Showing papers by "Iran University of Science and Technology published in 2020"
TL;DR: A novel metaheuristic algorithm inspired by the individual intelligence and sexual motivation of chimps in their group hunting, which is different from the other social predators, is proposed, which indicates that the ChOA outperforms the other benchmark optimization algorithms.
Abstract: This paper proposes a novel metaheuristic algorithm called Chimp Optimization Algorithm (ChOA) inspired by the individual intelligence and sexual motivation of chimps in their group hunting, which is different from the other social predators. ChOA is designed to further alleviate the two problems of slow convergence speed and trapping in local optima in solving high-dimensional problems. In this paper, a mathematical model of diverse intelligence and sexual motivation of chimps is proposed. In this regard, four types of chimps entitled attacker, barrier, chaser, and driver are employed for simulating the diverse intelligence. Moreover, four main steps of hunting, i.e. driving, chasing, blocking, and attacking, are implemented. The proposed ChOA algorithm is evaluated in 3 main phases. First, a set of 30 mathematical benchmark functions is utilized to investigate various characteristics of ChOA. Secondly, ChOA was tested by 13 high-dimensional test problems. Finally, 10 real-world optimization problems were used to evaluate the performance of ChOA. The results are compared to several newly proposed meta-heuristic algorithms in terms of convergence speed, the probability of getting stuck in local minimums, and exploration, exploitation. Also, statistical tests were employed to investigate the significance of the results. The results indicate that the ChOA outperforms the other benchmark optimization algorithms.
501 citations
TL;DR: Several deep convolutional networks with the introduced training techniques for classifying X-ray images into three classes: normal, pneumonia, and COVID-19 are trained, and a neural network that is a concatenation of Xception and ResNet50V2 networks is proposed that achieved the best accuracy.
290 citations
TL;DR: An overview of the historic evolution of porosity and activation of MOFs is provided, followed by a synopsis of the strategies to design and preserve permanent porosity in MOFs.
Abstract: Since the first reports of metal–organic frameworks (MOFs), this unique class of crystalline, porous materials has garnered increasing attention in a wide variety of applications such as gas storage and separation, catalysis, enzyme immobilization, drug delivery, water capture, and sensing. A fundamental feature of MOFs is their porosity which provides space on the micro- and meso-scale for confining and exposing their functionalities. Therefore, designing MOFs with high porosity and developing suitable activation methods for preserving and accessing their pore space have been a common theme in MOF research. Reticular chemistry allows for the facile design of MOFs from highly tunable metal nodes and organic linkers in order to realize different pore structures, topologies, and functionalities. With the hope of shedding light on future research endeavors in MOF porosity, it is worthwhile to examine the development of MOFs, with an emphasis on their porosity and how to properly access their pore space. In this review, we will provide an overview of the historic evolution of porosity and activation of MOFs, followed by a synopsis of the strategies to design and preserve permanent porosity in MOFs.
267 citations
TL;DR: The procedure and application of vibration-based, vision-based monitoring, along with some of the recent technologies used for SHM, such as sensors, unmanned aerial vehicles (UAVs), etc. are discussed.
Abstract: Data-driven methods in structural health monitoring (SHM) is gaining popularity due to recent technological advancements in sensors, as well as high-speed internet and cloud-based computation. Since the introduction of deep learning (DL) in civil engineering, particularly in SHM, this emerging and promising tool has attracted significant attention among researchers. The main goal of this paper is to review the latest publications in SHM using emerging DL-based methods and provide readers with an overall understanding of various SHM applications. After a brief introduction, an overview of various DL methods (e.g., deep neural networks, transfer learning, etc.) is presented. The procedure and application of vibration-based, vision-based monitoring, along with some of the recent technologies used for SHM, such as sensors, unmanned aerial vehicles (UAVs), etc. are discussed. The review concludes with prospects and potential limitations of DL-based methods in SHM applications.
232 citations
TL;DR: In this article, the thermal performance of the passive thermal management system (TMS) of the 18,650 lithium-ion battery with application of phase change materials (PCM) was analyzed.
