Nou sistema per localitzar llocs d'aparcament al carrer

Investigadors de la UAB, de l'empresa WorldSensing i del Centre Tecnològic de Telecomunicacions de Catalunya (CTTC) han desenvolupat un sistema que localitza places d'aparcament lliures al carrer i guia l'usuari fins a la més propera. El sistema, al que han anomenat XALOC, està basat en una nova tecnologia de localització més precisa que el GPS en zones urbanes.Investigadores de la UAB, de la empresa WorldSensing y del CentroTecnológico de Telecomunicaciones de Catalunya (CTTC) han desarrollado un sistema que localiza plazas de aparcamiento libres en la calle y que guía al usuario hasta la más próxima. El sistema, al que han llamado XALOC, está basado en una nueva tecnología de localización más precisa que el GPS en zonas urbanas

A Review of pedestrian indoor positioning systems for mass market applications

In the last decade, the interest in Indoor Location Based Services (ILBS) has increased stimulating the development of Indoor Positioning Systems (IPS). In particular, ILBS look for positioning systems that can be applied anywhere in the world for millions of users, that is, there is a need for developing IPS for mass market applications. Those systems must provide accurate position estimations with minimum infrastructure cost and easy scalability to different environments. This survey overviews the current state of the art of IPSs and classifies them in terms of the infrastructure and methodology employed. Finally, each group is reviewed analysing its advantages and disadvantages and its applicability to mass market applications

Aplicación de la programación en Autolisp en la enseñanza de la geometría. Curvas trocoidales

En este artículo presentamos las posibilidades que, desde un punto de vista didáctico, ofrece la programación en AutoLISP en la enseñanza de la geometría, en particular en el trazado y aplicación de curvas trocoidales. La idea original consistía en realizar un programa que crease un nuevo comando de AutoCAD capaz de trazar cualquier curva trocoidal –tanto particulares (evolventes y cicloides) como no particulares (epitrocoides, hipotrocoides o peritrocoides)–, con independencia de la situación del punto generador (alargadas, acortadas o normales). La elaboración del programa es relativamente simple. Para obtener todos los puntos de la curva es necesario aplicar siempre las mismas propiedades, lo que hace especialmente aconsejable la automatización del proceso. El programa realizado permite seleccionar sobre la pantalla generatriz y directriz, así como el punto que genera la curva. Se nos ofrece, asimismo, establecer el sentido de giro de la ruleta, la precisión de puntos hallados en cada ciclo (tantos como deseemos o como nuestro ordenador sea capaz de soportar), el número de vueltas y, por último, la posibilidad de apreciar, o no, la generación de la trocoide. Esta última opción permite observar, en la pantalla del ordenador, el giro de la generatriz sobre la directriz y la generación progresiva de la curva. Esta pequeña animación –contenida en un comando de AutoCAD– ha convertido un programa originalmente concebido para el trazado de curvas trocoidales, en una herramienta didáctica

A Recurrent Neural Network for Wastewater Treatment Plant Effuents' Prediction

[Abstract] Wastewater Treatment Plants (WWTP) are industries devoted to process water coming from cities' sewer systems and to reduce their contamination. High-pollutant products are generated in the pollutant reduction processes. For this reason, certain limits are established and violations of them are translated into high economic punishments and environmental problems. In this paper data driven methods are performed to monitor the WWTP behaviour. The aim is to predict its effluent concentrations in order to reduce possible violations and their derived costs. To do so, an alarm generation system based on the application of Artificial Neural Networks (ANNs) is proposed. The proposed system shows a good prediction accuracy (errors around 5%) and a reduced miss-detection probability (30%).[Resumen] Las Plantas de tratamiento de aguas residuales (PTAR) son industrias dedicadas a procesar el agua que proviene de los sistemas de alcantarillado de las ciudades y reducir su contaminación. Los productos de alta contaminación se generan en los procesos de reducción de contaminantes. Por esta razón, se establecen ciertos límites y sus violaciones se traducen en castigos económicos elevados y problemas ambientales. En este documento, se realizan métodos controlados por datos para monitorizar el comportamiento de la EDAR. El objetivo es predecir sus concentraciones de efluentes para reducir las posibles violaciones y sus costos derivados. Para ello, se propone un sistema de generación de alarmas basado en la aplicación de Redes Neuronales Artificiales (ANN). El sistema propuesto muestra una buena precisión de predicción (errores en torno al 5%) y una probabilidad de detección errónea reducida (30%).Ministerio de Economía y Empresa; DPI2016-77271-

