Search results for " Mach"
showing 10 items of 1388 documents
Maximum Torque per Ampere Control strategy for low-saliency ratio IPMSMs
2019
This paper deals with electrical drives employing low-saliency ratio interior permanent magnet synchronous motors. In particular, in order to help the designers choosing the best control algorithm, the performances of the Maximum Torque Per Ampere Control (MTPA) and the Field Orientation Control (FOC) are here both theoretically and experimentally assessed and compared, by using, as performance indicators, the torque-current ratio and the power losses. The tests are carried out on a low-power motor for various speeds and loads by implementing the two control strategies in a dSPACE® rapid prototyping system. The results show that the Maximum Torque Per Ampere algorithm has some appreciable a…
Development of a self-pumping extracorporeal blood oxygenation device characterized by a rotating shaft with embedded fiber packages.
2019
Introduction: To offer respiratory support for patients with lung disease, a novel technological solution for blood pumping and oxygenation is being developed. The pump–lung system was designed to integrate fiber membranes into six packages radially embedded in a rotating hollow shaft placed along the longitudinal axis of the device. Fiber packages are inclined with respect to the rotation axis so that the rotational motion of the rotating shaft allows a self-pumping system to be obtained. Method: Both hemodynamic and gas transfer performances were investigated using both in vitro experiments and in silico flow analyses. Results: The predicted flow velocity in the pump chamber was smooth an…
Strategies to develop radiomics and machine learning models for lung cancer stage and histology prediction using small data samples
2021
Abstract Predictive models based on radiomics and machine-learning (ML) need large and annotated datasets for training, often difficult to collect. We designed an operative pipeline for model training to exploit data already available to the scientific community. The aim of this work was to explore the capability of radiomic features in predicting tumor histology and stage in patients with non-small cell lung cancer (NSCLC). We analyzed the radiotherapy planning thoracic CT scans of a proprietary sample of 47 subjects (L-RT) and integrated this dataset with a publicly available set of 130 patients from the MAASTRO NSCLC collection (Lung1). We implemented intra- and inter-sample cross-valida…
Faults diagnosis based on proportional integral observer for TS fuzzy model with unmeasurable premise variable
2014
In this work, we focus on the synthesis of a Proportional Integral (PI) observer for the actuators and sensors faults diagnosis based on Takagi-Sugeno (TS) fuzzy model with unmeasurable premise variables. The faults estimation method is based on the assumption that these faults act as unknown inputs under polynomials form whose their kth derivatives are bounded. The convergence conditions of the observer as well as the faults reconstruction are established on the basis of the Lyapunov stability theory and the L 2 optimization technique, expressed as Linear Matrix Inequalities (LMI) constraints. In order to validate the proposed approach, a hydraulic system with two tanks is proposed.
Tool support for MOLA
2006
AbstractThe paper describes the MOLA Tool, which supports the model transformation language MOLA. MOLA Tool consists of two parts: MOLA definition environment and MOLA execution environment. MOLA definition environment is based on the GMF (Generic Modeling Framework) and contains graphical editors for metamodels and MOLA diagrams, as well as the MOLA compiler. The main component of MOLA execution environment is a MOLA virtual machine, which performs model transformations, using an SQL database as a repository. The execution environment may be used as a plug-in for Eclipse based modeling tools (e.g., IBM Rational RSA). The current status of the tool is truly academic.
An Efficient and Secure Energy Trading Approach with Machine Learning Technique and Consortium Blockchain
2022
In this paper, a secure energy trading mechanism based on blockchain technology is proposed. The proposed model deals with energy trading problems such as insecure energy trading and inefficient charging mechanisms for electric vehicles (EVs) in a vehicular energy network (VEN). EVs face two major problems: finding an optimal charging station and calculating the exact amount of energy required to reach the selected charging station. Moreover, in traditional trading approaches, centralized parties are involved in energy trading, which leads to various issues such as increased computational cost, increased computational delay, data tempering and a single point of failure. Furthermore, EVs fac…
Smart River : Towards Efficient Cooperative Autonomous Inland Navigation
2022
In recent years, inland waterway transport has witnessed increasing attention from France and many European countries. However, this mode of transport lacks flexibility, has an aging infrastructure, and the current ships are not adapted to an increase in transport capacity ensuring the safety of vessels and goods as well as reliable and constant delivery times. Therefore, inland transport must go through an organizational and technical renovation specific to its particular environment in order to hope to compete with land transport.In this thesis, we propose developing a smart river ecosystem that focuses on three principal axes: (i) automatic inland infrastructure, (ii) autonomous inland s…
Editorial: Consciousness in Humanoid Robots
2019
Building a conscious robot is a grand scientific and technological challenge. Debates about the possibility of conscious robots and the related positive outcomes and hazards for human beings are today no longer confined to philosophical circles.
A case study on feature sensitivity for audio event classification using support vector machines
2016
Automatic recognition of multiple acoustic events is an interesting problem in machine listening that generalizes the classical speech/non-speech or speech/music classification problem. Typical audio streams contain a diversity of sound events that carry important and useful information on the acoustic environment and context. Classification is usually performed by means of hidden Markov models (HMMs) or support vector machines (SVMs) considering traditional sets of features based on Mel-frequency cepstral coefficients (MFCCs) and their temporal derivatives, as well as the energy from auditory-inspired filterbanks. However, while these features are routinely used by many systems, it is not …
Energy Harvesting Enabled Energy Efficient Cognitive Machine-to-Machine Communications
2020
Energy harvesting based cognitive machine-to-machine (EH-CM2M) communication has been introduced to overcome the problem of spectrum scarcity and limited battery capacity by enabling M2M transmitters (M2M-TXs) to harvest energy from ambient radio frequency signals, as well as to reuse the resource blocks (RBs) allocated to CUs in an opportunistic manner. However, the complex interference scenarios and the stringent QoS requirements pose new challenges on resource allocation optimization. In this chapter, we consider how to maximize the energy efficiency of M2M-TXs via the joint optimization of channel selection, peer discovery, power control, and time allocation.