Showing papers by "Amirkabir University of Technology published in 2019"

Deep Learning-Based Channel Estimation

Abstract: In this letter, we present a deep learning algorithm for channel estimation in communication systems. We consider the time–frequency response of a fast fading communication channel as a 2D image. The aim is to find the unknown values of the channel response using some known values at the pilot locations. To this end, a general pipeline using deep image processing techniques, image super-resolution (SR), and image restoration (IR) is proposed. This scheme considers the pilot values, altogether, as a low-resolution image and uses an SR network cascaded with a denoising IR network to estimate the channel. Moreover, the implementation of the proposed pipeline is presented. The estimation error shows that the presented algorithm is comparable to the minimum mean square error (MMSE) with full knowledge of the channel statistics, and it is better than an approximation to linear MMSE. The results confirm that this pipeline can be used efficiently in channel estimation.

A comprehensive survey: Whale Optimization Algorithm and its applications

Abstract: Whale Optimization Algorithm (WOA) is an optimization algorithm developed by Mirjalili and Lewis in 2016. An overview of WOA is described in this paper, rooted from the bubble-net hunting strategy, besides an overview of WOA applications that are used to solve optimization problems in various categories. The best solution has been determined to make something as functional and effective as possible through the optimization process by minimizing or maximizing the parameters involved in the problems. Research and engineering attention have been paid to Meta-heuristics for purposes of decision-making given the growing complexity of models and the needs for quick decision making in the engineering. An updated review of research of WOA is provided in this paper for hybridization, improved, and variants. The categories included in the reviews are Engineering, Clustering, Classification, Robot Path, Image Processing, Networks, Task Scheduling, and other engineering applications. According to the reviewed literature, WOA is mostly used in the engineering area to solve optimization problems. Providing an overview and summarizing the review of WOA applications are the aims of this paper.

Mapping 123 million neonatal, infant and child deaths between 2000 and 2017

Abstract: Since 2000, many countries have achieved considerable success in improving child survival, but localized progress remains unclear. To inform efforts towards United Nations Sustainable Development Goal 3.2—to end preventable child deaths by 2030—we need consistently estimated data at the subnational level regarding child mortality rates and trends. Here we quantified, for the period 2000–2017, the subnational variation in mortality rates and number of deaths of neonates, infants and children under 5 years of age within 99 low- and middle-income countries using a geostatistical survival model. We estimated that 32% of children under 5 in these countries lived in districts that had attained rates of 25 or fewer child deaths per 1,000 live births by 2017, and that 58% of child deaths between 2000 and 2017 in these countries could have been averted in the absence of geographical inequality. This study enables the identification of high-mortality clusters, patterns of progress and geographical inequalities to inform appropriate investments and implementations that will help to improve the health of all populations.

Applying various hybrid intelligent systems to evaluate and predict slope stability under static and dynamic conditions

Abstract: The evaluation and precise prediction of safety factor (SF) of slopes can be useful in designing/analyzing these important structures. In this study, an attempt has been made to evaluate/predict SF of many homogenous slopes in static and dynamic conditions through applying various hybrid intelligent systems namely imperialist competitive algorithm (ICA)-artificial neural network (ANN), genetic algorithm (GA)-ANN, particle swarm optimization (PSO)-ANN and artificial bee colony (ABC)-ANN. In fact, ICA, PSO, GA and ABC were used to adjust weights and biases of ANN model. In order to achieve the aim of this study, a database composed of 699 datasets with 5 model inputs including slope gradient, slope height, friction angle of soil, soil cohesion and peak ground acceleration and one output (SF) was established. Several parametric investigations were conducted in order to determine the most effective factors of GA, ICA, ABC and PSO algorithms. The obtained results of hybrid models were check considering two performance indices, i.e., root-mean-square error and coefficient of determination $$(R^{2})$$ . To evaluate capability of all hybrid models, a new system of ranking, i.e., the color intensity rating, was developed. As a result, although all predictive models are able to approximate slope SF values, PSO-ANN predictive model can perform better compared to others. Based on $$R^{2}$$ , values of (0.969, 0.957, 0.980 and 0.920) were found for testing of ICA-ANN, ABC-ANN, PSO-ANN and GA-ANN predictive models, respectively, which show higher efficiency of the PSO-ANN model in predicting slope SF values.

