Search results for "Machine"

Real-time flaw detection on complex part: Study of SVM and hyperrectangle based method

2002

We present in this paper the study of two classifications methods used in order to control in real-time some industrials parts. We present the practical frame in which is made the operations, natures of the anomaly to be detected as well as the features extractions method. We tested two techniques of classification, with different algorithm complexities and performances. We compare the results obtained on various features spaces. We end by a combinatorial perspective of results of classification.

CFD Simulation of Particle Distribution in a Multiple-impeller High-Aspect-Ratio Stirred Vessel

2000

Publisher Summary This chapter describes fully predictive simulations of solid–liquid suspensions in a high-aspect-ratio, multiple-impeller stirred tank. These are performed by using the Inner Outer impeller modeling technique coupled with the Multi Fluid Model (MFM) for the treatment of the dispersed phase. The strongly simplified Settling Velocity Model (SVM) is also tested. The effects of free-stream turbulence on the drag coefficient CD and particle settling velocity are accounted for by means of a recently proposed correlation. Comparison of simulation results with experimental particle concentration profiles shows that the MFM approach leads to fair agreement with experimental data. R…

A Review of Kernel Methods in ECG Signal Classification

2011

Kernel methods have been shown to be effective in the analysis of electrocardiogram (ECG) signals. These techniques provide a consistent and well-founded theoretical framework for developing nonlinear algorithms. Kernel methods exhibit useful properties when applied to challenging design scenarios, such as: (1) when dealing with low number of (potentially high dimensional) training samples; (2) in the presence of heterogenous multimodalities; and (3) with different noise sources in the data. These characteristics are particularly appropriate for biomedical signal processing and analysis, and hence, the widespread of these techniques in biomedical signal processing in general, and in ECG dat…

Kernel-based retrieval of atmospheric profiles from IASI data

2011

This paper proposes the use of kernel ridge regression (KRR) to derive surface and atmospheric properties from hyperspectral infrared sounding spectra. We focus on the retrieval of temperature and humidity atmospheric profiles from Infrared Atmospheric Sounding Interferometer (MetOp-IASI) data, and provide confidence maps on the predictions. In addition, we propose a scheme for the identification of anomalies by supervised classification of discrepancies with the ECMWF estimates. For the retrieval, we observed that KRR clearly outperformed linear regression. Looking at the confidence maps, we observed that big discrepancies are mainly due to the presence of clouds and low emissivities in de…

Applications of Kernel Methods

2009

In this chapter, we give a survey of applications of the kernel methods introduced in the previous chapter. We focus on different application domains that are particularly active in both direct application of well-known kernel methods, and in new algorithmic developments suited to a particular problem. In particular, we consider the following application fields: biomedical engineering (comprising both biological signal processing and bioinformatics), communications, signal, speech and image processing.

2014

For locating inaccurate problem of the discrete localization criterion proposed by Demigny, a new criterion expression of “good localization” is proposed. Firstly, a discrete expression of good detection and good localization criterion of two dimension edge detection operator is employed, and then an experiment to measure optimal parameters of two dimension Canny's edge detection operator is introduced after. Moreover, a detailed performance comparison and analysis of two dimension optimal filter obtained via utilizing tensor product for one dimension optimal filter are provided which can prove that least square support vector regression (LS-SVR) is a smoothness filter and give the construc…

An architecture for automatic gesture analysis

2000

The field of human-computer interaction has been widely investigated in the last years, resulting in a variety of systems used in different application fields like virtual reality simulation environments, software user interfaces, and digital library systems.A very crucial part of all these systems is the input module which is devoted to recognize the human operator in terms of tracking and/or recognition of human face, arms position, hand gestures, and so on.In this work a software architecture is presented, for the automatic recognition of human arms poses. Our research has been carried on in the robotics framework. A mobile robot that has to find its path to the goal in a partially struc…

Retrieval of oceanic chlorophyll concentration with relevance vector machines

2006

Abstract In this communication, we evaluate the performance of the relevance vector machine (RVM) for the estimation of biophysical parameters from remote sensing data. For illustration purposes, we focus on the estimation of chlorophyll-a concentrations from remote sensing reflectance just above the ocean surface. A variety of bio-optical algorithms have been developed to relate measurements of ocean radiance to in situ concentrations of phytoplankton pigments, and ultimately most of these algorithms demonstrate the potential of quantifying chlorophyll-a concentrations accurately from multispectral satellite ocean color data. Both satellite-derived data and in situ measurements are subject…

Optimal gossip algorithm for distributed consensus SVM training in wireless sensor networks

2009

In this paper, we consider the distributed training of a SVM using measurements collected by the nodes of aWireless Sensor Network in order to achieve global consensus with the minimum possible inter-node communications for data exchange. We derive a novel mathematical characterization for the optimal selection of partial information that neighboring sensors should exchange in order to achieve consensus in the network. We provide a selection function which ranks the training vectors in order of importance in the learning process. The amount of information exchange can vary, based on an appropriately chosen threshold value of this selection function, providing a desired trade-off between cla…

Multi-dimensional Function Approximation and Regression Estimation

2002

In this communication, we generalize the Support Vector Machines (SVM) for regression estimation and function approximation to multi-dimensional problems. We propose a multi-dimensional Support Vector Regressor (MSVR) that uses a cost function with a hyperspherical insensitive zone, capable of obtaining better predictions than using an SVM independently for each dimension. The resolution of the MSVR is achieved by an iterative procedure over the Karush-Kuhn-Tucker conditions. The proposed algorithm is illustrated by computers experiments.