Search results for "electrical"
showing 10 items of 11048 documents
Model Predictive Control for Shunt Active Filters With Fixed Switching Frequency
2016
This paper presents a modification to the classical Model Predictive Control algorithm, named Modulated Model Predictive Control, and its application to active power filters. The proposed control is able to retain all the advantages of a Finite Control Set Model Predictive Control whilst improving the generated waveforms harmonic spectrum. In fact a modulation algorithm, based on the cost function ratio for different output vectors, is inherently included in the MPC. The cost function-based modulator is introduced and its effectiveness on reducing the current ripple is demonstrated. The presented solution provides an effective and straightforward single loop controller, maintaining an excel…
Low repetition rate gain-switched double-clad thulium-doped fiber laser operating in the 2 µm wavelength region
2021
Abstract The experimental demonstration of a gain-switched pulsed fiber laser with low repetition rate emission in the 2 µm wavelength region is presented. The laser cavity is based on the figure-9 shape, where the gain-switched operation of the laser is obtained by using a double-clad Tm-doped fiber (DCTDF) as gain medium and a commercial pulsed laser diode at 793-nm with configurable parameters as pump source. The pulse parameters of the pump source are optimized for efficient suppressing of unstable gain-switched laser oscillations. As a result, laser pulses with low repetition rate in a range from 10 to 20 kHz with laser emission at the wavelength of 1951 nm are obtained. The generated …
Remote sensing image segmentation by active queries
2012
Active learning deals with developing methods that select examples that may express data characteristics in a compact way. For remote sensing image segmentation, the selected samples are the most informative pixels in the image so that classifiers trained with reduced active datasets become faster and more robust. Strategies for intelligent sampling have been proposed with model-based heuristics aiming at the search of the most informative pixels to optimize model's performance. Unlike standard methods that concentrate on model optimization, here we propose a method inspired in the cluster assumption that holds in most of the remote sensing data. Starting from a complete hierarchical descri…
Recognition of Falls and Daily Living Activities Using Machine Learning
2018
A robust fall detection system is essential to support the independent living of elderlies. In this context, we develop a machine learning framework for fall detection and daily living activity recognition. Using acceleration data from public databases, we test the performance of two algorithms to classify seven different activities including falls and activities of daily living. We extract new features from the acceleration signal and demonstrate their effect on improving the accuracy and the precision of the classifier. Our analysis reveals that the quadratic support vector machine classifier achieves an overall accuracy of 93.2% and outperforms the artificial neural network algorithm. Re…
Novel solutions for closed-loop Reverse Electrodialysis: thermodynamic characterisation and perspective analysis
2019
Abstract Closed-loop Reverse Electrodialysis is a novel technology to directly convert low-grade heat into electricity. It consists of a reverse electrodialysis (RED) unit where electricity is produced exploiting the salinity gradient between two salt-water solutions, coupled with a regeneration unit where waste-heat is used to treat the solutions exiting from the RED unit and restore their initial composition. One of the most important advantages of closed-loop systems compared to the open systems is the possibility to select ad-hoc salt solutions to achieve high efficiencies. Therefore, the properties of the salt solutions are essential to assess the performance of the energy generation a…
Combining Benford's Law and machine learning to detect money laundering. An actual Spanish court case.
2017
Abstract Objectives This paper is based on the analysis of the database of operations from a macro-case on money laundering orchestrated between a core company and a group of its suppliers, 26 of which had already been identified by the police as fraudulent companies. In the face of a well-founded suspicion that more companies have perpetrated criminal acts and in order to make better use of what are very limited police resources, we aim to construct a tool to detect money laundering criminals. Methods We combine Benford’s Law and machine learning algorithms (logistic regression, decision trees, neural networks, and random forests) to find patterns of money laundering criminals in the conte…
A Hierarchical Detection and Response System to Enhance Security Against Lethal Cyber-Attacks in UAV Networks
2018
International audience; Unmanned aerial vehicles (UAVs) networks have not yet received considerable research attention. Specifically, security issues are a major concern because such networks, which carry vital information, are prone to various attacks. In this paper, we design and implement a novel intrusion detection and response scheme, which operates at the UAV and ground station levels, to detect malicious anomalies that threaten the network. In this scheme, a set of detection and response techniques are proposed to monitor the UAV behaviors and categorize them into the appropriate list (normal, abnormal, suspect, and malicious) according to the detected cyber-attack. We focus on the m…
FPGA Implementation of an Adaptive Filter Robust to Impulsive Noise: Two Approaches
2011
Adaptive filters are used in a wide range of applications such as echo cancellation, noise cancellation, system identification, and prediction. Its hardware implementation becomes essential in many cases where real-time execution is needed. However, impulsive noise affects the proper operation of the filter and the adaptation process. This noise is one of the most damaging types of signal distortion, not always considered when implementing algorithms, particularly in specific hardware platforms. Field-programmable gate arrays (FPGAs) are used widely for real-time applications where timing requirements are strict. Nowadays, two main design processes can be followed for embedded system design…
Distributed Adaptive Control for Asymptotically Consensus Tracking of Uncertain Nonlinear Systems With Intermittent Actuator Faults and Directed Comm…
2019
In this article, we investigate the output consensus tracking problem for a class of high-order nonlinear systems with unknown parameters, uncertain external disturbances, and intermittent actuator faults. Under the directed topology conditions, a novel distributed adaptive controller is proposed. The common time-varying trajectory is allowed to be totally unknown by part of subsystems. Therefore, the assumption on the linearly parameterized trajectory signal in most literature is no longer needed. To achieve the relaxation, extra distributed parameter estimators are introduced in all subsystems. Besides, to handle the actuator faults occurring at possibly infinite times, a new adaptive com…
Improved Performance of a PV Solar Panel with Adaptive Neuro Fuzzy Inference System ANFIS based MPPT
2018
This article presents the development of an intelligent technique of Adaptive-Neuro-Fuzzy Inference System (ANFIS) based on Maximum Power Point Tracking (ANFIS-MPPT) algorithm with PI controller in order to increase the performances of the photovoltaic panel system below change atmospheric circumstances. In this work, the mathematical principles of the ANFIS method were presented and developed using the software Matlab/Simulink. Moreover, the effectiveness of this ANFIS-MPPT technique is demonstrated by a comparison of the obtained results with others obtained from a classical (Perturb & Observe) P & O-MPPT method.From the analysis of the obtained results, the ANFIS-MPPT command provide bet…