The Fast Evaluation Strategy for Evolvable Hardware

An evolutionary algorithm implemented in hardware is expected to operate much faster than the equivalent software implementation. However, this may not be true for slow fitness evaluation applications. This paper introduces a fast evolutionary algorithm (FEA) that does not evaluate all new individuals, thus operating faster for slow fitness evaluation applications. Results of a hardware implementation of this algorithm are presented that show the real time advantages of such systems for slow fitness evaluation applications. Results are presented for six optimisation functions and for image compression hardware.     

Multifunctional composites using reinforced laminae with carbon-nanotube forests

Traditional fibre-reinforced composite materials with excellent in-plane properties fare poorly when out-of-plane through-thickness properties are important. Composite architectures with fibres designed orthogonal to the two-dimensional (2D) layout in traditional composites could alleviate this weakness in the transverse direction, but all of the efforts so far have only produced limited success. Here, we unveil an approach to the 3D composite challenge, without altering the 2D stack design, on the basis of the concept of interlaminar carbon-nanotube forests that would provide enhanced multifunctional properties along the thickness direction. The carbon-nanotube forests allow the fastening of adjacent plies in the 3D composite. We grow multiwalled carbon nanotubes on the surface of micro-fibre fabric cloth layouts, normal to the fibre lengths, resulting in a 3D effect between plies under loading. These nanotube-coated fabric cloths serve as building blocks for the multilayered 3D composites, with the nanotube forests providing much-needed interlaminar strength and toughness under various loading conditions. For the fabricated 3D composites with nanotube forests, we demonstrate remarkable improvements in the interlaminar fracture toughness, hardness, delamination resistance, in-plane mechanical properties, damping, thermoelastic behaviour, and thermal and electrical conductivities making these structures truly multifunctional.     

Investigation of Size and Shape Dependencies of Phase Diagrams of the Ising Nanofilms and Nanotubes on the Honeycomb Lattice Using Cellular Automata Simulation Approach

Size and shape dependencies of phase diagrams of the Ising nanofilms and nanotubes on the honeycomb lattice are investigated by means of probabilistic cellular automata simulation based on Glauber algorithm. The values of reduced critical temperature, K _c = k _B T _c/J (where k _B and J are the Boltzmann constant and nearest-neighbor coupling, respectively), for both nanofilms and nanotubes, are obtained at the different sizes of the lattices and the dependency of K _c to the number of layers is studied. By increasing the number of layers K _c increases but for number of layer more than 8, the critical temperature increases very slowly. We have shown that between two isotropic nanotubes with the same number of spins, the ones with greater diameter (more spins on the edge) have larger critical temperature. For equal size of lattices, the obtained values of K _c for nanotube are greater than the nanofilm, but for large sizes, this difference disappears.     

Power efficiency enhancement analysis of an inverse class D power amplifier for NB-IoT applications

Analog Integrated Circuits and Signal Processing - The power amplifiers (PAs) are generally the most power-consuming building blocks in Radio Frequency (RF) transceivers. This paper presents a high...     

Multi-objective optimization of a hatchback rear end utilizing fractional factorial design algorithm

Air flow has significant effects on fuel consumption, performance, and comfort. Decreasing drag coefficient enhances fuel consumption and vehicle performance. Moreover, omitting or reducing the power of aerodynamic noise sources provides passengers comfort. In this paper, optimization of a hatchback rear end is conducted considering drag and aerodynamic noise objectives. To this end, five geometrical parameters of the hatchback rear end are chosen as design variables in two levels. Numerical simulation is applied to survey air flow features around the models in the wind tunnel. To reduce the number of runs, fraction factorial design algorithm is applied to generate layout of the simulations which decreased the number of case studies to half. Main and interaction effects of these factors on drag coefficient and acoustic power of the rear end source are derived using analysis of variance. Optimum level for each parameter is chosen considering simultaneous drag and noise goals. Finally, characteristics of air flow and acoustic power around optimum model are discussed.

     

Stores clustering using a data mining approach for distributing automotive spare-parts to reduce transportation costs

Clustering of retail stores in a distribution network with specific geographical limits plays an important and effective role in distribution and transportation costs reduction. In this paper, the relevant data and information for an established automotive spare-parts distribution and after-sales services company (ISACO) for a 3-year period have been analyzed. With respect to the diversity and lot size of the available information such as stores location, order, goods, transportation vehicles and road and traffic information, three effecting factors with specific weights have been defined for the similarity function: 1. Euclidean distance, 2. Lot size 3. Order concurrency. Based on these three factors, the similarity function has been examined through 5 steps using the Association Rules principles, where the clustering of the stores is performed using k-means algorithm and similar stores are allocated to the clusters. These steps include: 1. Similarity function based on the Euclidean distances, 2. Similarity function based on the order concurrency, 3. Similarity function based on the combination of the order concurrency and lot size, 4. Similarity function based on the combination of these three factors and 5. Improved similarity function. The above mentioned clustering operation for each 5 cases addressed in data mining have been carried out using R software and the improved combinational function has been chosen as the optimal clustering function. Then, trend of each retail store have been analyzed using the improved combinational function and along with determining the priority of the depot center establishment for every cluster, the appropriate distribution policies have been formulated for every cluster. The obtained results of this study indicate a significant cost reduction (32%) in automotive spare-parts distribution and transportation costs.     

