Interactive Evolutionary Multi-Objective Optimization Algorithm Using Cone Dominance

As the number of objectives increases, the performance of the Pareto dominance-based Evolutionary Multi-objective Optimization (EMO) algorithms such as NSGA-II, SPEA2 severely deteriorates due to the drastic increase in the Pareto-incomparable solutions. We propose a sorting method which classifies these incomparable solutions into several ordered classes by using the decision maker's (DM) preference information. This is accomplished by designing an interactive evolutionary algorithm and constructing convex cones. This method allows the DMs to drive the search process toward a preferred region of the Pareto optimal front. The performance of the proposed algorithm is assessed for two, three, and four-objective knapsack problems. The results demonstrate the algorithm's ability to converge to the most preferred point. The evaluation and comparison of the results indicate that the proposed approach gives better solutions than that of NSGA-II. In addition, the approach is more efficient compared to NSGA-II in terms of the number of generations required to reach the preferred point.     

Home Automation Using Augmented Reality (HAAR)

Wireless Personal Communications - The development of Smart Home Controllers has seen rapid growth in recent years, especially for smart devices, that can utilize the Internet of Things (IoT)....     

Rapid and multimodal <Emphasis Type="Italic">in vivo</Emphasis> bioimaging of cancer cells through <Emphasis Type="Italic">in situ</Emphasis> biosynthesis of Zn&;Fe nanoclusters

Early diagnosis remains highly important for efficient cancer treatment,and hence,there is significant interest in the development of effective imaging strategies.This work reports a new multimodal bioimaging method for accurate and rapid diagnosis of cancer cells by introducing aqueous Fe2+ and Zn2+ ions into cancer cells (i.e.,HeLa,U87,and HepG2 cancer cells).We found that the biocompatible metal ions Fe2+ and Zn2+ forced the cancer cells to spontaneously synthesize fluorescent ZnO nanoclusters and magnetic Fe3O4 nanoclusters.These clusters could then be used for multimodal cancer imaging by combining fluorescence imaging with magnetic resonance imaging and computed tomography imaging.Meanwhile,for normal cells (i.e.,L02) and tissues,neither fluorescence nor any other obvious difference could be detected between preand post-injection.This multimodal bioimaging strategy based on the in situ biosynthesized Zn&Fe oxide nanoclusters might therefore be useful for early cancer diagnosis and therapy.     

In situ trapped high-density single metal atoms within graphene: Iron-containing hybrids as representatives for efficient oxygen reduction

Nano Research - Atomically dispersed catalysts have attracted attention in energy conversion applications because their efficiency and chemoselectivity for special catalysis are superior to those...     

A Framework of Deep Optimal Features Selection for Apple Leaf Diseases Recognition

Identifying fruit disease manually is time-consuming, expert-required, and expensive; thus, a computer-based automated system is widely required. Fruit diseases affect not only the quality but also the quantity. As a result, it is possible to detect the disease early on and cure the fruits using computer-based techniques. However, computer-based methods face several challenges, including low contrast, a lack of dataset for training a model, and inappropriate feature extraction for final classification. In this paper, we proposed an automated framework for detecting apple fruit leaf diseases using CNN and a hybrid optimization algorithm. Data augmentation is performed initially to balance the selected apple dataset. After that, two pre-trained deep models are fine-tuning and trained using transfer learning. Then, a fusion technique is proposed named Parallel Correlation Threshold (PCT). The fused feature vector is optimized in the next step using a hybrid optimization algorithm. The selected features are finally classified using machine learning algorithms. Four different experiments have been carried out on the augmented Plant Village dataset and yielded the best accuracy of 99.8%. The accuracy of the proposed framework is also compared to that of several neural nets, and it outperforms them all.     

The mask of (2b + 4)-point n-ary subdivision scheme

In this paper, we present general formulae for the mask of (2b + 4)-point n-ary approximating as well as interpolating subdivision schemes for any integers ${b\,\geqslant\,0}$ and ${n\,\geqslant\,2}$ . These formulae corresponding to the mask not only generalize and unify several well-known schemes but also provide the mask of higher arity schemes. Moreover, the 4-point and 6-point a-ary schemes introduced by Lian [Appl Appl Math Int J 3(1):18–29, 2008] are special cases of our general formulae.     

Wind Farm Layout Design Using Cuckoo Search Algorithms

Wind energy has emerged as a strong alternative to fossil fuels for power generation. To generate this energy, wind turbines are placed in a wind farm. The extraction of maximum energy from these wind farms requires optimal placement of wind turbines. Due to complex nature of micrositing of wind turbines, the wind farm layout design problem is considered a complex optimization problem. In the recent past, various techniques and algorithms have been developed for optimization of energy output from wind farms. The present study proposes an optimization approach based on the cuckoo search (CS) algorithm, which is relatively a recent technique. A variant of CS is also proposed that incorporates a heuristic-based seed solution for a better performance. The proposed CS algorithms are compared with genetic and particle swarm optimization (PSO) algorithms, which have been extensively applied to wind farm layout design. Empirical results indicate that the proposed CS algorithms outperformed the genetic and PSO algorithms for the given test scenarios in terms of yearly power output and efficiency.     

Nonlinear robust state feedback control system design for nonlinear uncertain systems

This paper presents a systematic approach to the design of a nonlinear robust dynamic state feedback controller for nonlinear uncertain systems using copies of the plant nonlinearities. The technique is based on the use of integral quadratic constraints and minimax linear quadratic regulator control, and uses a structured uncertainty representation. The approach combines a linear state feedback guaranteed cost controller and copies of the plant nonlinearities to form a robust nonlinear controller with a novel control architecture. A nonlinear state feedback controller is designed for a synchronous machine using the proposed method. The design provides improved stability and transient response in the presence of uncertainty and nonlinearity in the system and also provides a guaranteed bound on the cost function. An automatic voltage regulator to track reference terminal voltage is also provided by a state feedback equivalent robust nonlinear proportional integral controller. Copyright © 2016 John Wiley & Sons, Ltd.     

Three-dimensional Numerical Study of Laminar Confined Slot Jet Impingement Cooling using Slurry of Nano—encapsulated Phase Change Material

This Article presents a three dimensional numerical model investigating thermal performance and hydrodynamics features of the confined slot jet impingement using slurry of Nano Encapsulated Phase Change Material (NEPCM) as a coolant. The slurry is composed of water as a base fluid and n-octadecane NEPCM particles with mean diameter of 100nm suspended in it. A single phase fluid approach is employed to model the NEPCM slurry.The thermo physical properties of the NEPCM slurry are computed using modern approaches being proposed recently and governing equations are solved with a commercial Finite Volume based code. The effects of jet Reynolds number varying from 100 to 600 and particle volume fraction ranging from 0% to 28% are considered. The computed results are validated by comparing Nusselt number values at stagnation point with the previously published results with water as working fluid. It was found that adding NEPCM to the base fluid results with considerable amount of heat transfer enhancement.The highest values of heat transfer coefficients are observed at H/W=4 and C_m=0.28. However, due to the higher viscosity of slurry compared with the base fluid, the slurry can produce drastic increase in pressure drop of the system that increases with NEPCM particle loading and jet Reynolds number.