As optical metasurfaces become progressively ubiquitous, the expectations from them are becoming increasingly complex. The limited number of structural parameters in the conventional metasurface building blocks, and existing phase engineering rules do not completely support the growth rate of metasurface applications. In this paper, we present digitized-binary elements, as alternative high-dimensional building blocks, to accommodate the needs of complex-tailorable-multifunctional applications. To design these complicated platforms, we demonstrate adaptive genetic algorithm (AGA), as a powerful evolutionary optimizer, capable of handling such demanding design expectations. We solve four complex problems of high current interest to the optics community, namely, a binary-pattern plasmonic reflectarray with high tolerance to fabrication imperfections and high reflection efficiency for beam-steering purposes, a dual-beam aperiodic leaky-wave antenna, which diffracts TE and TM excitation waveguides modes to arbitrarily chosen directions, a compact birefringent all-dielectric metasurface with finer pixel resolution compared to canonical nano-antennas, and a visible-transparent infrared emitting/absorbing metasurface that shows high promise for solar-cell cooling applications, to showcase the advantages of the combination of binary-pattern metasurfaces and the AGA technique. Each of these novel applications encounters computational and fabrication challenges under conventional design methods, and is chosen carefully to highlight one of the unique advantages of the AGA technique. Finally, we show that large surplus datasets produced as by-products of the evolutionary optimizers can be employed as ingredients of the new-age computational algorithms, such as, machine learning and deep leaning. In doing so, we open a new gateway of predicting the solution to a problem in the fastest possible way based on statistical analysis of the datasets rather than researching the whole solution space.

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Adaptive Genetic Algorithm for Optical Metasurfaces Design

Due to the possibility of providing energy with much less dependence on the fossil fuels, renew-competent energy sources, in specified sun photovoltaic (PV) conversion have received elevated acceptance and progress in latest times. Big benefits of PV panels comprise easy and trustworthy power production and suitability for disbursed iteration. In addition the costs for photovoltaic modules is drastically lowering. To comprehend this issue, a control plan with modulation compensation scheme is likewise proposed. An exploratory three-stage seven-level cascaded H-bridge inverter has been manufactured using nine H-bridge modules (three modules for each stage). Each H-bridge module is associated with a 185-W solar panel. Simulation results are introduced to confirm the practicality of the proposed approach.

Modular Cascaded H-Bridge Multilevel PV Inverter with Distributed MPPT for Grid-Connected Applications

Although mesh-connected computers are used almost exclusively for low-level local image processing, they are also suitable for higher level image processing tasks We illustrate this by presenting new optimal (in the O-notational sense) algorithms for computing several geometric properties of figures For example, given a black/white picture stored one pixel per processing element in an n × n mesh-connected computer, we give ?(n) time algorithms for determining the extreme points of the convex hull of each component, for deciding if the convex hull of each component contains pixels that are not members of the component, for deciding if two sets of processors are linearly separable, for deciding if each component is convex, for determining the distance to the nearest neighboring component of each component, for determining internal distances in each component, for counting and marking minimal internal paths in each component, for computing the external diameter of each component, for solving the largest empty circle problem, for determining internal diameters of components without holes, and for solving the all-points farthest point problem Previous mesh-connected computer algorithms for these problems were either nonexistent or had worst case times of ?(n2) Since any serial computer has a best case time of ?(n2) when processing an n × n image, our algorithms show that the mesh-connected computer provides significantly better solutions to these problems

Geometric algorithms for digitized pictures on a mesh-connected computer

Investigation on load harmonic reduction through solar-power utilization in intermittent SSFI using particle swarm, genetic, and modified firefly optimization algorithms

A new Symmetric Solar Fed Inverter (SSFI) proposed with a reduced number of components compared to the classical, modified, conventional type of Multilevel Inverter (MLI). The objective of this architecture is to design fifteen-level SSFI, this circuit uses a single switch with minimizing harmonics, and Modulation Index (MI) values. Power Quality (PQ) is developed by using the optimization algorithms like as Particle Swarm Optimization (PSO), Genetic algorithm (GA), Modified Firefly Algorithm (MFA). It’s determined to generate the gating pulse and finding optimum firing angle values calculate as per the input of MPP intelligent controller schemes. The proposed circuit is solar fed inverter used for optimization techniques governed by switching controller approach delivers a major task. The comparison is made for different optimization algorithm has significantly reduced the harmonic content by varying the modulation index and switching angle values. SSFI generates low distortion output uses through without any additional filter component through utilizing MATLAB Simulink software (2020a). The SSFI circuit assist Xilinx Spartan 3-AN Filed Program Gate Array (FPGA) tuned by optimization techniques are presented for the effectiveness of the proposed model. 

