Application of support vector machine in a traffic lights control

The Application of Least Square Support Vector Machine as a Mathematical Algorithm for Diagnosing Drilling Effectivity in Shaly Formations

The problem of slow drilling in deep shale formations occurs worldwide causing significant expenses to the oil industry. Bit balling which is widely considered as the main cause of poor bit performance in shales, especially deep shales, is being drilled with water-based mud. Therefore, efforts have been made to develop a model to diagnose drilling effectivity. Hence, we arrived at graphical cor...

Application of Support Vector Machine Regression for Predicting Critical Responses of Flexible Pavements

This paper aims to assess the application of Support Vector Machine (SVM) regression in order to analysis flexible pavements. To this end, 10000 Four-layer flexible pavement sections consisted of asphalt concrete layer, granular base layer, granular subbase layer, and subgrade soil were analyzed under the effect of standard axle loading using multi-layered elastic theory and pavement critical r...

Organic Control of Traffic Lights

Simulation Of Traffic Lights Control

Simulation of traffic control is very important nowadays for dealing with increasing urban traffic. It helps to construct the strategy of traffic lights switching and determine alternative path in the case of an accident or traffic jam. In the paper we present the application of simulation tool DYNASIM being used by French company DYNALOGIC. It has big number of alternative possibilities in mod...

High performance of the support vector machine in classifying hyperspectral data using a limited dataset

To prospect mineral deposits at regional scale, recognition and classification of hydrothermal alteration zones using remote sensing data is a popular strategy. Due to the large number of spectral bands, classification of the hyperspectral data may be negatively affected by the Hughes phenomenon. A practical way to handle the Hughes problem is preparing a lot of training samples until the size ...