Showing papers by "Iran University of Science and Technology published in 2019"

Design principles of ion selective nanostructured membranes for the extraction of lithium ions

Abstract: It is predicted that the continuously increasing demand for the energy-critical element of lithium will soon exceed its availability, rendering it a geopolitically significant resource. The present work critically reviews recent reports on Li+ selective membranes. Particular emphasis has been placed on the basic principles of the materials' design for the development of membranes with nanochannels and nanopores with Li+ selectivity. Fundamental and practical challenges, as well as prospects for the targeted design of Li+ ion-selective membranes are also presented, with the goal of inspiring future critical research efforts in this scientifically and strategically important field.

Multi-objective multi-robot path planning in continuous environment using an enhanced genetic algorithm

Abstract: This paper presents a hybrid approach for path planning of multiple mobile robots in continuous environments. For this purpose, first, an innovative Artificial Potential Field (APF) algorithm is presented to find all feasible paths between the start and destination locations in a discrete gridded environment. Next, an enhanced Genetic Algorithm (EGA) is developed to improve the initial paths in continuous space and find the optimal path between start and destination locations. The proposed APF works based on a time-efficient deterministic scheme to find a set of feasible initial paths and is guaranteed to find a feasible path if one exists. The EGA utilizes five customized crossover and mutation operators to improve the initial paths. In this paper, path length, smoothness, and safety are combined to form a multi-objective path planning problem. In addition, the proposed method is extended to deal with multiple mobile robot path planning problem. For this purpose, a new term is added to the objective function which measures the distance between robots and a collision removal operator is added to the EGA to remove possible collision between paths. To assess the efficiency of the proposed algorithm, 12 planar environments with different sizes and complexities were examined. Evaluations showed that the control parameters of the proposed algorithm do not affect the performance of the EGA considerably. Moreover, a comparative study has been made between the proposed algorithm, A*, PRM, B-RRT and Particle Swarm Optimization (PSO). The comparative study showed that the proposed algorithm outperforms PSO as well as well-recognized deterministic (A*) and probabilistic (PRM and B-RRT) path planning algorithms in terms of path length, run time, and success rate. Finally, simulations proved the efficiency of the proposed algorithm for a four-robot path planning problem. In this case, not only the proposed algorithm determined collision-free paths, but also it found near optimal solution for all robots.

Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions

Abstract: In recent years, deep learning-based networks have achieved state-of-the-art performance in medical image segmentation. Among the existing networks, U-Net has been successfully applied on medical image segmentation. In this paper, we propose an extension of U-Net, Bi-directional ConvLSTM U-Net with Densely connected convolutions (BCDU-Net), for medical image segmentation, in which we take full advantages of U-Net, bi-directional ConvLSTM (BConvLSTM) and the mechanism of dense convolutions. Instead of a simple concatenation in the skip connection of U-Net, we employ BConvLSTM to combine the feature maps extracted from the corresponding encoding path and the previous decoding up-convolutional layer in a non-linear way. To strengthen feature propagation and encourage feature reuse, we use densely connected convolutions in the last convolutional layer of the encoding path. Finally, we can accelerate the convergence speed of the proposed network by employing batch normalization (BN). The proposed model is evaluated on three datasets of: retinal blood vessel segmentation, skin lesion segmentation, and lung nodule segmentation, achieving state-of-the-art performance.

Extended Genetic Algorithm for solving open-shop scheduling problem

Abstract: Open-shop scheduling problem (OSSP) is a well-known topic with vast industrial applications which belongs to one of the most important issues in the field of engineering. OSSP is a kind of NP problems and has a wider solution space than other basic scheduling problems, i.e., Job-shop and flow-shop scheduling. Due to this fact, this problem has attracted many researchers over the past decades and numerous algorithms have been proposed for that. This paper investigates the effects of crossover and mutation operator selection in Genetic Algorithms (GA) for solving OSSP. The proposed algorithm, which is called EGA_OS, is evaluated and compared with other existing algorithms. Computational results show that selection of genetic operation type has a great influence on the quality of solutions, and the proposed algorithm could generate better solutions compared to other developed algorithms in terms of computational times and objective values.