Deriving operating policies for multi-objective reservoir systems: Application of Self-Learning Genetic Algorithm

Optimal multi-reservoir operation is a multi-objective problem in nature and some of its objectives are nonlinear, non-convex and multi-modal functions. There are a few areas of application of mathematical optimization models with a richer or more diverse history than in reservoir systems optimization. However, actual implementations remain limited or have not been sustained.Genetic Algorithms (GAs) are probabilistic search algorithms that are capable of solving a variety of complex multi-objective optimization problems, which may include non-linear, non-convex and multi-modal functions. GA is a population based global search method that can escape from local optima traps and find the global optima. However GAs have some drawbacks such as inaccuracy of the intensification process near the optimal set.In this paper, a new model called Self-Learning Genetic Algorithm (SLGA) is presented, which is an improved version of the SOM-Based Multi-Objective GA (SBMOGA) presented by Hakimi-Asiabar et al. (2009) [45]. The proposed model is used to derive optimal operating policies for a three-objective multi-reservoir system. SLGA is a new hybrid algorithm which uses Self-Organizing Map (SOM) and Variable Neighborhood Search (VNS) algorithms to add a memory to the GA and improve its local search accuracy. SOM is a neural network which is capable of learning and can improve the efficiency of data processing algorithms. The VNS algorithm can enhance the local search efficiency in the Evolutionary Algorithms (EAs).To evaluate the applicability and efficiency of the proposed methodology, it is used for developing optimal operating policies for the Karoon-Dez multi-reservoir system, which includes one-fifth of Iran's surface water resources. The objective functions of the problem are supplying water demands, generating hydropower energy and controlling water quality in downstream river.     

Inertial focusing of microparticles in curvilinear microchannels with different curvature angles

Inertial microfluidics has become one of the emerging topics due to potential applications such as particle separation, particle enrichment, rapid detection and diagnosis of circulating tumor cells. To realize its integration to such applications, underlying physics should be well understood. This study focuses on particle dynamics in curvilinear channels with different curvature angles (280°, 230°, and 180°) and different channel heights (90, 75, and 60 µm) where the advantages of hydrodynamic forces were exploited. We presented the cruciality of the three-dimensional particle position with respect to inertial lift forces and Dean drag force by examining the focusing behavior of 20 µm (large), 15 µm (medium) and 10 µm (small) fluorescent polystyrene microparticles for a wide range of flow rates (400–2700 µL/min) and corresponding channel Reynolds numbers. Migration of the particles in lateral direction and their equilibrium positions were investigated in detail. In addition, in the light of our findings, we described two different regions: transition region, where the inner wall becomes the outer wall and vice versa, and the outlet region. The maximum distance between the tight particle stream of 20 and 15 µm particles was obtained in the 90 high channel with curvature angle of 280° at Reynolds number of 144 in the transition region (intersection of the turns), which was the optimum condition/configuration for focusing.     

Design of microfluidic channels for magnetic separation of malaria-infected red blood cells

This study is motivated by the development of a blood cell filtration device for removal of malaria-infected, parasitized red blood cells (pRBCs). The blood was modeled as a multi-component fluid using the computational fluid dynamics discrete element method (CFD-DEM), wherein plasma was treated as a Newtonian fluid and the red blood cells (RBCs) were modeled as soft-sphere solid particles which move under the influence of drag, collisions with other RBCs, and a magnetic force. The CFD-DEM model was first validated by a comparison with experimental data from Han and Frazier (Lab Chip 6:265–273, 2006) involving a microfluidic magnetophoretic separator for paramagnetic deoxygenated blood cells. The computational model was then applied to a parametric study of a parallel-plate separator having hematocrit of 40 % with 10 % of the RBCs as pRBCs. Specifically, we investigated the hypothesis of introducing an upstream constriction to the channel to divert the magnetic cells within the near-wall layer where the magnetic force is greatest. Simulations compared the efficacy of various geometries upon the stratification efficiency of the pRBCs. For a channel with nominal height of 100 µm, the addition of an upstream constriction of 80 % improved the proportion of pRBCs retained adjacent to the magnetic wall (separation efficiency) by almost twofold, from 26 to 49 %. Further addition of a downstream diffuser reduced remixing and hence improved separation efficiency to 72 %. The constriction introduced a greater pressure drop (from 17 to 495 Pa), which should be considered when scaling up this design for a clinical-sized system. Overall, the advantages of this design include its ability to accommodate physiological hematocrit and high throughput, which is critical for clinical implementation as a blood-filtration system.     

QoS-enabled TCP for software-defined networks: a combined scheduler-per-node approach

The Journal of Supercomputing - Software-defined network (SDN) can ease the implementation of QoS concept by introducing a flexible and manageable mechanism to overcome the TCP pacing in the...