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Bivariate failure data are common in reliability and survival studies, where estimation of dependency is often an important step in data analysis. In the literature, it known that the correlation coefficients measure the linear relationship between two variables, but strong non-linear relationship can also exist between them. Kendall's T concordance coefficient has become a useful tool for bivariate data analysis, which is used in nonparametric tests of independence and as a complementary measures of association. In the analysis of reliability data, there is a phenomenon that occurs when the value of the lifetime is partially known, which is known as censoring. In this paper, two estimation methods of Kendall's t are compared via simulation, one of them assuming normality in marginal distributions and adjusting them individually and the other based on copulas (Gaussian and Clayton), where the bivariate data are interval censored. The comparison is made using the mean squared error and the median absolute deviation. The results show that the method based on the copula approximation generally produces more precise estimates than the method of individual adjustment of the marginals. [ABSTRACT FROM AUTHOR]

This paper proposes several techniques for simultaneous localization and mapping of the environment, which allow mobile robots to self-reference themselves in a navigation environment with reduced accessibility by external means, such as the Global Position System (GPS). The methodology consists of implementing four unsupervised machine learning algorithms, using data sets generated based on a cloud of range points delivered by LiDAR sensor measurements. The proposed approach identifies characteristics from a navigability map, whereas an additional method based on Extended Kalman Filter (EKF) allows to find the robot positioning in conjunction. The proposed approach identifies navigability map features and an additional method based on EKF. EKF allows finding the robot positioning that is conjugated with each of the proposed algorithms. The first proposed method consists of estimating the features of the environment using heuristic methods and shaping the map using geometric principles. The second method is based on K-Means to incorporate the uncertainty in the sensor measurement, while the third solution uses the Gaussian Mixture Model (GMM). The fourth method focuses on Density-Based Spatial Clustering (DBSCAN). An odometry error is induced in the robot to include uncertainty within the test environment, which propagates into the positioning readings. The results show that DBSCAN presents a better execution time for the proposed localization system than the other comparative methods. Additionally, the robot's localization is more accurate with this method, showing a 5% reduction of the error compared to the result obtained from the other proposed algorithms. Finally, with the results achieved, it is expected that the consumption of robot resources can be reduced with the reduction of localization error and automatic mapping. [ABSTRACT FROM AUTHOR]

Species identification from timber helps combat ilegal wood trafficking. In the present work, it is shown a method that aims to detect wood species from previously cut and stored pieces through the interaction of the volatile compounds that they emanate with an array of chemical sensors (electronic nose). The device processes the response of the chemical sensor array using linear regularization and probabilistic machine learning methods trying to resemble current biometric systems. In particular, this method includes a heuristic parameter estimation stage on the signals provided by the sensor array, followed by a variable selection stage through L¹ regularization, to finally use Gaussian mixture models [GMMs] for the process of probabilistic modeling. As a result, a performance (measured by EER) of 17.5 % is obtained for the detection of four wood species; and, for the particular case of Sapán, an EER of 12 % is achieved. In conclusion, this biometric approach gives good results compared to previous works, taking into account that in the present work, the experiments are carried out in conditions that are closer to reality. [ABSTRACT FROM AUTHOR]

This systematic review is based on the PRISMA statement. Studies of the last 10 years were considered, in which the usefulness of mathematical models to study and predict the behavior of the dispersion of atmospheric pollutants is focused. The following databases were used; ScienceDirect, Dialnet, Scielo, ScienceResearch, PubMed and Redalyc, obtaining a total of 4,544 records, discarding 61 of these due to duplicity, finally considering 4,483 records, 3,429 were excluded, subsequently, of the 1,054 articles chosen, 991 were excluded because they were not related with dispersion models. Finally, 63 original articles were included for analysis and preparation of the systematic review. Model selection depends on weather, scenario, emission source, and atmospheric stability. The importance of pollution control leads us to the use of models that allow us to forecast the concentration in the dispersion of atmospheric pollutants. These can be theoretical or semi-theoretical, and to the extent that the parameters are included in the model adequate in such a way that it is possible to approach reality, the results, shown, will be very reliable, but this leads to a very complex model. The most used models are Gaussian and Lagrangian models. [ABSTRACT FROM AUTHOR]

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