Search results for " Mach"

PIECEWISE ANOMALY DETECTION USING MINIMAL LEARNING MACHINE FOR HYPERSPECTRAL IMAGES

2021

Abstract. Hyperspectral imaging, with its applications, offers promising tools for remote sensing and Earth observation. Recent development has increased the quality of the sensors. At the same time, the prices of the sensors are lowering. Anomaly detection is one of the popular remote sensing applications, which benefits from real-time solutions. A real-time solution has its limitations, for example, due to a large amount of hyperspectral data, platform’s (drones or a cube satellite) constraints on payload and processing capability. Other examples are the limitations of available energy and the complexity of the machine learning models. When anomalies are detected in real-time from the hyp…

Robust g-filter using support vector method

2004

This Letter presents a new approach to time series modelling using the support vector machines (SVM). Although the g filter can provide stability in several time series models, the SVM is proposed here to provide robustness in the estimation of the g filter coefficients. Examples in chaotic time series prediction and channel equalization show the advantages of the joint SVM g filter. Publicado

Learning non-linear time-scales with kernel -filters

2009

A family of kernel methods, based on the @c-filter structure, is presented for non-linear system identification and time series prediction. The kernel trick allows us to develop the natural non-linear extension of the (linear) support vector machine (SVM) @c-filter [G. Camps-Valls, M. Martinez-Ramon, J.L. Rojo-Alvarez, E. Soria-Olivas, Robust @c-filter using support vector machines, Neurocomput. J. 62(12) (2004) 493-499.], but this approach yields a rigid system model without non-linear cross relation between time-scales. Several functional analysis properties allow us to develop a full, principled family of kernel @c-filters. The improved performance in several application examples suggest…

Robust γ-filter using support vector machines

2009

This Letter presents a new approach to time-series modelling using the support vector machines (SVM). Although the g-filter can provide stability in several time-series models, the SVM is proposed here to provide robustness in the estimation of the g-filter coefficients. Examples in chaotic time-series prediction and channel equalization show the advantages of the joint SVM g-filter. Teoría de la Señal y Comunicaciones

A linear generator for a waveroller power device

2017

In this paper experimental tests on a small scale tubular linear generator to be applied to wave rollers device are presented. A reduced scale of gnerator is presented and experimental results obtained in on test bench are shown. Experimental waveforms of current and voltage are recorded. Simulation results are compared to the experimental ones in order to validate the mathematical models used.

Interior Permanent Magnet Synchronous Machine Drive Powered by Fuel Cell for Automotive Applications

2020

Electric vehicles represent an optimal solution for the reduction of pollution in urban areas. In particular, the Fuel Cell (FC) technology is a promising solution especially for its charging times and zero CO2 direct emissions. The paper addresses the design and performance study of an Interior Permanent Magnet Synchronous Machine (IPMSM) drive powered by fuel cell for automotive applications. The IPMSM drive is powered by the use of 5,5 kW FC unit and it is composed of two DC-DC power converters and one inverter. In detail, a test bench has been carried out for the evaluation of the performances of each IPMSM drive conversion stage. Moreover, in order to simulate automotive working condit…

Revisiting corpus creation and analysis tools for translation tasks

2016

Many translation scholars have proposed the use of corpora to allow professional translators to produce high quality texts which read like originals. Yet, the diffusion of this methodology has been modest, one reason being the fact that software for corpora analyses have been developed with the linguist in mind, which means that they are generally complex and cumbersome, offering many advanced features, but lacking the level of usability and the specific features that meet translators’ needs. To overcome this shortcoming, we have developed TranslatorBank, a free corpus creation and analysis tool designed for translation tasks. TranslatorBank supports the creation of specialized monolingual …

Aspects Concerning SVM Method’s Scalability

2008

In the last years the quantity of text documents is increasing continually and automatic document classification is an important challenge. In the text document classification the training step is essential in obtaining a good classifier. The quality of learning depends on the dimension of the training data. When working with huge learning data sets, problems regarding the training time that increases exponentially are occurring. In this paper we are presenting a method that allows working with huge data sets into the training step without increasing exponentially the training time and without significantly decreasing the classification accuracy.

Non-Linear Hysteretic Instability in Rotating Machinery

2013

On the impact of forgetting on learning machines

1995

People tend not to have perfect memories when it comes to learning, or to anything else for that matter. Most formal studies of learning, however, assume a perfect memory. Some approaches have restricted the number of items that could be retained. We introduce a complexity theoretic accounting of memory utilization by learning machines. In our new model, memory is measured in bits as a function of the size of the input. There is a hierarchy of learnability based on increasing memory allotment. The lower bound results are proved using an unusual combination of pumping and mutual recursion theorem arguments. For technical reasons, it was necessary to consider two types of memory : long and sh…