Application of several optimization techniques for estimating TBM advance rate in granitic rocks

Abstract: This study aims to develop several optimization techniques for predicting advance rate of tunnel boring machine (TBM) in different weathered zones of granite. For this purpose, extensive field and laboratory studies have been conducted along the 12,649 m of the Pahang – Selangor raw water transfer tunnel in Malaysia. Rock properties consisting of uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), rock mass rating (RMR), rock quality designation (RQD), quartz content (q) and weathered zone as well as machine specifications including thrust force and revolution per minute (RPM) were measured to establish comprehensive datasets for optimization. Accordingly, to estimate the advance rate of TBM, two new hybrid optimization techniques, i.e. an artificial neural network (ANN) combined with both imperialist competitive algorithm (ICA) and particle swarm optimization (PSO), were developed for mechanical tunneling in granitic rocks. Further, the new hybrid optimization techniques were compared and the best one was chosen among them to be used for practice. To evaluate the accuracy of the proposed models for both testing and training datasets, various statistical indices including coefficient of determination (R2), root mean square error (RMSE) and variance account for (VAF) were utilized herein. The values of R2, RMSE, and VAF ranged in 0.939–0.961, 0.022–0.036, and 93.899–96.145, respectively, with the PSO-ANN hybrid technique demonstrating the best performance. It is concluded that both the optimization techniques, i.e. PSO-ANN and ICA-ANN, could be utilized for predicting the advance rate of TBMs; however, the PSO-ANN technique is superior.

Three hybrid intelligent models in estimating flyrock distance resulting from blasting

Abstract: Flyrock is an adverse effect produced by blasting in open-pit mines and tunnelling projects. So, it seems that the precise estimation of flyrock is essential in minimizing environmental effects induced by blasting. In this study, an attempt has been made to evaluate/predict flyrock induced by blasting through applying three hybrid intelligent systems, namely imperialist competitive algorithm (ICA)–artificial neural network (ANN), genetic algorithm (GA)–ANN and particle swarm optimization (PSO)–ANN. In fact, ICA, PSO and GA were used to adjust weights and biases of ANN model. To achieve the aim of this study, a database composed of 262 datasets with six model inputs including burden to spacing ratio, blast-hole diameter, powder factor, stemming length, the maximum charge per delay, and blast-hole depth and one output (flyrock distance) was established. Several parametric investigations were conducted to determine the most effective factors of GA, ICA and PSO algorithms. Then, at the end of modelling process of each hybrid model, eight models were constructed and their results were checked considering two performance indices, i.e., root mean square error (RMSE) and coefficient of determination (R2). The obtained results showed that although all predictive models are able to approximate flyrock, PSO–ANN predictive model can perform better compared to others. Based on R2, values of (0.943, 0.958 and 0.930) and (0.958, 0.959 and 0.932) were found for training and testing of ICA–ANN, PSO–ANN and GA–ANN predictive models, respectively. In addition, RMSE values of (0.052, 0.045 and 0.057) and (0.045, 0.044 and 0.058) were achieved for training and testing of ICA–ANN, PSO–ANN and GA–ANN predictive models, respectively. These results show higher efficiency of the PSO–ANN model in predicting flyrock distance resulting from blasting. Moreover, sensitivity analysis shows that hole diameter is more effective than others.

Distributed Noise-Resilient Secondary Voltage and Frequency Control for Islanded Microgrids

Abstract: This paper proposes a novel distributed noise-resilient secondary control for voltage and frequency restoration of islanded microgrid inverter-based distributed generations (DGs) with an additive type of noise. The existing distributed methods commonly are designed as secondary control system systems that operate on the assumption of ideal communication networks among DGs. However, the channels are prone to stochastic noise, whereas each DG obtains noisy measurements of the states of its neighbors via environmental noises. The existing distributed noise-resilient methods, ignore a complete model of the system. In contrast, this paper proposes consensus protocols that take into account both the noisy measurements and a complete nonlinear model of the system, examines the mean-square average consensus for voltage and frequency restoration of islanded ac microgrids in an uncertain environment, and provides accurate proportional real power sharing. Our proposed consensus protocol contains two parts: the state feedback of the agent and the relative states of the DG and its neighboring DGs. Finally, simulation studies are carried out in MATLAB/SimPowerSystems to evaluate the performance of the control laws. Simulation results and comparison with previous work reveals the effectiveness of the proposed method in regulating microgrid voltage and frequency and providing accurate proportional real power sharing.

Application of Heterogeneous Nano-Semiconductors for Photocatalytic Advanced Oxidation of Organic Compounds: A Review

Abstract: Advanced oxidation processes (AOPs) introduce a hopeful technology for the removal of water polluted with hard degradable organic compounds. Today, one of the main effective AOP technologies to degrade these hazardous organics is via the photocatalytic process. The present review focuses on the recent progress in the fixed-bed and immobilized nanophotocatalysts and also their applications for resolving the environmental concerns. Various organics elimination using a variety of photocatalysts and innovative reactors are discussed. This critical review summarizes the recent progress in the synthesis and modification (physical and chemical) of semiconductors. Furthermore, different ways of photocatalysts immobilization are explained in details. By considering different dangerous chemicals present in wastewater and different industries, catalytic methods used by researchers to degrade these organic compounds are reviewed. In addition, the degradation pathway of some organics through the catalytic operation is described.

