Sharif Digital Repository

Intelligent solutions, based on artificial intelligence (AI) technologies, to solve complicated practical problems in various sectors are becoming more and more widespread nowadays, because of their flexibility, symbolic reasoning, and explanation capabilities. Meanwhile, accurate forecasts on tourism demand and study on the pattern of the tourism demand from various origins is essential for the tourism-related industries to formulate efficient and effective strategies on maintaining and boosting tourism industry in a country. In this paper we develop a hybrid AI model to deal with tourist arrival forecasting problems. The hybrid model adopts Adaptive Neuro-Fuzzy Inference System (ANFIS) and... 

Alzheimer’s disease (AD) is currently ranked as the sixth leading cause of death in the United States and recent estimates indicate that the disorder may rank third, just behind heart disease and cancer, as a cause of death for older people. Clearly, predicting this disease in the early stages and preventing it from progressing is of great importance. The diagnosis of Alzheimer’s disease (AD) requires a variety of medical tests, which leads to huge amounts of multivariate heterogeneous data. It can be difficult and exhausting to manually compare, visualize, and analyze this data due to the heterogeneous nature of medical tests, therefore, an efficient approach for accurate prediction of the... 

In this paper, dynamic modelling and control of WindPACT 1.5 MW wind turbine in Region 2 for extracting the maximum energy from wind is investigated (where the wind velocity is greater than ‘cut in’ and below ‘rated’ wind speeds). In this region, the generator torque must regulate the rotor speed in its optimal value while the blade pitch angle is considered constant in its optimal value. To achieve a more accurate model, wind turbine is modeled as an electromechanical system with two masses dynamics. A new method based on adaptive neuro fuzzy inference system (ANFIS) is considered for wind speed estimation; where rotor speed, output power and pitch angle are inputs of such system and... 

A novel UWB MIMO antenna with band-notched characteristic operating in the frequency range between 3.1 GHz and 10.6 GHz is presented. The designed MIMO antenna has two ports and the size of 32×14 mm2 which is fabricated on an FR-4 printed-circuit-board. The feeding system of the proposed antenna is a microstrip line. The two antennas are positioned face to face but laid reversely upon the substrate in order to have a good isolation, less cross polarization, high gain and good envelope correlation coefficient. S-parameters describe the input-output relationship between ports (or terminals) in a MIMO antenna. The effects of the strip feed line width on the variations in reflection coefficient... 

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... 

Purpose - The purpose of this paper is to devise a new approach to synthesize closed-loop feedback guidance law for online thrust- insensitive optimal trajectory generation utilizing neural networks. Design/methodology/approach - The proposed methodology utilizes an open- loop variational formulation that initially determines optimal launch/ ascent trajectories for various scenarios of known uncertainties in the thrust profile of typical solid propellant engines. These open-loop optimized trajectories will then provide the knowledge base needed for the subsequent training of a neural network. The trained network could eventually produce thrust-insensitive closed-loop optimal guidance laws... 

The stability issue of mobile manipulators, particularly when the end-effector and the vehicle have to follow a predefined trajectory (for some special duties like painting a plane or carrying a light load), is a crucial subject and needs special attention. In this paper, by utilizing the manipulator compensation motions, the instantaneous proper configuration for a redundant mobile robot is determined. A fast methodology taking into account the dynamic interaction between the manipulator and the vehicle is proposed for enhancing the tipover stability (i.e. stability against overturning) of the mobile manipulator by employing the soft computing approach including a genetic algorithm, neural... 

This research deals with developing an intelligent trajectory tracking control approach for an aircraft in the presence of internal and external disturbances. Internal disturbances including actuators faults, unmodeled dynamics, and model uncertainties as well as the external disturbances such as wind turbulence significantly affect the performance of the common trajectory tracking control approaches. There are several fault-tolerant control approaches in the literature to overcome the effects of specific actuator or sensor faults during the flight. However, trajectory tracking control of an air vehicle in the presence of unexpected faults and simultaneous presence of wind turbulence is... 

In this paper, some important operating parameters of nuclear power plants (NPPs) transients are forecasted using different supervised learning methods including feed-forward back propagation (FFBP) neural networks such as cascade feed-forward neural network (CFFNN), statistical methods such as support vector regression (SVR), and localized networks such as radial basis network (RBN). Different learning algorithms, including gradient descent (GD), gradient descent with momentum (GDM), scaled conjugate gradient (SCG), Levenberg-Marquardt (LM), and Bayesian regularization (BR) are used in CFFNN method. SVR method is used with different kernel functions including Gaussian, polynomial, and... 

This study proposes a novel socio-hydrological modeling framework for assessing the performance of complex water resources systems. It employs and integrates agent-based modeling (ABM) and the theory of planned behavior (TPB) into the socio-hydrological modeling framework to account for agents’ behaviors. Due to farmers’ major role in anthropogenic droughts, this paper mainly focuses on farmers’ behavior. The TPB framework and the agents’ behavioral rules in ABM are structured based on the data obtained from field questionnaires and interviews by the farmers in the Zarrineh River Basin as the main river feeding the Urmia Lake. The proposed modeling framework, including the TPB and ABM... 

