Search results for "algorithm"

A motion planning algorithm for the invalid initial state disassembly problem

2015

Sampling-based motion planners are able to plan disassembly paths at high performance. They are limited by the fact that the input triangle sets of the static and dynamic object need to be free of collision in the initial and all following states. In real world applications, like the disassembly planning in car industry, this often does not hold true. Beside data inaccuracy, this is mainly caused by the modeling of flexible parts as rigid bodies, especially fixture elements like clips. They cause the invalid initial state disassembly problem. In the literature there exists no algorithm that is able to calculate a reasonable disassembly path for an invalid initial state. Our novel algorithm …

Estimation of brain connectivity through Artificial Neural Networks

2019

Among different methods available for estimating brain connectivity from electroencephalographic signals (EEG), those based on MVAR models have proved to be flexible and accurate. They rely on the solution of linear equations that can be pursued through artificial neural networks (ANNs) used as MVAR model. However, when few data samples are available, there is a lack of accuracy in estimating MVAR parameters due to the collinearity between regressors. Moreover, the assessment procedure is also affected by the lack of data points. The mathematical solution to these problems is represented by penalized regression methods based on l 1 norm, that can reduce collinearity by means of variable sel…

From the nearest neighbour rule to decision trees

1998

This paper proposes an algorithm to design a tree-like classifier whose result is equivalent to that achieved by the classical Nearest Neighbour rule. The procedure consists of a particular decomposition of a d-dimensional feature space into a set of convex regions with prototypes from just one class. Some experimental results over synthetic and real databases are provided in order to illustrate the applicability of the method.

Comparative Study About Different Experimental Layouts Used on Single Point Incremental Forming Process

2018

Abstract The present paper proposes a comparative study between two of the most used experimental layouts on the single point incremental forming with the advantages and disadvantages of these experimental layouts. After a short presentation of the newest technological opportunities on single point incremental forming, the paper presents a classification of the experimental layouts used on this kind of forming process. The comparative study highlights the advantages and the disadvantages of using the universal milling machines and the industrial robots on single point incremental forming. There are presented the results focused on thinning and forces in the SPIF process.

Towards an Efficient Implementation of an Accurate SPH Method

2020

A modified version of the Smoothed Particle Hydrodynamics (SPH) method is considered in order to overcome the loss of accuracy of the standard formulation. The summation of Gaussian kernel functions is employed, using the Improved Fast Gauss Transform (IFGT) to reduce the computational cost, while tuning the desired accuracy in the SPH method. This technique, coupled with an algorithmic design for exploiting the performance of Graphics Processing Units (GPUs), makes the method promising, as shown by numerical experiments.

Enhancing the Resolution of the Spectrogram of Non-Stationary Mobile Radio Channels by Using Massive MIMO Techniques

2017

This paper is concerned with the enhancement of the resolution of the spectrogram of non-stationary mobile radio channels using massive multiple-input multiple-output (MIMO) techniques. By starting from a new generic geometrical model for a non-stationary MIMO channel, we derive the complex MIMO channel gains under the assumption that the mobile station (MS) moves with time-variant speed. Closed-form solutions are derived for the spectrogram of the complex MIMO channel gains by using a Gaussian window. It is shown that the window spread can be optimized subject to the MS's speed change. Furthermore, it is shown that the spectrogram can be split into an auto-term and a cross-term. The auto-t…

Experimental validation for spectrum cartography using adaptive multi-kernels

2017

This paper validates the functionality of an algorithm for spectrum cartography, generating a radio environment map (REM) using adaptive radial basis functions (RBF) based on a limited number of measurements. The power at all locations is estimated as a linear combination of different RBFs without assuming any prior information about either power spectral densities (PSD) of the transmitters or their locations. The RBFs are represented as centroids at optimized locations, using machine learning to jointly optimize their positions, weights and Gaussian decaying parameters. Optimization is performed using expectation maximization with a least squares loss function and a quadratic regularizer. …

COMMENT ON THE SPACE TRANSLATION METHOD AS USED IN LASER-ASSISTED COLLISION PROBLEMS

1982

Krill herd algorithm-based neural network in structural seismic reliability evaluation

2018

ABSTRACTIn this research work, the relative displacement of the stories has been determined by means of a feedforward Artificial Neural Network (ANN) model, which employs one of the novel methods for the optimization of the artificial neural network weights, namely the krill herd algorithm. For the purpose of this work, the area, elasticity, and load parameters were the input parameters and the relative displacement of the stories was the output parameter. To assess the precision of the feedforward (FF) model optimized using the Krill Herd Optimization (FF-KH) algorithm, comparison of results has been performed relative to the results obtained by the linear regression model, the Genetic Alg…

Advanced computation in cardiovascular physiology: New challenges and opportunities

2021

Recent developments in computational physiology have successfully exploited advanced signal processing and artificial intelligence tools for predicting or uncovering characteristic features of physiological and pathological states in humans. While these advanced tools have demonstrated excellent diagnostic capabilities, the high complexity of these computational 'black boxes’ may severely limit scientific inference, especially in terms of biological insight about both physiology and pathological aberrations. This theme issue highlights current challenges and opportunities of advanced computational tools for processing dynamical data reflecting autonomic nervous system dynamics, with a speci…