Multi-target tracking using CNN-based features: CNNMTT

Abstract: In this paper, we focus mainly on designing a Multi-Target Object Tracking algorithm that would produce high-quality trajectories while maintaining low computational costs. Using online association, such features enable this algorithm to be used in applications like autonomous driving and autonomous surveillance. We propose CNN-based, instead of hand-crafted, features to lead to higher accuracies. We also present a novel grouping method for 2-D online environments without prior knowledge of camera parameters and an affinity measure based on the groups maintained in previous frames. Comprehensive evaluations of our algorithm (CNNMTT) on a publicly available and widely used dataset (MOT16) reveal that the CNNMTT method achieves high quality tracking results in comparison to the state of the art while being faster and involving much less computational cost.

Predicting tunnel boring machine performance through a new model based on the group method of data handling

Abstract: The tunnel boring machine (TBM), developed within the past few decades, is designed to make the process of tunnel excavation safer and more economical. The use of TBMs in civil and mining construction projects is controlled by several factors including economic considerations and schedule deadlines. Hence, improved methods for estimating TBM performance are important for future projects. This paper presents a new model based on the group method of data handling (GMDH) for predicting the penetration rate (PR) of a TBM. In order to achieve this aim, after investigation of the most effective parameters of PR, rock quality designation, uniaxial compressive strength, rock mass rating, Brazilian tensile strength, weathering zone, thrust force per cutter and revolutions per minute were selected and measured to estimate TBM PR. A database composed of 209 datasets was prepared according to the mentioned model inputs and output. Then, based on the most influential factors of GMDH, a series of parametric investigations were carried out on the established database. In the following, five different datasets with different sets of training and testing were selected and used to construct GMDH models. Aside from that, five multiple regression (MR) models/equations were also proposed to predict TBM PR for comparison purposes. After that, a ranking system was used in order to evaluate the obtained results. As a result, performance prediction results of [i.e. coefficient of determination (R2) = 0.946 and 0.924, root mean square error (RMSE) = 0.141 and 0.169 for training and testing datasets, respectively] demonstrated a high accuracy level of GMDH model in estimating TBM PR. Although both methods are applicable for estimation of PR, GMDH is able to provide a higher degree of accuracy and can be introduced as a new model in this field.

A Novel ANFIS-Based Islanding Detection for Inverter-Interfaced Microgrids

Abstract: This paper presents a new islanding detection strategy for low-voltage inverter-interfaced microgrids based on adaptive neuro-fuzzy inference system (ANFIS) The proposed islanding detection method exploits the pattern recognition capability of ANFIS and its nonlinear mapping of relation between inputs The ANFIS monitors seven inputs measured at point of common coupling (PCC), namely root-mean square (RMS) of voltage and current (RMS U and RMS I ), total harmonic distortion (THD) of voltage and current (THD U and THD I ), frequency ( ${f}$ ), and active and reactive powers ( ${P}$ , ${Q}$ ) that are experimentally obtained based on practical measurement in a real-life microgrid The proposed method is composed of passive monitoring of the mentioned inputs It does not influence power quality (PQ) and considerably decreases non detection zones (NDZs) In order to cover as many situations as possible, minimize false tripping and still remain selective; type and number of samples are introduced Here, one of the primary goals is reducing NDZ while keeping PQ in order Based on the sampled frequency and number of samples, we find that the proposed method has less detection time and better accuracy when compared to the reported methods Simulations performed in MATLAB/Simulink software environment and several tests performed based on different active load conditions and multiple distributed generation, prove the effectiveness, authenticity, selectivity, accuracy and precision of the proposed method with allowable impact on PQ according to UL1741 standard

Vibration analysis of functionally graded graphene platelet reinforced cylindrical shells with different porosity distributions

Abstract: This paper studies free vibrational behavior of porous nanocomposite shells reinforced with graphene platelets (GPLs). GPLs are uniformly and nonuniformly distributed thorough the thickness directi...

A neuro-genetic predictive model to approximate overbreak induced by drilling and blasting operation in tunnels

Abstract: Overbreak in tunnel construction creates additional costs, and it could put the safety conditions at potential risk. This paper is aimed to predict overbreak in order to control it before drilling and blasting operations through two intelligence systems, namely, an artificial neural network (ANN) and a hybrid genetic algorithm (GA)-ANN. To achieve this aim, a database comprising of 406 datasets were prepared in the Gardaneh Rokh tunnel, Iran. In these datasets, rock mass rating (RMR), spacing, burden, special drilling, number of delays, powder factor and advance length were considered as inputs while overbreak is set as output system. Many intelligence models were created to achieve higher levels of accuracy in accordance with several performance indices, i.e., root mean square error (RMSE), variance account for (VAF) and coefficient of determination (R2). After selection of the best models, GA-ANN model results (VAF = 90.134 and 88.030, R2 = 0.903 and 0.881 and RMSE = 0.058 and 0.074 for training and testing, respectively) were better compared to ANN model results (VAF = 70.319 and 68.731, R2 = 0.703 and 0.693 and RMSE = 0.103 and 0.108 for training and testing, respectively). As a result, the GA-ANN predictive approach can be used for overbreak prediction with high performance capacity. Moreover, results of sensitivity analysis showed that overbreak is mainly influenced by the RMR parameter compared to other inputs.