PurposeThe purpose of this paper is to describe a methodology that has been set up to schedule predictive maintenance of distribution transformers at Cauca Department (Colombia) using machine learning.Design/methodology/approachThe proposed methodology relies on classification predictive model that finds the minimal number of distribution transformers prone to failure. To verify this, the model was implemented and tested with real data in Cauca Department Colombia.FindingsThe implementation of the methodology allows a saving of 13% in corrective maintenance expenses for the year 2020.Originality/valueThe proposed model is an effective decision-making tool that provides an ideal solution for preventive maintenance scheduling problems for distribution transformers.

The aim of this article is to present a statistical comparison of the electric energy expenditure between two techniques for the generation of gait patterns in biped robots. The first one is to minimize the sum of the squared torques applied to the joints of the robot, and the second one is based on the cart-table model. For the experiments, we measured the energy delivered by the battery of the robot to the servomotors. We applied the two aforementioned methods for three velocities (0.5, 1.0, and 1.3 m/min). Additionally, each combination of method and velocity was performed by the robot 10 times. The energy expenditure for each method was compared by applying the Wilcoxon test. In all comparisons, the value of p was lower than 0.004, indicating that the differences were statistically significant. The optimization approach leads to a reduction in energy expenditure that ranged from 9.16 % to 13.35 %. The conclusion is that all the effort required to implement an approach that requires a complete dynamic model of the robot allows a significant reduction in energy consumption.

In this paper we study a generalisation of the Igusa-Todorov functions which gives rise to a vast class of algebras satisfying the finitistic dimension conjecture. This class of algebras is called Lat-Igusa-Todorov and includes, among others, the Igusa-Todorov algebras (defined by J. Wei) and the self-injective algebras which in general are not Igusa-Todorov algebras. Finally, some applications of the developed theory are given in order to relate the different homological dimensions which have been discussed through the paper.

This paper proposes human motion capture to generate movements for the right leg in swing phase of a biped robot restricted to the sagittal plane. Such movements are defined by time functions representing the desired angular positions for the joints involved. Motion capture performed with a Microsoft Kinect TM camera and from the data obtained joint trajectories were generated to control the robot's right leg in swing phase. The proposed control law is a hybrid strategy; the first strategy is based on a computed torque control to track reference trajectories, and the second strategy is based on time scaling control ensuring the robot's balance. This work is a preliminary study to generate humanoid robot trajectories from motion capture.

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