Hybrid multiscale integration for directionally scale separable problems

2017 ◽  
Vol 113 (12) ◽  
pp. 1755-1778 ◽  
Author(s):  
Shuhai Zhang ◽  
Caglar Oskay
Keyword(s):  
Separable Problems

Recognition of Moving Tracked and Wheeled Vehicles Based on Sound Analysis and Machine Learning Algorithms

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Jacek Jakubowski ◽  
Jerzy Jackowski
Keyword(s):  
Linear Prediction ◽  
Machine Learning Algorithms ◽  
Spectral Density Estimation ◽  
Power Spectral ◽  
Separable Problems ◽  
Characteristic Features ◽  
Iterative Procedures ◽  
Linear Prediction Coding ◽  
Signal Processing Methods ◽  
Wheeled Vehicles

The paper presents results of a preliminary study on verification of the possibility to establish simple methods to process acquired sound signals that were generated by a vehicle in motion; to determine its characteristic features for classification as a wheeled or tracked one. The analysis covered 220 signals acquired from real experiment and pre-processed with the use of power spectral density estimation (PSD) and linear prediction coding (LPC). The signal processing methods were used to generate features for which applicability in the classification process was assessed using a statistical method. The set of features was then optimised to reduce the dimensionality of data. Results of recognition obtained with the proposed non-iterative procedures for solving linearly separable problems were compared with results from standard methods, including SVM and k-NN. The developed features as well as selected methods of classification were proposed with respect to the possibility to implement them in low computational power computers for embedded applications.

Numerical experiments on stochastic block proximal-gradient type method for convex constrained optimization involving coordinatewise separable problems

2019 ◽  
Vol 35 (3) ◽  
pp. 371-378
Author(s):  
PORNTIP PROMSINCHAI ◽  
NARIN PETROT ◽  
◽  
◽  
Keyword(s):  
Constrained Optimization ◽  
Optimization Problems ◽  
Gradient Algorithm ◽  
Objective Functions ◽  
Constrained Optimization Problems ◽  
Type Method ◽  
Coordinate Descent Algorithm ◽  
Proximal Gradient Algorithm ◽  
Separable Problems ◽  
Gradient Type

In this paper, we consider convex constrained optimization problems with composite objective functions over the set of a minimizer of another function. The main aim is to test numerically a new algorithm, namely a stochastic block coordinate proximal-gradient algorithm with penalization, by comparing both the number of iterations and CPU times between this introduced algorithm and the other well-known types of block coordinate descent algorithm for finding solutions of the randomly generated optimization problems with regularization term.

A MODIFIED KOHONEN SELF-ORGANIZING MAP (KSOM) CLUSTERING FOR FOUR CATEGORICAL DATA

2016 ◽  
Vol 78 (6-13) ◽  
Author(s):  
Azlin Ahmad ◽  
Rubiyah Yusof
Keyword(s):  
Breast Cancer ◽  
Categorical Data ◽  
Data Sets ◽  
Complex Data ◽  
Self Organizing Map ◽  
Distance Calculation ◽  
The Neural Network ◽  
Complex Data Sets ◽  
Separable Problems ◽  
Self Organizing

The Kohonen Self-Organizing Map (KSOM) is one of the Neural Network unsupervised learning algorithms. This algorithm is used in solving problems in various areas, especially in clustering complex data sets. Despite its advantages, the KSOM algorithm has a few drawbacks; such as overlapped cluster and non-linear separable problems. Therefore, this paper proposes a modified KSOM that inspired from pheromone approach in Ant Colony Optimization. The modification is focusing on the distance calculation amongst objects. The proposed algorithm has been tested on four real categorical data that are obtained from UCI machine learning repository; Iris, Seeds, Glass and Wisconsin Breast Cancer Database. From the results, it shows that the modified KSOM has produced accurate clustering result and all clusters can clearly be identified.

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