional random field that contains two types of pairwise terms. The first pairwise term encodes the spatial... 

Asphaltene precipitation in petroleum industries is known as major problems. To solve problems there are approaches for inhibition of asphaltene precipitation, Asphaltene inhibitors are known effective and economical approach for inhibition and prevention of asphaltene precipitation. In the present study Adaptive neuro-fuzzy inference system (ANFIS) was coupled with Particle swarm optimization (PSO) to create a novel approach to predict effect of inhibitors on asphaltene precipitation as function of crude oil properties and concentration and structure of asphaltene inhibitors.in order to training and testing the algorithm, a total number of 75 experimental data was gathered from the... 

This paper presents a comparative study of various soft computing algorithms for reconstruction of neutron noise sources in the nuclear reactor cores. To this end, the computational code for reconstruction of neutron noise source is developed based on the Adaptive Neuro-Fuzzy Inference System (ANFIS), Decision Tree (DT), Radial Basis Function (RBF) and Support Vector Machine (SVM) algorithms. Neutron noise source reconstruction process using the developed computational code consists of three stages of training, testing and validation. The information of neutron noise sources and induced neutron noise distributions are used as output and input data of training stage, respectively. As input... 

In the open-pit mines and civil projects, drilling and blasting is the most common method for rock fragmentation aims. This article proposes a new hybrid forecasting model based on firefly algorithm, as an algorithm optimizer, combined with the adaptive neuro-fuzzy inference system for estimating the fragmentation. In this regard, 72 datasets were collected from Shur river dam region, and the required parameters were measured. Using the different input parameters, six hybrid models were constructed. In these models, 58 and 14 data were used for training and testing, respectively. The proposed hybrid models were then evaluated in accordance with statistical criteria such as coefficient of...