Over the years, the power utility relies on a countless number of computer applications to manage their assets, perform calculations of network status, retrieve topological data, store energy exchange data, among others; adding, the fact, that there are new players in the energy market. This has generated the use of different applications in order to share information, either in the same company or outside of it. The construction of an ontology for the energy balance system for the electrical industry is presented as a first step towards achieving reliable communication using global web standards and global standards of the electrical industry. This will allow, in the future, an exchange and consolidation of data of this application using standards of the Semantic Web.
A. Martinez Molina, R. Rodriguez Jorge, R. Villa-Angulo, J. Bila, J. Mizera-Pietraszko, S. Torres Arguelles
219 - 231
An electrocardiogram (ECG) is a non-invasive technique that checks for problems with the electrical activity of a patient's heart. ECG is economical and extremely versatile. Some of its characteristics make it a very useful tool to detect cardiac pathologies. The ECG records a series of characteristic waves called PQRST; however, the QRS complex analysis enables the detection of a type of arrhythmia in an ECG. Technological developments enable the storage of a large amount of data, from which knowledge extraction is impossible without a powerful data processing tool; in particular, an adequate signal processing tool, whose output provides reliable parameters as a basis to make a precise clinical diagnosis. Thus, ECG signal processing creates an opportunity to analyze and recognize possible arrhythmia patterns. This paper reviews the use of artificial neural networks (ANNs) to detect and recognize cardiac arrhythmia patterns. Recurrent neural networks (RNNs) and higher-order neural units are inspected. In addition, the potentials of using higher-order neural units such as the quadratic dynamic neural unit (D-QNU) and dynamic cubic neural unit (D-CNU) for cardiac arrhythmia detection are analyzed.
Ricardo Rodriguez Jorge, Jiri Bila, Jolanta Mizera-Pietraszko, Ricardo Ezequiel Loya Orduño, Edgar Martinez Garcia, Rafael Torres Córdoba
232 - 239
A cubic neural unit is a kind of a higher-order neural unit which can be used for prediction tasks among others, in the medical field. The example of the tasks includes monitoring cardiac behavior in real-time either for preemptive treatment, or for supporting a doctor to reach a more accurate diagnosis. We propose a predictive model which has been developed as an application in open source code with the aim to make it publicly accessible for research community and medical professionals and also to decrease the implementation cost. The proposed model uses sample-by-sample adaptation of the gradient descent method with error back-propagation. This paper presents an application of a cubic neural unit as a prediction mechanism for abnormal cardiac behavior, and it describes a new adaptive methodology based on application of a dynamic cubic neural unit for cardiac arrhythmia prediction. To validate the model, it has been tested on the data from the Massachusetts Institute of Technology-Beth Israel Hospital Cardiac Record Database. This paper is focused on premature ventricular contraction, atrial premature contraction and normal heartbeat records.
Daniel Gerardo Ortega Meza, Onofre Amador Morfín Garduño, Manuel Iván Castellanos García
243 - 256
On this paper a robust decentralized control system at cascade scheme applied to medium capacity wind systems is proposed. On the first closed-loop subsystem, the PMSG-side converter controller is designed for controlling the angular rotor velocity of a permanent magnet synchronous generator to maximize the wind energy capture. On the second closed-loop subsystem, the grid-side converter controller is designed for regulating the link DC-bus voltage located between two converters, and the reactive power to set a unity power factor. The two proposed controllers are based on a combination of the block control linearization technique and SOSM-super-twisting algorithm. Finally, simulation results of the wind systems are reported to validate the performance of the proposed controller having an oscillatory wind speed as input.
In this study, we develop a fuzzy control that has the ability to reduce energy use and uncertainties in crop production by reducing or increasing the temperature in a homemade urban greenhouse by opening a window to two different angles to allow the temperature inside the greenhouse to be equal to the outside temperature. We use hydroponic tomato cultivation as our test case because tomatoes are part of the goods and services category for the “Índice Nacional de Precios al Consumidor” in México, which is an important reference for greenhouse crops and because hydroponics is a technique that saves precious resources, such as water. Fuzzy control allows a person to give instructions to the greenhouse in a natural language. Therefore, we generate input variables to relate the temperature inside the greenhouse to favorable or unfavorable conditions for crop growth and an output variable that allows the control to keep the window closed or to open the window at 45 or 90 degrees. After the mathematical model was refined, it is executed in a GNU Octave environment to generate the temperature values at which the window should react.