Abstract: This study aims to analyze the thermal performance of the passive thermal management system (TMS) of the 18,650 lithium-ion battery with application of phase change materials (PCM). To improve performance of TMS, nanoparticles, fins and porous metal foam are used beside the PCM, and their effects on the system performance are compared. The local thermal non-equilibrium (LTNE) model and non-Darcy law are considered to simulate the nano-PCM melting inside the porous media. Numerical results are validated through previously published experimental data and results are presented for two, 4.6 W and 9.2 W, heat generation rates. Sole effects of adding nanoparticles to the PCM, utilizing different numbers of fins, and application of the metal foam on the system performance are scrutinized. Results indicated that the porous-PCM composition performs more efficiently than the nano-PCM and the fin-PCM ones. In addition, ΔTavg, battery parameter is introduced and its variations are analyzed to judge about the effect of each technique to reduce the battery mean temperature. Using the porous-PCM led to 4–6 K reduction in the battery mean temperature with respect to the pure PCM. Moreover, for the porous-PCM composition a delay is observed in the PCM melting initiation time that can adversely affect the performance of battery TMS.
217 citations
TL;DR: In this article, the authors trained several deep convolutional networks with introduced training techniques for classifying X-ray images into three classes: normal, pneumonia, and COVID-19, based on two open-source datasets.
Abstract: In this paper, we have trained several deep convolutional networks with introduced training techniques for classifying X-ray images into three classes: normal, pneumonia, and COVID-19, based on two open-source datasets. Our data contains 180 X-ray images that belong to persons infected with COVID-19, and we attempted to apply methods to achieve the best possible results. In this research, we introduce some training techniques that help the network learn better when we have an unbalanced dataset (fewer cases of COVID-19 along with more cases from other classes). We also propose a neural network that is a concatenation of the Xception and ResNet50V2 networks. This network achieved the best accuracy by utilizing multiple features extracted by two robust networks. For evaluating our network, we have tested it on 11302 images to report the actual accuracy achievable in real circumstances. The average accuracy of the proposed network for detecting COVID-19 cases is 99.50%, and the overall average accuracy for all classes is 91.4%.
202 citations
TL;DR: In this article, a dual-loop organic Rankine cycle (ORC) engine is used for waste-heat recovery from a solid oxide fuel cell system equipped with a gas turbine (SOFC-GT).
181 citations
TL;DR: A hybrid approach of fuzzy analysis network process, fuzzy decision-making trial and evaluation laboratory, and multi-objective mixed-integer linear programming models are developed for circular supplier selection and order allocation in a multi-product circular closed-loop supply chain (C-CLSC).
168 citations
TL;DR: In this article, the effects of cross fluid, microorganisms, and magnetic field on velocity, temperature, and concentration profiles of cross-fluid flow containing gyrotactic microorganisms and nanoparticles on a horizontal and three-dimensional cylinder were investigated.
Abstract: Due to the variation in fluid flow behavior in various physical conditions, the presented study have been performed an investigation of cross-fluid flow containing gyrotactic microorganisms and nanoparticles on a horizontal and three-dimensional cylinder considering viscous dissipation and magnetic field. The governing equations of the problem have been solved by the Runge-Kutta fifth-order method. The aim of this study is to inspect the effects of cross fluid, microorganisms, and magnetic field, on velocity, temperature, and concentration profiles. Also, Heat flux and mass flux values for nanoparticles and microorganisms have been calculated in tabular form. In this research, the simultaneous utilization of nanoparticles with motile microorganisms in cross fluid, and three-dimensional assessment on the cylinder has been proposed as an innovation. The results show that, when the Brownian motion parameter varies from 0.1 to 0.4 and at η = 4 , the concentration of nanoparticle deduces about 80.43%. Furthermore, with the change of bioconvection Lewis number from 0.2 to 0.5, it was observed that the concentration of the microorganisms reduced about 78.38%.
164 citations
TL;DR: In order to improve the adsorption of activated carbon, which highly depends on its pore size and surface area, the authors in this paper prepared highly porous adsorbent composites of activation carbon (AC)/chromium-based MOF (MIL-101(Cr)).
159 citations
TL;DR: A novel yet efficient deep learning method for analyzing the driver behavior by learning a 2D Convolutional Neural Network on images constructed from driving signals based on recurrence plot technique.
