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- On asynchronous parallelization of order-based GA over grid-enabled heterogenous commodity hardwarePublication . Valente de Oliveira, JOSÉ; Baltazar, Sérgio; Daniel, HelderIn real-world applications, the runtime of genetic algorithms (GAs) can be computationally demanding, an issue that can be mitigated using parallelization. The study evaluates the parallelization of order-based GAs using the island model in an asynchronous heterogeneous computing environment. The island model allows for a considerable number of migration topologies. The study offers a systematic review of the studies on migration topologies and observes that no study is available yet on the performance of these migration topologies over asynchronous heterogeneous environments. Based on a statistical analysis of a comprehensive set of experiments, using real-world TSPLIB instances, the study researches the question: What is the fastest island model topology for order-based genetic algorithm, in an asynchronous distributed heterogeneous grid-enabled commodity computing environment, without losing significant fitness comparatively to the correspondent sequential panmictic implementation of the same algorithm?. Moreover, a new speedup index, the expected root speedup, is also proposed. A diversity of topology types and characteristics are considered: the single node, star, ring, cartwheel, rooted ordered tree, rooted full binary tree, coordinated tree-ring, and feedforward fully connected layered type. Different number of nodes are also considered. While some of the types of topologies are well known, the coordinated tree-ring topology is a novelty. These types of topologies allow us to assess three notable cases: (i) no migration (isolated island), (ii) migration toward the coordinator only, and (iii) migration flows to, and from, the coordinator.
- A file group data replication algorithm for data gridsPublication . Rahmani, Amir Masoud; Azari, Leila; Daniel, HelderIn recent years data grids have been deployed and grown in many scientific experiments and data centers. The deployment of such environments has allowed grid users to gain access to a large number of distributed data. Data replication is a key issue in a data grid and should be applied intelligently because it reduces data access time and bandwidth consumption for each grid site. Therefore this area will be very challenging as well as providing much scope for improvement. In this paper, we introduce a new dynamic data replication algorithm named Popular File Group Replication, PFGR which is based on three assumptions: first, users in a grid site (Virtual Organization) have similar interests in files and second, they have the temporal locality of file accesses and third, all files are read-only. Based on file access history and first assumption, PFGR builds a connectivity graph for a group of dependent files in each grid site and replicates the most popular group files to the requester grid site. After that, when a user of that grid site needs some files, they are available locally. The simulation results show that our algorithm increases performance by minimizing the mean job execution time and bandwidth consumption and avoids unnecessary replication.
- Wind turbines drive train fault detection: random forests vs CNNsPublication . Daniel, Helder; Baltazar, Sérgio; Li, Chuan; LUÍS VALENTE DE OLIVEIRA, JOSÉThe production of wind-powered energy is harvested by huge wind turbines installed in locations where winds are strong but difficult to access. Detecting minor severity faults allows for the scheduling of defective component replacement during planned maintenance dates, before the fault severity increases. This significantly reduces maintenance costs. A key component of wind turbines is the drivetrain, which transfers mechanical energy from the rotating blades to an electric energy generator. This gearbox system is quite exposed to faults, such as damaged gears and broken or worn teeth. This paper presents and discuss the identification and classification of gearbox faults using vibration and acoustic emission signals. It is shown that Random Forests (RFs) classifiers can be trained to achieve 100% accuracy rate, by performing previously classical feature extraction[8] on the raw signals, while Convolutional Neural Networks (CNNs) classifiers also achieve 100% accuracy rates, directly on raw signals and with a shorter duration than required by RF classifiers.