LSTM-Based Wastewater Treatment Plants Operation Strategies for Effluent Quality Improvement

Wastewater Treatment Plants (WWTPs) are facilities devoted to managing and reducing the pollutant concentrations present in the urban residual waters. Some of them consist in nitrogen and phosphorus derived products which are harmful for the environment. Consequently, certain constraints are applied to pollutant concentrations in order to make sure that treated waters comply with the established regulations. In that sense, efforts have been applied to the development of control strategies that help in the pollutant reduction tasks. Furthermore, the appearance of Artificial Neural Networks (ANNs) has encouraged the adoption of predictive control strategies. In such a fashion, this work is mainly focused on the adoption and development of them to actuate over the pollutant concentrations only when predictions of effluents determine that violations will be produced. In that manner, the overall WWTP's operational costs can be reduced. Predictions are generated by means of an ANN-based Soft-Sensor which adopts Long-Short Term Memory cells to predict effluent pollutant levels. These are the ammonium (S-{NH,e}) and the total nitrogen (S-{Ntot,e}) which are predicted considering influent parameters such as the ammonium concentration at the entrance of the WWTP reactor tanks (S-{NH,po}), the reactors' input flow rate (Q-{po}), the WWTP recirculation rate (Q-{a}) and the environmental temperature (T-{as}). Moreover, this work presents a new multi-objective control scenario which consists in a unique control structure performing the reduction of S-{NH,e} and S-{Ntot,e} concentrations simultaneously. Performance of this new control approach is contrasted with other strategies to determine the improvement provided by the ANN-based Soft-Sensor as well as by the fact of being controlling two pollutants at the same time. Results show that some brief and small violations are still produced. Nevertheless, an improvement in the WWTPs performance w.r.t.The most common control strategies around 96.58% and 98.31% is achieved for S-{NH,e} and S-{Ntot,e}, respectively

Denoising Autoencoders and LSTM-Based Artificial Neural Networks Data Processing for Its Application to Internal Model Control in Industrial Environments-The Wastewater Treatment Plant Control Case

Altres ajuts: Secretaria d'Universitats i Recerca del Departament d'Empresa i Coneixement de la Generalitat de Catalunya i del Fons Social Europeu (2020 FI_B2 000)The evolution of industry towards the Industry 4.0 paradigm has become a reality where different data-driven methods are adopted to support industrial processes. One of them corresponds to Artificial Neural Networks (ANNs), which are able to model highly complex and non-linear processes. This motivates their adoption as part of new data-driven based control strategies. The ANN-based Internal Model Controller (ANN-based IMC) is an example which takes advantage of the ANNs characteristics by modelling the direct and inverse relationships of the process under control with them. This approach has been implemented in Wastewater Treatment Plants (WWTP), where results show a significant improvement on control performance metrics with respect to (w.r.t.) the WWTP default control strategy. However, this structure is very sensible to non-desired effects in the measurements-when a real scenario showing noise-corrupted data is considered, the control performance drops. To solve this, a new ANN-based IMC approach is designed with a two-fold objective, improve the control performance and denoise the noise-corrupted measurements to reduce the performance degradation. Results show that the proposed structure improves the control metrics, (the Integrated Absolute Error (IAE) and the Integrated Squared Error (ISE)), around a 21.25% and a 54.64%, respectively

Transfer learning in wastewater treatment plant control design : from conventional to long short-term memory-based controllers