Abstract: Driver behavior monitoring system as Intelligent Transportation Systems (ITS) have been widely exploited to reduce the traffic accidents risk. Most previous methods for monitoring the driver behavior are rely on computer vision techniques. Such methods suffer from violation of privacy and the possibility of spoofing. This paper presents a novel yet efficient deep learning method for analyzing the driver behavior. We have used the driving signals, including acceleration, gravity, throttle, speed, and Revolutions Per Minute (RPM) to recognize five types of driving styles, including normal, aggressive, distracted, drowsy, and drunk driving. To take the advantages of successful deep neural networks on images, we learn a 2D Convolutional Neural Network (CNN) on images constructed from driving signals based on recurrence plot technique. Experimental results confirm that the proposed method can efficiently detect the driver behavior.
05 Sep 2020
TL;DR: This paper aims to propose a high- speed and accurate fully-automated method to detect COVID-19 from the patient9s CT scan, and proposes a new modified deep convolutional network that is based on ResNet50V2 and enhanced by the feature pyramid network for classifying the selected CT images into CO VID-19 or normal.
Abstract: This paper aims to propose a high-speed and accurate fully-automated method to detect COVID-19 from the patient's chest CT scan images. We introduce a new dataset that contains 48,260 CT scan images from 282 normal persons and 15,589 images from 95 patients with COVID-19 infections. At the first stage, this system runs our proposed image processing algorithm that analyzes the view of the lung to discard those CT images that inside the lung is not properly visible in them. This action helps to reduce the processing time and false detections. At the next stage, we introduce a novel architecture for improving the classification accuracy of convolutional networks on images containing small important objects. Our architecture applies a new feature pyramid network designed for classification problems to the ResNet50V2 model so the model becomes able to investigate different resolutions of the image and do not lose the data of small objects. As the infections of COVID-19 exist in various scales, especially many of them are tiny, using our method helps to increase the classification performance remarkably. After running these two phases, the system determines the condition of the patient using a selected threshold. We are the first to evaluate our system in two different ways on Xception, ResNet50V2, and our model. In the single image classification stage, our model achieved 98.49% accuracy on more than 7996 test images. At the patient condition identification phase, the system correctly identified almost 234 of 245 patients with high speed. Our dataset is accessible at https://github.com/mr7495/COVID-CTset .
TL;DR: In this paper, an annular porous structure is installed inside the absorber tube to improve heat transfer and the effects of simultaneous utilization of porous structure and nanoparticle addition on heat transfer, pressure drop, and thermal efficiency of the receiver are investigated for different values of Reynolds number, volume fraction of nanoparticles, inlet temperature and Darcy number of the porous region.
TL;DR: The Independence Performance Index (IPI) is introduced for the MGs to reduce energy exchange with the main grid and improve system losses, voltage drop, and greenhouse gas emissions.
TL;DR: This review attempts to cover the recent progresses in the fabrication, characterization and broad applications of biogenic Pd NPs in environmental and catalytic systems.
TL;DR: In this article, three different configurations of a hybrid battery thermal management system (BTMS) using phase change material (PCM) as passive and air coolant as active cooling system were investigated.
TL;DR: The ability to deliver small biomolecules, such as dexamethasone, different growth factors, vitamins and mineral ions depends on the morphology, porosity, and crystallinity of SNMs and their composites with other polymeric materials.
TL;DR: In this article, the authors systematically reviewed 70 journal articles, published in the field of building energy performance forecasting between 2015 and 2018, focusing on an urban-scale, and categorized them according to five criteria: 1. Learning Method, 2. Building Type, 3. Energy Type, 4. Input Data, and 5. Time-scale.
Abstract: In developed countries, buildings are involved in almost 50% of total energy use and 30% of global green-house gas emissions. Buildings' operational energy is highly dependent on various building physical, operational, and functional characteristics, as well as meteorological and temporal properties. Besides physics-based building energy modeling, machine learning techniques can provide faster and higher accuracy estimates, given buildings' historic energy consumption data. Looking beyond individual building levels, forecasting buildings’ energy performance helps city and community managers have a better understanding of their future energy needs, and plan for satisfying them more efficiently. Focusing on an urban-scale, this study systematically reviews 70 journal articles, published in the field of building energy performance forecasting between 2015 and 2018. The recent literature have been categorized according to five criteria: 1. Learning Method, 2. Building Type, 3. Energy Type, 4. Input Data, and 5. Time-scale. The scarcity of building energy performance forecasting studies in urban-scale versus individual level is considerable. There is no study incorporating building functionality in terms of space functionality share percentages, nor assessing the effects of climate change on urban buildings energy performance using machine learning approaches and future weather scenarios. There is no optimal criteria combination for achieving the most accurate machine learning-based forecast, as there is no universal measure able to provide such global comparison. Accuracy levels are highly correlated with the characteristics of forecasting problems. The goal is to provide a comprehensive status of machine learning applications in urban building energy performance forecasting, during 2015–2018.