This paper presents a developed computational code based on Support Vector Machine (SVM) for reconstruction of energy spectrum of neutron source. To reconstruct unknown energy spectrum using known neutron pulse height distribution, the developed machine is trained by known neutron pulse height distribution of detector and corresponding energy spectrum of neutron source. Validation and testing are the next steps to verify the validity of the calculations done with the developed computational code. The calculated neutron pulse height distributions due to randomly generated energy spectrum using MCNPX-ESUT (MCNPX-Energy engineering of Sharif University of Technology) computational code are used... 

This article summarizes the first and second Iranian brain-computer interface competitions held in 2017 and 2018 by the National Brain Mapping Lab. Two 64-channel electroencephalography (EEG) datasets were contributed, including motor imagery as well as motor execution by three limbs. The competitors were asked to classify the type of motor imagination or execution based on EEG signals in the first competition and the type of executed motion as well as the movement onset in the second competition. Here, we provide an overview of the datasets, the tasks, the evaluation criteria, and the methods proposed by the top-ranked teams. We also report the results achieved with the submitted algorithms... 

Single Nucleotide Polymorphisms (SNPs), a single DNA base varying from one individual to another, are believed to be the most frequent form responsible for genetic differences. Genotype is the conflated information of a pair of haplotypes on homologous chromosomes. Although haplotypes have more information for disease associating than individual SNPs and genotype, it is substantially more difficult to determine haplotypes through experiments. Hence, computational methods which can reduce the cost of determining haplotypes become attractive alternatives. MEC, as a standard model for haplotype reconstruction, is fed by fragments as input to infer the best pair of haplotypes with minimum error... 

In this paper, we propose a new approach in computing interference distribution and outage probability for uplink CDMA cellular networks. In this approach, we model time-varying interference as a discrete-time Markov chain with flexible approximation and find its steady state distribution. Our approach is based on tracing the time variation of the effective factors in interference such as spatial locations, activity status, and shadowing status for the connected users. To this end, we approximate shadowing with several discrete levels by Lloyd algorithm. Moreover, we incorporate correlation properties of the shadowing status in our model. Then, we map the effective factors onto the... 

In this article, a high-accuracy hybrid approach for short-term wind power forecasting is proposed using historical data of wind farm and Numerical Weather Prediction (NWP) data. The power forecasting is carried out in three stages: wind direction forecasting, wind speed forecasting, and wind power forecasting. In all three phases, the same hybrid method is used, and the only difference is in the input data set. The main steps of the proposed method are constituted of outlier detection, decomposition of time series using wavelet transform, effective feature selection and prediction of each time series decomposed using Multilayer Perceptron (MLP) neural network. The combination of automatic... 

Magnetorheological (MR) damper is a prominent semi-active control device to vibrate mitigation of structures. Due to the inherent non-linear nature of MR damper, an intelligent non-linear neuro-fuzzy control strategy is designed to control wave-induced vibration of an offshore steel jacket platform equipped with MR dampers. In the proposed control system, a dynamic-feedback neural network is adapted to model non-linear dynamic system, and the fuzzy logic controller is used to determine the control forces of MR dampers. By use of two feedforward neural networks required voltages and actual MR damper forces are obtained, in which the first neural network and the second one acts as the inverse... 

Missing or incomplete data in short or long intervals is a common problem in measuring air pollution. Severe issues may arise when dealing with missing data for time-series prediction schemes or mean analysis. This study aimed to develop a new regression imputation framework to impute missing values in the hourly air quality data set of Tehran and enhance the applicability of Tehran Air Pollution Forecasting System (TAPFS). The proposed framework was designed based on three types of features including measurements of other stations, WRF and CAMx physical models. In this framework, elastic net and neuro-fuzzy networks were efficiently combined in a two-layer structure. The framework was... 

Autism spectrum disorder (ASD) includes different neurodevelopmental disorders characterized by deficits in social communication, and restricted, repetitive patterns of behavior, interests or activities. Based on the importance of early diagnosis for effective therapeutic intervention, several strategies have been employed for detection of the disorder. The artificial neural network (ANN) as a type of machine learning method is a common strategy. In the current study, we extracted genomic data for 487 ASD patients and 455 healthy individuals. All individuals were genotyped in certain single-nucleotide polymorphisms within retinoic acid-related orphan receptor alpha (RORA), gamma-aminobutyric... 

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... 

Objective: A novel electrocardiogram (ECG) classification algorithm is proposed for continuous cardiac monitoring on wearable devices with limited processing capacity. Methods: The proposed solution employs a novel architecture consisting of wavelet transform and multiple long short-term memory (LSTM) recurrent neural networks (see Fig. 1). Results: Experimental evaluations show superior ECG classification performance compared to previous works. Measurements on different hardware platforms show the proposed algorithm meets timing requirements for continuous and real-time execution on wearable devices. Conclusion: In contrast to many compute-intensive deep-learning based approaches, the...