Search results for "Machines"
showing 10 items of 113 documents
A Planar Generator for a Wave Energy Converter
2019
This article presents a permanent magnet planar translational generator which is able to exploit multiple modes of sea wave energy extraction. Linear electrical generators have recently been studied for the exploitation of sea wave energy, but, to the best of our knowledge, no synchronous planar translational generator has been proposed. In this article, to maximize the energy extraction, we have considered all the potential modes of motion due to wave excitation and included them within the mathematical model of the proposed system. The principle of operation of the generator can be summarized as follows: the moving part (translator) of the generator is driven from the sea waves and induce…
Computer-aided analysis and design procedure for rotating induction machine magnetic circuits and windings
2018
The aim of this study is to present a new, accurate, and user-friendly software procedure for the analysis and rapid design of rotating induction machine windings, considering both the electric and the magnetic specifications of the machine itself. This procedure is a valid aid for quick first stage design without the necessity of using finite element method (FEM)-based design procedures. FEM can be used in a second design phase in order to refine the first stage results. The design procedure is hereafter outlined and some examples show its capability.
Extreme minimal learning machine: Ridge regression with distance-based basis
2019
The extreme learning machine (ELM) and the minimal learning machine (MLM) are nonlinear and scalable machine learning techniques with a randomly generated basis. Both techniques start with a step in which a matrix of weights for the linear combination of the basis is recovered. In the MLM, the feature mapping in this step corresponds to distance calculations between the training data and a set of reference points, whereas in the ELM, a transformation using a radial or sigmoidal activation function is commonly used. Computation of the model output, for prediction or classification purposes, is straightforward with the ELM after the first step. In the original MLM, one needs to solve an addit…
Kick Detection and Influx Size Estimation during Offshore Drilling Operations using Deep Learning
2019
An uncontrolled or unobserved influx or kick during drilling has the potential to induce a well blowout, one of the most harmful incidences during drilling both in regards to economic and environmental cost. Since kicks during drilling are serious risks, it is important to improve kick and loss detection performance and capabilities and to develop automatic flux detection methodology. There are clear patterns during a influx incident. However, due to complex processes and sparse instrumentation it is difficult to predict the behaviour of kicks or losses based on sensor data combined with physical models alone. Emerging technologies within Deep Learning are however quite adapt at picking up …
Eventual Consistency Formalized
2019
Distribution of computation is well-known, and there are several frameworks, including some formal frameworks, that capture distributed computation. As yet, however, models of distributed computation are based on the idea that data is conceptually centralized. That is, they assume that data, even if it is distributed, is consistent. This assumption is not valid for many of the database systems in use today, where consistency is compromised to ensure availability and partition tolerance. Starting with an informal definition of eventual consistency, this paper explores several measures of inconsistency that quantify how far from consistency a system is. These measures capture key aspects of e…
Learning automata based energy-efficient AI hardware design for IoT applications
2020
Energy efficiency continues to be the core design challenge for artificial intelligence (AI) hardware designers. In this paper, we propose a new AI hardware architecture targeting Internet of Things applications. The architecture is founded on the principle of learning automata, defined using propositional logic. The logic-based underpinning enables low-energy footprints as well as high learning accuracy during training and inference, which are crucial requirements for efficient AI with long operating life. We present the first insights into this new architecture in the form of a custom-designed integrated circuit for pervasive applications. Fundamental to this circuit is systematic encodin…
Improving active learning methods using spatial information
2011
Active learning process represents an interesting solution to the problem of training sample collection for the classification of remote sensing images. In this work, we propose a criterion based on the spatial information that can be used in combination with a spectral criterion in order to improve the selection of training samples. Experimental results obtained on a very high resolution image show the effectiveness of regularization in spatial domain and open challenging perspectives for terrain campaigns planning. © 2011 IEEE.
Intra-individual gait patterns across different time-scales as revealed by means of a supervised learning model using kernel-based discriminant regre…
2017
Objective Traditionally, gait analysis has been centered on the idea of average behavior and normality. On one hand, clinical diagnoses and therapeutic interventions typically assume that average gait patterns remain constant over time. On the other hand, it is well known that all our movements are accompanied by a certain amount of variability, which does not allow us to make two identical steps. The purpose of this study was to examine changes in the intra-individual gait patterns across different time-scales (i.e., tens-of-mins, tens-of-hours). Methods Nine healthy subjects performed 15 gait trials at a self-selected speed on 6 sessions within one day (duration between two subsequent ses…
Fracture Mechanics of Collagen Fibrils: Influence of Natural Cross-Links
2013
AbstractTendons are important load-bearing structures, which are frequently injured in both sports and work. Type I collagen fibrils are the primary components of tendons and carry most of the mechanical loads experienced by the tissue, however, knowledge of how load is transmitted between and within fibrils is limited. The presence of covalent enzymatic cross-links between collagen molecules is an important factor that has been shown to influence mechanical behavior of the tendons. To improve our understanding of how molecular bonds translate into tendon mechanics, we used an atomic force microscopy technique to measure the mechanical behavior of individual collagen fibrils loaded to failu…
How self-assembly of amphiphilic molecules can generate complexity in the nanoscale
2015
Abstract Given the importance of nanomaterials and nanostructures in modern technology, in the past decades much effort has been directed to set up efficient bottom up protocols for the piloted self-assembly of molecules. However, molecules are generally disinclined to adopt the desired structural organization because they behave according to their own specific intermolecular interactions. Thus, only some selected classes of chemical compounds are capable to lead to useful self-assembled structures. Amphiphiles, simultaneously possessing polar and apolar moieties within their molecular architecture, can give a wide scenario of possible intermolecular interactions: polar–polar, polar–apolar,…