An essential aspect to achieving safety with a UAV is that it operates within the limits of its capabilities, the available flight time being a key aspect when planning and executing a mission. The flight time will depend on the relationship between the available energy and the energy required by the UAV to complete the mission. This paper addresses the problem of estimating the energy required to perform a mission, for which a fuzzy Takagi–Sugeno system was implemented, whose premises were developed using fuzzy C-means to estimate the power required in the different stages of the mission. The parameters used in the fuzzy C-means algorithm were optimized using particle swarm optimization. On the other hand, an equivalent circuit model of a battery was used, for which fuzzy modeling was employed to determine the relationship between the open-circuit voltage and the state of charge of the battery, which in conjunction with an extended Kalman filter allows determining the battery charge. In addition, we developed a methodology to determine the minimum allowable battery charge level. From this, it is possible to determine the available flight time at the end of a mission defined as the flight time margin. In order to evaluate the developed methodology, a physical experiment was performed using an hexarotor UAV obtaining a maximum prediction error equivalent to the energy required to operate for 7 s, which corresponds to 2% of the total mission time.

An advanced metering infrastructure (AMI) system has been designed to measure electrical parameters and transmit them to a database (DB) on the smart grid (SG) concept. The present system allows remote monitoring of electrical parameters for a single-phase residential service, able to automatically correct the power factor (PF) and upload information to a DB. The systems performance has been tested by calculating non-homogeneous parameters of ten thousand residential users to simulate its massive operation. The geographical coverage of more than two thousand users clustered as a private wireless network can be ensured in line-of-sight. It has been estimated the reduction of average current consumption around 29%. Collected data in the DB can be used for modeling and optimization purposes in electricity generation and distribution according to demand-side management techniques.

This article studies the most common methods of autonomous vehicle guidance, as well as a review of companies that have already developed their own self-guided tractors. The most common methods for autonomous guidance are through the position obtained from an RTK-GPS and by artificial vision. For both cases, sensors have to be implemented that help the task assigned, such as LIDAR sensors, proximity sensors, among others, that detect the tractor to know the working environment and define the correct route and even avoid collision, to comply with the assigned task, managing to improve production time and quality. The two guidance methods mentioned in this work fail to have error = 0, either by the type of sensor or by the technique used. Among the most commonly used techniques for obstacle avoidance are the possible field method or the probabilistic route planner.

Hypertensive disorders in pregnancy, which include preeclampsia, eclampsia, and chronic hypertension, complicate approximately 10% of all pregnancies in the world, constituting one of the most serious causes of mortality and morbidity in gestation. To help predict the occurrence of hypertensive disorders, a study based on algorithms that help model this health problem using mathematical tools is proposed. This study proposes a fuzzy c-means (FCM) model based on the Takagi–Sugeno (T-S) type of fuzzy rule to predict hypertensive disorders in pregnancy. To test different modeling methodologies, cross-validation comparisons were made between random forest, decision tree, support vector machine, and T-S and FCM methods, which achieved 80.00%, 66.25%, 70.00%, and 90.00%, respectively. The evaluation consisted of calculating the true positive rate (TPR) over the true negative rate (TNR), with equal error rate (EER) curves achieving a percentage of 20%. The learning dataset consisted of a total of 371 pregnant women, of which 13.2% were diagnosed with a condition related to gestational hypertension. The dataset for this study was obtained from the Secretaría de Salud del Estado de Hidalgo (SSEH), México. A random sub-sampling technique was used to adjust the class distribution of the data set, and to eliminate the problem of unbalanced classes. The models were trained using a total of 98 samples. The modeling results indicate that the T-S and FCM method has a higher predictive ability than the other three models in this research.

En el presente artículo se muestra un esquema de identificación y control que sintoniza en línea las ganancias proporcional, integral y derivativa de un controlador PID discreto aplicado a un sistema dinámico SISO. Esto se logra empleando una red neuronal de base radial con funciones de activación wavelet hijas Morlet (wavenet) adicionalmente en cascada un filtro de respuesta infinita al impulso (IIR). Dicho esquema es aplicado en tiempo real para controlar la velocidad de un motor de inducción de CA trifásico del tipo jaula de ardilla (MIJA) alimentado con un variador de frecuencia trifásico, de esta forma se muestra cómo este esquema de identificación y control en línea, puede ser implementado en este tipo de plantas que son ampliamente utilizadas en la industria, sin la necesidad de obtener los parámetros del modelo matemático del conjunto variador de frecuencia-motor de inducción trifásico. Se presentan los resultados obtenidos en simulación numérica y experimentales, empleando para esto la plataforma de LabVIEW.