Prediction of PM10 concentrations using Fuzzy c-Means and ANN

Salamanca has been considered among the most polluted cities in Mexico. The vehicular park, the industry and the emissions produced by agriculture, as well as orography and climatic characteristics have propitiated the increment in pollutant concentration of Particulate Matter less than 10 μg/m3 in diameter (PM10). In this work, a Multilayer Perceptron Neural Network has been used to make the prediction of an hour ahead of pollutant concentration. A database used to train the Neural Network corresponds to historical time series of meteorological variables (wind speed, wind direction, temperature and relative humidity) and air pollutant concentrations of PM10. Before the prediction, Fuzzy c-Means clustering algorithm have been implemented in order to find relationship among pollutant and meteorological variables. These relationship help us to get additional information that will be used for predicting. Our experiments with the proposed system show the importance of this set of meteorological variables on the prediction of PM10 pollutant concentrations and the neural network efficiency. The performance estimation is determined using the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The results shown that the information obtained in the clustering step allows a prediction of an hour ahead, with data from past 2 hours

Microcalcifications Detection using PFCM and ANN

This work presents a method to detect Microcalcifications in Regions of Interest from digitized mammograms. The method is based mainly on the combination of Image Processing, Pattern Recognition and Artificial Intelligence. The Top-Hat transform is a technique based on mathematical morphology operations that, in this work is used to perform contrast enhancement of microcalcifications in the region of interest. In order to find more or less homogeneous regions in the image, we apply a novel image sub-segmentation technique based on Possibilistic Fuzzy c-Means clustering algorithm. From the original region of interest we extract two window-based features, Mean and Deviation Standard, which will be used in a classifier based on a Artificial Neural Network in order to identify microcalcifications. Our results show that the proposed method is a good alternative in the stage of microcalcifications detection, because this stage is an important part of the early Breast Cancer detection

Inverse kinematics of a 6 DoF human upper limb using ANFIS and ANN for anticipatory actuation in ADL-based physical Neurorehabilitation

Objective: This research is focused in the creation and validation of a solution to the inverse kinematics problem for a 6 degrees of freedom human upper limb. This system is intended to work within a realtime dysfunctional motion prediction system that allows anticipatory actuation in physical Neurorehabilitation under the assisted-as-needed paradigm. For this purpose, a multilayer perceptron-based and an ANFIS-based solution to the inverse kinematics problem are evaluated. Materials and methods: Both the multilayer perceptron-based and the ANFIS-based inverse kinematics methods have been trained with three-dimensional Cartesian positions corresponding to the end-effector of healthy human upper limbs that execute two different activities of the daily life: "serving water from a jar" and "picking up a bottle". Validation of the proposed methodologies has been performed by a 10 fold cross-validation procedure. Results: Once trained, the systems are able to map 3D positions of the end-effector to the corresponding healthy biomechanical configurations. A high mean correlation coefficient and a low root mean squared error have been found for both the multilayer perceptron and ANFIS-based methods. Conclusions: The obtained results indicate that both systems effectively solve the inverse kinematics problem, but, due to its low computational load, crucial in real-time applications, along with its high performance, a multilayer perceptron-based solution, consisting in 3 input neurons, 1 hidden layer with 3 neurons and 6 output neurons has been considered the most appropriated for the target application.

ANN and Fuzzy c-Means applied to environmental pollution prediction

Salamanca, situated in center of Mexico is among the cities which suffer most from the air pollution in Mexico. The vehicular park and the industry, as well as orography and climatic characteristics have propitiated the increment in pollutant concentration of Sulphur Dioxide (SO2). In this work, a Multilayer Perceptron Neural Network has been used to make the prediction of an hour ahead of pollutant concentration. A database used to train the Neural Network corresponds to historical time series of meteorological variables and air pollutant concentrations of SO2. Before the prediction, Fuzzy c-Means and K-means clustering algorithms have been implemented in order to find relationship among pollutant and meteorological variables. Our experiments with the proposed system show the importance of this set of meteorological variables on the prediction of SO2 pollutant concentrations and the neural network efficiency. The performance estimation is determined using the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The results showed that the information obtained in the clustering step allows a prediction of an hour ahead, with data from past 2 hours.

Diseño del equipamiento de un estudio de grabación digital

El presente proyecto describe la instalación de audio de un estudio de grabación digital musical. La finalidad de este proyecto es puramente educativa, afianzando conceptos que se han contemplado durante la carrera. La instalación tiene carácter ficticio, por lo que no tiene implementación real. Aun así, se ha intentado desarrollar con carácter profesional. El proyecto se ha dividido en varias fases de trabajo. Primeramente, se procedió a la búsqueda de información relativa a estudios de grabación, atendiendo principalmente a sus configuraciones. Paralelamente, se buscó información sobre los principales equipos dentro de un estudio de grabación y realizando un pequeño estudio de mercado. Posteriormente, se ha procedido a la elección de la configuración del equipamiento del estudio, atendiendo a las ventajas e inconvenientes de cada tipo de configuración. La tercera fase, corresponde a la elección de los equipos. Siguiendo la cadena de audio, se ha ido analizando la necesidad de cada uno de ellos. Seguidamente, se ha realizado una comparación de diferentes equipos que componen cada bloque de elección, y finalmente la selección del más apropiado junto con su justificación. En la última fase se ha realizado la interconexión de todos los equipos atendiendo a la configuración elegida en la segunda fase. Para ello, se ha llevado a cabo la implementación de una serie de tablas escritas, donde se especifica cada tipo de conexión. El proyecto ha terminado con una presentación del presupuesto, dividido en varios aparatados, y el desarrollo de las conclusiones. En ellas, se ha analizado tanto los objetivos propuestos al principio del proyecto como una valoración personal del proyecto en general. ABSTRACT. This project describes the audio installation of a digital music recording studio. The purpose of this project is purely educational, strengthening concepts that have been laid during college. The installation is fictitious and has not been implemented in a real situation. Nevertheless, it has been developed with a professional character. This project has been divided in various phases. Firstly, I proceeded to search information related to recording studios, focusing specially on their configurations. Simultaneously, I looked for information about the main digital equipment of a recording studio and performed a brief market research. Secondly, I selected the studio equipment configuration, taking care of the advantages and disadvantages of each type of configuration. The third phase corresponds to the choice of the equipment. Following the audio chain, I analyzed the need for each of them. Then, I compared the different equipment that compose each of the choice blocks and finally opt for the most appropriate with its justification. In the last phase, I interconnected all the equipment according to the chosen configuration of the second phase. For this, I implemented a series of written tables, where I specified each connection type. The Project ends with a presentation of the budget, divided into several sections, followed by the conclusion in which I analyze both the objectives of the project and my personal valuation.

Analysis of the discharge capacity of radial-gated spillways using CFD and ANN - Oliana Dam case study.

The paper focuses on the analysis of radial-gated spillways, which is carried out by the solution of a numerical model based on the finite element method (FEM). The Oliana Dam is considered as a case study and the discharge capacity is predicted both by the application of a level-set-based free-surface solver and by the use of traditional empirical formulations. The results of the analysis are then used for training an artificial neural network to allow real-time predictions of the discharge in any situation of energy head and gate opening within the operation range of the reservoir. The comparison of the results obtained with the different methods shows that numerical models such as the FEM can be useful as a predictive tool for the analysis of the hydraulic performance of radial-gated spillways.