What is the importance of selecting features for non-technical losses identification?

Although non-technical losses automatic identification has been massively studied, the problem of selecting the most representative features in order to boost the identification accuracy has not attracted much attention in this context. In this paper, we focus on this problem applying a novel feature selection algorithm based on Particle Swarm Optimization and Optimum-Path Forest. The results demonstrated that this method can improve the classification accuracy of possible frauds up to 49% in some datasets composed by industrial and commercial profiles. © 2011 IEEE.

Technical change and technology policy: the case of numerically controlled lathes in Argentina

Includes bibliography

Technical description of the DOCPAL System: aspects relevant to compatibility among population documentation systems

Includes bibliography

Predicting academic success of engineering students in technical drawing from working memory

Tests on spatial aptitude, in particular Visualization, have been shown to be efficient predictors of the academic performance of Technical Drawing stu-dents. It has recently been found that Spatial Working Memory (a construct defined as the ability to perform tasks with a figurative content that require si-multaneous storage and transformation of information) is strongly associated with Visualization. In the present study we analyze the predictive efficiency of a bat-tery of tests that included tests on Visualization, SpatialWorking Memory, Spatial Short-term Memory and Executive Function on a sample of first year engineering students. The results show that Spatial Working Memory (SWM) is the most important predictor of academic success in Technical Drawing. In our view, SWM tests can be useful for detecting as early as possible those students who will require more attention and support in the teaching-learning process.

On the micro-economics of technical progress

Includes bibliography

Notes on technical change induced under conditions of protection, distortions and rationing

Includes bibliography

Identification and feature selection of non-technical losses for industrial consumers using the software WEKA

This work has as objectives the implementation of a intelligent computational tool to identify the non-technical losses and to select its most relevant features, considering information from the database with industrial consumers profiles of a power company. The solution to this problem is not trivial and not of regional character, the minimization of non-technical loss represents the guarantee of investments in product quality and maintenance of power systems, introduced by a competitive environment after the period of privatization in the national scene. This work presents using the WEKA software to the proposed objective, comparing various classification techniques and optimization through intelligent algorithms, this way, can be possible to automate applications on Smart Grids. © 2012 IEEE.

Learning and technical change in the steelplant of Acindar, SA in Rosario, Argentina

Includes bibliography

Capacity-stretching technical change: some empirical and theoretical aspects

Includes bibliography

Technical change and productive restructuring

Includes bibliography

Attitudes to technical change

Includes bibliography

The in-bond assembly industry and technical change

Includes bibliography

The technical skills of information technology workers in Argentina

Includes bibliography

Latin America: incorporating environmental factors into the measurement of production efficiency and technical change

Includes bibliography