TL;DR: A multi-objective multi-period sustainable location-allocation supply chain network model that addresses the challenge of different levels of technology for vehicle fleet and its implications for sustainability.
Abstract: In this paper, a multi-objective multi-period sustainable location-allocation supply chain network model will be presented. Different levels of technology for vehicle fleet, which leads to differen...
TL;DR: The Water Strider Algorithm is a population-based optimizer inspired by the life cycle of water strider bugs that is applied to classical constrained, unconstrained, continuous and discrete structural design problems confirming its capability of tackling various challenging problems.
TL;DR: This paper presents a mixed-integer linear program to restore prioritized loads while satisfying topology and operational constraints and determines the required emergency budgets of operation to restore a distribution system from extreme events.
TL;DR: In this paper, an in-depth review of energy and exergy efficiencies of organic Rankine cycle power plants is conducted, where key factors that influence the energy and energy efficiencies are discussed in detail.
TL;DR: In this article, the authors investigated a hybrid system for combined cooling and power driven by geothermal energy using energy, exergy, and economic analyses, and the results showed that application of LiBr absorption chiller downstream of the organic Rankine cycle (ORC) cycle increases the energy efficiency of the system from 9.3% to 47.3%.
TL;DR: In this article, a palladium-supported o-phenylenediamine-functionalized Fe3O4 magnetic nanoparticles are presented for the Suzuki-Miyaura coupling of various aryl halides with phenylboronic acids.
TL;DR: In this article, the authors provided extensive information about renewables, desalination and performance analysis of power systems, and obtained results from energy and exergy analysis would have provided a better insight.
Abstract: Water and energy are two key factors in human life that always control the growth and development of human societies. Climate changes, increasing the population in urban areas and industrialization, have increased the demands for freshwater around the world. Estimates show that a small percentage of all freshwater produced in the world is from renewable sources. By developing the technology, lowering equipment prices and increasing attention to the environmental problems of fossil fuels, utilizing renewable energy is growing. By providing a wide variety of conventional desalination methods driven by various types of renewable energy technologies in the world, water and energy legislators should choose different methods to meet the needs based on the local potentials by paying attention to the desalination processes and power systems. In some cases, concentrated solar power for thermal desalination or electricity generated by the photovoltaic plants for membrane desalination systems can be used in arid areas. Definitely, the most problem of using renewable sources is their unsteady natures, which using storage systems or combining with other renewable sources can solve this problem. This chapter provides extensive information about renewables, desalination and performance analysis of power systems. Reverse osmosis technique is a practical process in desalination which 69% of desalination plants use this system. Solar energy is an important source of energy for hybrid systems. The geothermal has a steady performance at a specified depth. Ultimately, obtained results from energy and exergy analysis would have provided a better insight.
Sharif University of Technology1, Iran University of Science and Technology2, Shahid Beheshti University of Medical Sciences and Health Services3, Education and Research Network4, Tehran University of Medical Sciences5, Amirkabir University of Technology6, National University of Singapore7, Palacký University, Olomouc8, Seoul National University9
TL;DR: This manuscript critically reviews recent literature to specifically illustrate nano-engineered effective and rapid solutions for rapid detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).
Abstract: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused the COVID-19 pandemic that has been spreading around the world since December 2019. More than 10 million affected cases and more than half a million deaths have been reported so far, while no vaccine is yet available as a treatment. Considering the global healthcare urgency, several techniques, including whole genome sequencing and computed tomography imaging have been employed for diagnosing infected people. Considerable efforts are also directed at detecting and preventing different modes of community transmission. Among them is the rapid detection of virus presence on different surfaces with which people may come in contact. Detection based on non-contact optical techniques is very helpful in managing the spread of the virus, and to aid in the disinfection of surfaces. Nanomaterial-based methods are proven suitable for rapid detection. Given the immense need for science led innovative solutions, this manuscript critically reviews recent literature to specifically illustrate nano-engineered effective and rapid solutions. In addition, all the different techniques are critically analyzed, compared, and contrasted to identify the most promising methods. Moreover, promising research ideas for high accuracy of detection in trace concentrations, via color change and light-sensitive nanostructures, to assist fingerprint techniques (to identify the virus at the contact surface of the gas and solid phase) are also presented.