In the last decade, industrial environments have been experiencing a change in their control processes. It is more frequent that control strategies adopt Artificial Neural Networks (ANNs) to support control operations, or even as the main control structure. Thus, control structures can be directly obtained from input and output measurements without requiring a huge knowledge of the processes under control. However, ANNs have to be designed, implemented, and trained, which can become complex and time-demanding processes. This can be alleviated by means of Transfer Learning (TL) methodologies, where the knowledge obtained from a unique ANN is transferred to the remaining nets reducing the ANN design time. From the control viewpoint, the first ANN can be easily obtained and then transferred to the remaining control loops. In this manuscript, the application of TL methodologies to design and implement the control loops of a Wastewater Treatment Plant (WWTP) is analysed. Results show that the adoption of this TL-based methodology allows the development of new control loops without requiring a huge knowledge of the processes under control. Besides, a wide improvement in terms of the control performance with respect to conventional control structures is also obtained. For instance, results have shown that less oscillations in the tracking of desired set-points are produced by achieving improvements in the Integrated Absolute Error and Integrated Square Error which go from 40.17% to 94.29% and from 34.27% to 99.71%, respectively

Advanced Pedestrian Positioning System to Smartphones and Smartwatches

In recent years, there has been an increasing interest in the development of pedestrian navigation systems for satellite-denied scenarios. The popularization of smartphones and smartwatches is an interesting opportunity for reducing the infrastructure cost of the positioning systems. Nowadays, smartphones include inertial sensors that can be used in pedestrian dead-reckoning (PDR) algorithms for the estimation of the user's position. Both smartphones and smartwatches include WiFi capabilities allowing the computation of the received signal strength (RSS). We develop a new method for the combination of RSS measurements from two different receivers using a Gaussian mixture model. We also analyze the implication of using a WiFi network designed for communication purposes in an indoor positioning system when the designer cannot control the network configuration. In this work, we design a hybrid positioning system that combines inertial measurements, from low-cost inertial sensors embedded in a smartphone, with RSS measurements through an extended Kalman filter. The system has been validated in a real scenario, and results show that our system improves the positioning accuracy of the PDR system thanks to the use of two WiFi receivers. The designed system obtains an accuracy up to 1.4 m in a scenario of 6000 m2

Transfer Learning Improving Predictive Mortality Models for Patients in End-Stage Renal Disease

Deep learning is becoming a fundamental piece in the paradigm shift from evidence-based to data-based medicine. However, its learning capacity is rarely exploited when working with small data sets. Through transfer learning (TL), information from a source domain is transferred to a target one to enhance a learning task in such domain. The proposed TL mechanisms are based on sample and feature space augmentation. Thus, deep autoencoders extract complex representations for the data in the TL approach. Their latent representations, the so-called codes, are handled to transfer information among domains. The transfer of samples is carried out by computing a latent space mapping matrix that links codes from both domains for later reconstruction. The feature space augmentation is based on the computation of the average of the most similar codes from one domain. Such an average augments the features in a target domain. The proposed framework is evaluated in the prediction of mortality in patients in end-stage renal disease, transferring information related to the mortality of patients with acute kidney injury from the massive database MIMIC-III. Compared to other TL mechanisms, the proposed approach improves 6-11% in previous mortality predictive models. The integration of TL approaches into learning tasks in pathologies with data volume issues could encourage the use of data-based medicine in a clinical setting

A reduced complexity approach to IAA beamforming for efficient DOA estimation of coherent sources

Altres ajuts : Chair of Knowledge and Technology Transfer Parc de Recerca UAB-SantanderWe address the 2D direction-of-arrival (DOA) estimation problem in scenarios with coherent sources. More specifically, we adopt beamforming solutions based on the iterative adaptive approach (IAA) recently proposed in the literature. The motivation of such adoption mainly comes from the excellent behavior these beamformers provide in scenarios with coherent sources. Nonetheless, these strategies suffer from a prohibitive computational complexity, especially in 2D scenarios. In order to alleviate the, we propose two reduced-complexity (RC) versions of the IAA and IAA based on maximum likelihood (IAA-ML) algorithms. The proposed beamformers are referred to as IAA-RC and IAA-ML-RC and provide similar results to those obtained with their original counterparts. Computational complexity, however, is further reduced. Numerical results presented in the paper show that the computational burden can be decreased by a 52 with our proposed solutions in the considered scenarios