Multilevel inverters are one of the most brilliant technology evolution in power conversion. It provides a stair wave output signal out of multi DC sources used as input. This paper presents a new multilevel inverter topology. The 7 levels architecture principle used in this study is first presented. In order to achieve a better signal output quality an adaptive genetic algorithm is used to reduce the harmonic content. The THD minimization is realized by calculating the appropriate switching angle in real time. However this study is focused on the 7 levels case, it also refers to other levels. The analysis is based on THD evaluation in different cases. All simulations and programming were done using Matlab.

A new multilevel inverter with genetic algorithm optimization for hybrid power station application

In this paper we present the design of a fast middleware for high performance computing on distributed systems. The proposed model allows us to build a polymorphic grid computing assigned to solve fine grained parallel problems over different well known, SIMD (Single Instruction Multiple Data), SPMD (Single Program Multiple Data), MIMD (Multiple Instruction Multiple Data) ) and MPMD (Multiple Program Multiple Data) machine structures. This model is built using dynamic distributed Virtual Processors Elements (VPE). Each physical processing unit is assigned to a node of the considered distributed system. These units may be heterogeneous machines such supercomputers, simple desktop or smart phones etc. The VPEs are designed to communicate with each other by exchanging, in local or remote ports, messages containing data, instructions or any task to be performed. The life cycle of VPEs, the load balancing system and the parallel application to be run, are managed by the VPE Manager which represent the virtual host of this model. At runtime VPEs can migrate from overloaded to under loaded nodes in order to keep the system in a perfect balancing state. The parallel application based in this middleware can be implemented in different languages. In this paper, we present the java implementation. To valid the performance of this Middleware, the paper presents two examples of parallel filtering and segmentation c-means algorithms, using program code and flow sheet

A Fast Middleware For Massively Parallel And Distributed Computing

In this paper, we present a single phase 5 levels H-Bridge multilevel inverter (CHMLI) with battery balancing technique. Each single full bridge is directly connected to a battery inside the power bank. The different combinations and batteries wiring sets offer the possibility to control the batteries discharge. The cascaded H-Bridge multilevel inverter is first described and the discharge is studied in normal conditions under different stress scenarios. State of charge (SOC) balancing technique is then achieved using an equalization algorithm controlling the different switching combination inside the power bank. Results of the simulation model with and without the SOC balancing is presented using Matlab.

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A Cascaded H-Bridge Multilevel Inverter with SOC Battery Balancing

In this paper, a new approach is presented based on ant colony algorithm with time windows in order to optimize daily activity chains with flexible mobility solutions. This flexibility is realized by temporal and spatial change of activities achieved by travellers during one day. With the injection of flexibility concept of time and locations, the requirements for such a transport system are high. However, our method has shown promising results by decreasing 10 to 20% the total travel time of travellers based on combining and comparing different transport modes including the private transport as well as the public transport and by choosing the optimal set of activities using our method.

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Simulation Results for a Daily Activity Chain Optimization Method based on Ant Colony Algorithm with Time Windows

Insurance fraud is one of the most practiced frauds in the sectors of the economy. Faced with increasingly imaginative underwriters to create fraud scenarios and the emergence of organized crime groups, the fraud detection process based on artificial intelligence remains one of the most effective approaches. Real world datasets are usually unbalanced and are mainly composed of "no-fraudulent" class with a very small percentage of "fraudulent" examples to train our model, thus prediction models see their performance severely degraded when the target class appears so poorly represented. Therefore, the present work aims to propose an approach that improves the relevance of the results of the best-known machine learning algorithms and deals with imbalanced classes in classification problems for prediction against insurance fraud. We use one of the most efficient approaches to re-balance training data: SMOTE. We adopted the supervised method applied to automobile claims dataset "carclaims.txt". We compare the results of the different measurements and question the results and relevance of the measurements in the field of study of unbalanced and labeled datasets. This work shows that the SMOTE Method with the KNN Algorithm can achieve better classifier performance in a True Positive Rate than the previous research. The goal of this work is to lead a study of algorithm selections and performance evaluation among different ML classification algorithms, as well as to propose a new approach TH-SMOTE for performance improvement using the SMOTE method by defining the optimum oversampling threshold according to the G-mean measure.

/pdf/empirical-oversampling-threshold-strategy-for-machine-89rkbc2bzf.pdf

Bouattane Omar

Papers

A new multilevel inverter with genetic algorithm optimization for hybrid power station application

A Fast Middleware For Massively Parallel And Distributed Computing

A Cascaded H-Bridge Multilevel Inverter with SOC Battery Balancing

Simulation Results for a Daily Activity Chain Optimization Method based on Ant Colony Algorithm with Time Windows

Empirical Oversampling Threshold Strategy for Machine Learning Performance Optimisation in Insurance Fraud Detection