2D multi-objective placement algorithm for free-form components

This article presents a generic method to solve 2D multi-objective placement problem for free-form components. The proposed method is a relaxed placement technique combined with an hybrid algorithm based on a genetic algorithm and a separation algorithm. The genetic algorithm is used as a global optimizer and is in charge of efficiently exploring the search space. The separation algorithm is used to legalize solutions proposed by the global optimizer, so that placement constraints are satisfied. A test case illustrates the application of the proposed method. Extensions for solving the 3D problem are given at the end of the article.Comment: ASME 2009 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, San Diego : United States (2009

A Time-driven Data Placement Strategy for a Scientific Workflow Combining Edge Computing and Cloud Computing

Compared to traditional distributed computing environments such as grids, cloud computing provides a more cost-effective way to deploy scientific workflows. Each task of a scientific workflow requires several large datasets that are located in different datacenters from the cloud computing environment, resulting in serious data transmission delays. Edge computing reduces the data transmission delays and supports the fixed storing manner for scientific workflow private datasets, but there is a bottleneck in its storage capacity. It is a challenge to combine the advantages of both edge computing and cloud computing to rationalize the data placement of scientific workflow, and optimize the data transmission time across different datacenters. Traditional data placement strategies maintain load balancing with a given number of datacenters, which results in a large data transmission time. In this study, a self-adaptive discrete particle swarm optimization algorithm with genetic algorithm operators (GA-DPSO) was proposed to optimize the data transmission time when placing data for a scientific workflow. This approach considered the characteristics of data placement combining edge computing and cloud computing. In addition, it considered the impact factors impacting transmission delay, such as the band-width between datacenters, the number of edge datacenters, and the storage capacity of edge datacenters. The crossover operator and mutation operator of the genetic algorithm were adopted to avoid the premature convergence of the traditional particle swarm optimization algorithm, which enhanced the diversity of population evolution and effectively reduced the data transmission time. The experimental results show that the data placement strategy based on GA-DPSO can effectively reduce the data transmission time during workflow execution combining edge computing and cloud computing

Optimal Sensors Placement to Enhance Damage Detection in Composite Plates

This paper examines an important challenge in ultrasonic structural health monitoring (SHM), which is the problem of the optimal placement of sensors in order to accurately detect and localize damages. The goal of this study is to enhance damage detection through an optimal sensor placement (OSP) algorithm. The problem is formulated as a global optimization problem, where the objective function to be maximized is evaluated by a ray tracing approach, which approximately models Lamb waves propagation. A genetic algorithm (GA) is then used to solve this optimization problem. Simulations and experiments were conducted to validate the proposed method on a carbon epoxy composite plate. Results show the effectiveness and the advantages of the proposed method as a tool for OSP with reasonable computation time.Projet AIRCELLE (EPICE/CORALIE

Optimized estimator for real-time dynamic displacement measurement using accelerometers

This paper presents a method for optimizing the performance of a real-time, long term, and accurate accelerometer based displacement measurement technique, with no physical reference point. The technique was applied in a system for measuring machine frame displacement. The optimizer has three objectives with the aim to minimize phase delay, gain error and sensor noise. A multi-objective genetic algorithm was used to find Pareto optimal estimator parameters. The estimator is a combination of a high pass filter and a double integrator. In order to reduce the gain and phase errors two approaches have been used: zero placement and pole-zero placement. These approaches were analysed based on noise measurement at 0g-motion and compared. Only the pole-zero placement approach met the requirements for phase delay, gain error, and sensor noise. Two validation experiments were carried out with a Pareto optimal estimator. First, long term measurements at 0g-motion with the experimental setup were carried out, which showed displacement error of 27.6 ± 2.3 nm. Second, comparisons between the estimated and laser interferometer displacement measurements of the vibrating frame were conducted. The results showed a discrepancy lower than 2 dB at the required bandwidth

OPTIMIZATION OF ACCESS POINT ARRANGEMENT AND PLACEMENT IN THE INDOOR ROOM OF SMP NEGERI 6 SALATIGA USING GENETIC ALGORITHM

Access points are generally only recommended to load up to 40 clients only. Thus, proper placement and arrangement of access points in a room or building can optimize the signal strength received by users. The purpose of this study is to optimize the arrangement and placement of access points built using the genetic algorithm method. In the system built, the user is required to input the number of populations, iterations and the number of nodes 1 and 2. In the program implementation, the population functions to load the genes resulting from the possible placement of the access point based on the iteration results. The access point placement optimization system using the genetic algorithm method has been successfully implemented, the system can perform calculations in processing the ideal access point placement. Access point placement using genetic algorithms can provide recommendations for network architecture design in terms of the coverage area that needs to be used so that designers can save time on node point design and estimate the ideal price in determining the number of access points for network installation and can assist in determining the required coverage area. ideal for schools

Optimal sensor placement for classifier-based leak localization in drinking water networks

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper presents a sensor placement method for classifier-based leak localization in Water Distribution Networks. The proposed approach consists in applying a Genetic Algorithm to decide the sensors to be used by a classifier (based on the k-Nearest Neighbor approach). The sensors are placed in an optimal way maximizing the accuracy of the leak localization. The results are illustrated by means of the application to the Hanoi District Metered Area and they are compared to the ones obtained by the Exhaustive Search Algorithm. A comparison with the results of a previous optimal sensor placement method is provided as well.Postprint (author's final draft

Genetic Diversity, Phylogenetics and Molecular Systematics of Guizotia Cass. (Asteraceae)

The genus Guizotia belongs to the tribe Heliantheae in the family Asteraceae. It has been placed under different subtribes. The genus has its center of origin, distribution and genetic diversity in Ethiopia, where G. abyssinica (niger) has been domesticated. Amplified Fragment Length Polymorphism (AFLP), Random Amplified Polymorphic DNA (RAPD) and DNA sequencing were applied to study the genetic diversity, phylogenetics, and molecular systematics of this genus. A large number of niger populations, representing all regions in Ethiopia where this crop is grown, was investigated using AFLP and RAPD molecular marker techniques. The extent of genetic variation in niger is distributed throughout its growing regions, regardless of the extent and altitude of cultivation. Despite the fact that most of the variation was within populations, significant population differentiation was obtained (AMOVA; P < 0.001) in all guizotias. It is concluded that both G. abyssinica and its wild and/or weedy relatives have wide genetic bases that need to be conserved and utilized for the improvement of G. abyssinica. Further collection of niger germplasm and exploration and conservation of highly localized guizotias are recommended. Most of the diagnostic markers generated from AFLPs and RAPDs in this study were specific to G. arborescens and G. zavattarii. Phylogenetic analyses of the genus Guizotia were undertaken based on molecular sequence data from the internal transcribed spacers (ITS) and five chloroplast DNA regions. The analyses revealed a close phylogenetic relationship between G. abyssinica and G. scabra ssp. schimperi and support the previous suggestion that the latter is the progenitor of the former. According to this study, G. scabra ssp. scabra, G. scabra ssp. schimperi, and the Chelelu and Ketcha populations are best viewed at present as separate species within the genus Guizotia. Those perennial guizotias with highly localized geographic distribution appears to have evolved first during the evolutionary history of the genus. This study supports the placement of the genus Guizotia within the subtribe Milleriinae. It is suggested that the present species composition of Guizotia and the subtribal placement of the genus need to be redefined