TL;DR: In this paper, flexible pressure sensors based on PVDF-PZT nanocomposite with different PZT volume fractions were prepared in the form of fibers through an electrospinning method for piezoelectric energy harvesting application.
TL;DR: This study provides new insights into the synthesis and application of hybrid magnetic adsorbents with synergistic properties of nanoporous MOF particles and dendrimer with large number of functional groups for removal of organic dyes.
Abstract: Herein, a magnetic zirconium-based metal-organic framework nanocomposite was synthesized by a simple solvothermal method and used as an adsorbent for the removal of direct and acid dyes from aqueous solution. To enhance its adsorption performance, poly(propyleneimine) dendrimer was used to functionalize the as-synthesized magnetic porous nanocomposite. The dendrimer-functionalized magnetic nanocomposite was characterized by field-emission scanning electron microscopy, X-ray diffraction, Fourier transform infrared spectroscopy, nitrogen adsorption/desorption isotherms, and vibration sample magnetometer. The obtained results revealed the successful synthesis and functionalization of the magnetic nanocomposite. The adsorbents exhibited good magnetic properties with high saturation magnetization and high specific surface area. The adsorption isotherms and kinetics of anionic dyes were described by the Freundlich and pseudo-second-order models, respectively. It was found that the kinetics of adsorption of both the investigated dyes by the dendrimer-functionalized magnetic composite is considerably faster than the magnetic composite under the same condition. The adsorption capacity of the dendrimer-functionalized magnetic composite for investigated direct and acid dyes was 173.7 and 122.5 mg/g, respectively, which was higher than those of the existing magnetic adsorbents. This work provides new insights into the synthesis and application of hybrid magnetic adsorbents with synergistic properties of nanoporous metal-organic frameworks and dendrimer with a large number of functional groups for the removal of organic dyes.
TL;DR: In this article, the authors investigated the impact of RAP material on the cracking behavior of asphalt mixtures using semi-circular bending (SCB) fracture tests at temperatures of −15, 0 and 15°C.
TL;DR: A novel resource provisioning mechanism and a workflow scheduling algorithm, named Greedy Resource Provisioning and modified HEFT (GRP-HEFT), for minimizing the makespan of a given workflow subject to a budget constraint for the hourly-based cost model of modern IaaS clouds.
Abstract: In Infrastructure as a Service (IaaS) Clouds, users are charged to utilize cloud services according to a pay-per-use model. If users intend to run their workflow applications on cloud resources within a specific budget, they have to adjust their demands for cloud resources with respect to this budget. Although several scheduling approaches have introduced solutions to optimize the makespan of workflows on a set of heterogeneous IaaS cloud resources within a certain budget, the hourly-based cost model of some well-known cloud providers (e.g., Amazon EC2 Cloud) can easily lead to a higher makespan and some schedulers may not find any feasible solution. In this article, we propose a novel resource provisioning mechanism and a workflow scheduling algorithm, named Greedy Resource Provisioning and modified HEFT (GRP-HEFT), for minimizing the makespan of a given workflow subject to a budget constraint for the hourly-based cost model of modern IaaS clouds. As a resource provisioning mechanism, we propose a greedy algorithm which lists the instance types according to their efficiency rate. For our scheduler, we modified the HEFT algorithm to consider a budget limit. GRP-HEFT is compared against state-of-the-art workflow scheduling techniques, including MOACS (Multi-Objective Ant Colony System), PSO (Particle Swarm Optimization), and GA (Genetic Algorithm). The experimental results demonstrate that GRP-HEFT outperforms GA, PSO, and MOACS for several well-known scientific workflow applications for different problem sizes on average by 13.64, 19.77, and 11.69 percent, respectively. Also in terms of time complexity, GRP-HEFT outperforms GA, PSO and MOACS.