A CSRR-Based Sensor for Full Characterization of Magneto-Dielectric Materials

Abstract: In this paper, a novel complementary split-ring resonator (CSRR)-based sensor for full characterization of magneto-dielectric materials is proposed. In general, the operation of microwave resonance-based sensor hinges on the shift in the resonance frequency and the change in the quality factor of the loaded structure. However, both the electric permittivity and the magnetic permeability of the material under test (MUT) have similar effect on the response of the sensor that makes the simultaneous determination of the permittivity and permeability challenging. To remove this difficulty, the main idea behind this paper is to localize the highest intensity of the electric and magnetic fields in two separate zones. By the analysis of the measured resonance frequency and quality factor, the real and imaginary parts of the electric permittivity and the magnetic permeability of the MUT can be determined. Although the characterization of the permittivity and permeability of materials using split-ring resonator and CSRR-based sensors has been widely used, to the best of our knowledge, the full characterization of magneto-dielectric materials using a single sensor has not yet been reported in this paper. As a proof of concept, the sensor was fabricated and used to measure the permittivity and permeability of several materials. Strong agreement between the extracted values and the reference data was achieved.

Numerical analysis of mixed convection of two-phase non-Newtonian nanofluid flow inside a partially porous square enclosure with a rotating cylinder

Abstract: In this study, mixed convection heat transfer of the non-Newtonian power-law nanofluid including CuO nanoparticles, inside a partially porous square enclosure with a concentric rotating cylinder and a hot side wall is numerically investigated. Two-phase mixture model is utilized for nanofluid flow simulation and the mixture viscosity and thermal conductivity are computed by Corcione’s correlation. The effect of different angular velocity (− 4000 ≤ Ω ≤ 4000) for various Rayleigh (104 ≤ Ra ≤ 106), Darcy (10−4 ≤ Da ≤ 10−1), power-law index (0.8 ≤ n ≥ 1.2) and effective to base fluid thermal conductivity ratio (keff/kf= 16, 4) are studied on heat transfer. Results are presented and compared in terms of the average Nusselt number, and streamline and isotherm contours. Outcomes show that for different kinds of fluid, depending on the value of Ra, Da, keff/kf and the amount and direction of angular velocity, heat transfer can be improved by augmenting heat convection and also can be deteriorated by increasing viscosity. Consequently, optimal values of Ra, Da, keff/kf and Ω exist in order to maximize the average Nu number.

Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions

Abstract: In recent years, deep learning-based networks have achieved state-of-the-art performance in medical image segmentation. Among the existing networks, U-Net has been successfully applied on medical image segmentation. In this paper, we propose an extension of U-Net, Bi-directional ConvLSTM U-Net with Densely connected convolutions (BCDU-Net), for medical image segmentation, in which we take full advantages of U-Net, bi-directional ConvLSTM (BConvLSTM) and the mechanism of dense convolutions. Instead of a simple concatenation in the skip connection of U-Net, we employ BConvLSTM to combine the feature maps extracted from the corresponding encoding path and the previous decoding up-convolutional layer in a non-linear way. To strengthen feature propagation and encourage feature reuse, we use densely connected convolutions in the last convolutional layer of the encoding path. Finally, we can accelerate the convergence speed of the proposed network by employing batch normalization (BN). The proposed model is evaluated on three datasets of: retinal blood vessel segmentation, skin lesion segmentation, and lung nodule segmentation, achieving state-of-the-art performance.

