Search results for "Machine"
showing 10 items of 2592 documents
Digital demodulation for fast set-up of sensorless PMSM electrical drives based on magnetic anisotropy
2009
This paper presents a demodulation algorithm applied to the sensorless control of permanent magnet synchronous motors (PMSM). The sensorless control technique reported in this paper is based on the injection of high frequency voltages to the stator so that the motor currents contain information on the rotor position. This information is then extracted by an appropriate demodulation technique. The presented demodulation algorithm is based on spectral analysis of the signal produced by the sensorless control technique because this is fully characterized and presents not ambiguous correlation between current harmonic content and rotor position. The most important feature of the demodulation al…
4th International Workshop on Language Engineering (ATEM 2007)
2008
Following the great success of previous editions, ATEM2007 is the 4thedition of the ATEM workshop series. The first two editions were held with WCRE in 2003 and 2004, while the 3rdone was held with MoDELS 2006. ATEM has always been focused on engineering of language descriptions. In order to cover as many aspects of language descriptions important for greater success and adoption of model-driven engineering, ATEM has been evolving so as its scope: The first edition was about metamodelsand schemas. The second about was metamodels, schemasand grammars. The third edition was about metamodels, schemas, grammarsand ontologies.
Design Considerations of Transverse Flux Generator to Sea Wave Energy
2014
In this paper we study the possibility to use a transverse flux linear generator (TFG) because transverse flux technology presents the highest force density per volume index among the iron based electrical machines. This paper is organized as follows an introduction to describe "state of the art" of WEC's and TFG, in the Section II we find a summary description of the marine condition of the chosen site; in the Section III there is an the analytical procedure for geometric and magnetic design of device (chosen material, length, etc) and in the Section IV the simulation of generator. Further defined the design of the generator, the machine was designed and analyzed through the use of a 3D FE…
An ontology-based metamodel for multiagent-based simulations
2014
Multiagent-based simulations enable us to validate dierent use-case scenarios in a lot of application domains. The idea is to develop a realistic virtual environment to test particular domain-specic procedures. This paper presents our general framework for interactive multiagent-based simulations in virtual environments. The major contribution of this paper is the integration of the notion of ontology as a core element to the design process of a behavioral simulation. The proposed metamodel describes the concepts of a multiagent simulation using situated agents moving in a semantically enriched 3D environment. The agents perceive the geometric and semantic data in the surrounding environmen…
IMAGE PROCESSING, SEGMENTATION AND MACHINE LEARNING MODELS TO CLASSIFY AND DELINEATE TUMOR VOLUMES TO SUPPORT MEDICAL DECISION
2020
Techniques for processing and analysing images and medical data have become the main’s translational applications and researches in clinical and pre-clinical environments. The advantages of these techniques are the improvement of diagnosis accuracy and the assessment of treatment response by means of quantitative biomarkers in an efficient way. In the era of the personalized medicine, an early and efficacy prediction of therapy response in patients is still a critical issue. In radiation therapy planning, Magnetic Resonance Imaging (MRI) provides high quality detailed images and excellent soft-tissue contrast, while Computerized Tomography (CT) images provides attenuation maps and very good…
A genetic algorithm approach to purify the classifier training labels for the analysis of remote sensing imagery
2017
This paper proposes a Genetic Algorithm (GA) approach to clean a given classifier training set for remote sensing image analysis. Starting from an initial set of training data, the new method called GA-Training Label Purifying (GA-TLP) consists of the significant training sample selection using GAs in order to maximize the classifier accuracy. This means to retain the most informative samples and to remove the uncertain, redundant, and misclassified ones. As a result of the selection process, we can obtain a purified training set. The proposed model is implemented and evaluated using a LANDSAT 7 ETM+ image. The experimental results confirm the effectiveness of the proposed approach.
Novel scaffold of natural compound eliciting sweet taste revealed by machine learning
2020
Abstract Sugar replacement is still an active issue in the food industry. The use of structure-taste relationships remains one of the most rational strategy to expand the chemical space associated to sweet taste. A new machine learning model has been setup based on an update of the SweetenersDB and on open-source molecular features. It has been implemented on a freely accessible webserver. Cellular functional assays show that the sweet taste receptor is activated in vitro by a new scaffold of natural compounds identified by the in silico protocol. The newly identified sweetener belongs to the lignan chemical family and opens a new chemical space to explore.
Comparative in vitro study of the accuracy of impression techniques for dental implants: Direct technique with an elastomeric impression material ver…
2019
Background The aim of this study was to compare a conventional technique (elastomeric impression material - EIM) and a digital technique (scanner digital model – SDM) on a six-analog master model (MM) to determine which was the most exact. Material and Methods Twenty impressions were taken of a master model (EIM) and twenty scanned impressions (SDM) (True Definition). A coordinate measuring machine (CMM) was used to measure the distances between adjacent analogues (1-2, 2-3, 3-4, 4-5, 5-6), intermittently positioned analogues (1-4, 3-6) and the most distal (1-6). Reference values were established from the master model, which were compared with the two impression techniques. The significance…
A Learning Automata Based Solution to Service Selection in Stochastic Environments
2010
Published version of a paper published in the book: Trends in Applied Intelligent Systems. Also available on SpringerLink: http://dx.doi.org/10.1007/978-3-642-13033-5_22 With the abundance of services available in today’s world, identifying those of high quality is becoming increasingly difficult. Reputation systems can offer generic recommendations by aggregating user provided opinions about service quality, however, are prone to ballot stuffing and badmouthing . In general, unfair ratings may degrade the trustworthiness of reputation systems, and changes in service quality over time render previous ratings unreliable. In this paper, we provide a novel solution to the above problems based …
On Obtaining Classification Confidence, Ranked Predictions and AUC with Tsetlin Machines
2020
Tsetlin machines (TMs) are a promising approach to machine learning that uses Tsetlin Automata to produce patterns in propositional logic, leading to binary (hard) classifications. In many applications, however, one needs to know the confidence of classifications, e.g. to facilitate risk management. In this paper, we propose a novel scheme for measuring TM confidence based on the logistic function, calculated from the propositional logic patterns that match the input. We then use this scheme to trade off precision against recall, producing area under receiver operating characteristic curves (AUC) for TMs. Empirically, using four real-world datasets, we show that AUC is a more sensitive meas…