Digital Metasurface Based on Graphene: An Application to Beam Steering in Terahertz Plasmonic Antennas

Abstract: Metasurfaces, the two-dimensional counterpart of metamaterials, have caught great attention thanks to their powerful capabilities on manipulation of electromagnetic waves. Recent times have seen the emergence of a variety of metasurfaces exhibiting not only countless functionalities, but also a reconfigurable response. Additionally, digital or coding metasurfaces have revolutionized the field by describing the device as a matrix of discrete building block states, thus drawing clear parallelisms with information theory and opening new ways to model, compose, and (re)program advanced metasurfaces. This paper joins the reconfigurable and digital approaches, and presents a metasurface that leverages the tunability of graphene to perform beam steering at terahertz frequencies. A comprehensive design methodology is presented encompassing technological, unit cell design, digital metamaterial synthesis, and programmability aspects. By setting up and dynamically adjusting a phase gradient along the metasurface plane, the resulting device achieves beam steering at all practical directions. The proposed design is studied through analytical models and validated numerically, showing beam widths and steering errors well below 10° and 5% in most cases. Finally, design guidelines are extracted through a scalability analysis involving the metasurface size and number of unit cell states.

Enhanced adsorption of dyes on microwave-assisted synthesized magnetic zeolite-hydroxyapatite nanocomposite

Abstract: In the present study, magnetic zeolite/hydroxyapatite (MZeo-HAP) nanocomposite was successfully synthesized using microwave-assisted method for the removal of Reactive Orange 5 (RO5), Reactive orange 16 (RO16), and Congo red (CR) anionic dyes from aqueous solutions through batch adsorption. Thorough characterization of the synthesized adsorbent was done by SEM, EDAX, XRD, TEM, FTIR, VSM, and BET. The MZeo-HAP was found to have a high surface area of 103.56 m2/g, which provided a large number of active sites for dye molecules. The effect of initial pH, contact time, adsorbent dosage, and initial dye concentration was investigated. The maximum dye removal was attained at pH 2 and the equilibrium contact time of 30 min. At dye concentration of 80 ppm, the highest adsorption capacity of RO5, RO16, and CR was 92.45, 88.31, and 104.05 mg/g, respectively. Thermodynamic parameters such as Δ G ° , Δ S ° a n d Δ H ° were estimated for the adsorption study and suggested that the adsorption was endothermic and maximum dye adsorption occurred at 40 °C. Adsorption equilibrium data obeys Freundlich model with correlation coefficient ranging from 0.9632 to 0.9903, indicating the chemisorption nature of adsorption. Similarly, pseudo second order model is the most appropriate model to describe adsorption kinetic.

Elastic and viscoelastic foundations: a review on linear and nonlinear vibration modeling and applications

Abstract: This paper presents a comprehensive review on different theoretical elastic and viscoelastic foundation models in oscillatory systems. The review covers the simplest foundation models to the most complicated one and fully tracks the recent theories on the topic of mechanical foundations. It is fully discussed why each theory has been developed, what limitations each one contains, and which approaches have been applied to remove these limitations. Moreover, corresponding theories about structures supported by such foundations are briefly reviewed. Subsequently, an introduction to popular solution methods is presented. Finally, several important practical applications related to the linear and nonlinear foundations are reviewed. This paper provides a detailed theoretical background and also physical understanding from different types of foundations with applications in structural mechanics, nanosystems, bio-devices, composite structures, and aerospace-based mechanical systems. The presented information of this review article can be used by researchers to select an appropriate kind of foundation/structure for their dynamical systems. The paper ends with a new idea of intelligent foundations based on nanogenerators, which can be exploited in future smart cities for both energy harvesting and self-powered sensing applications.

A novel method to enhance the interlayer bonding of 3D printing concrete: An experimental and computational investigation

Abstract: This study focuses on one of the bottlenecks facing the concrete 3D printing technology, the lack of proper bonding between the two adjacent layers of 3D printed concrete. Herein, a new polymer consisting of black carbon and sulfur was used to glue the two layers together. The experimental results, verified via molecular dynamics and density functional theory calculations, showed a considerable increase in the interlayer bonding strength. Two-fold rise in interlayer tensile strength as well as chemical cohesion depicted by scanning electron microscopy proves this approach to be successful in providing enhanced bonding between two adjacent printed mortar layers without hindering the printing process. The improvement arises from different types of forces in the interlayer region of modified samples, compared to that of the interlayer region of original sample. The uniform surface provided by the hardened polymer is a good substrate for the top layer in addition to extending the time gap between printing layers. This novel method can accelerate the automation of the construction industry, while reducing the costs in terms of both human labor and capital.