Low-Cost Surface Classification System Supported by Deep Neural Models

Determining the surface on which a vehicle is moving is vital information for im-proving active safety systems. Performing the surface classification or estimating adherence through tire slippage can lead to late action in possible risk situations. Currently, approaches based on image, sound, or vibration analysis are emerging as a viable alternative, though sometimes complex. This work proposes a methodology based on the use of low-cost accelerometers combined with Deep Learning tech-niques. The performance of the proposed system is evaluated with real tests, where high percentages of accuracy are obtained in the classification task.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Modeling of the Influence of Operational Parameters on Tire Lateral Dynamics

Tires play a critical role in vehicle safety. Proper modeling of tire–road interaction is essential for optimal performance of active safety systems. This work studies the influence of temperature, longitudinal vehicle speed, steering frequency, vertical load, and inflation pressure on lateral tire dynamics. To this end, a tire test bench that allows the accurate control of these parameters and the measurement of the variables of interest was used. The obtained results made it possible to propose a simple model that allowed the determination of relaxation length as a function of tire vertical load and vehicle linear speed, and the determination of a representative tread temperature. Additionally, a model has been proposed to determine the lateral friction coefficient from the aforementioned temperature. Finally, results also showed that some variables had little influence on the parameters that characterize lateral dynamicsThis work is partly supported partly by the Spanish Ministry of Science and Innovation under grant PID2019-105572RB-I00, partly by the Economy, Knowledge, Enterprise and Universities Council of the Andalusian Regional Government under grant UMA18-FEDERJA-109, partly by the Spanish Ministry of Education, Culture and Sport under grant FPU18/00450, and partly by the University of Malaga.Partial funding for open access charge: Universidad de Málag

On-line learning applied to spiking neural network for antilock braking systems

Computationally replicating the behaviour of the cerebral cortex to perform the control tasks of daily life in a human being is a challenge today. First, … Finally, a suitable learning model that allows adapting neural network response to changing conditions in the environment is also required. Spiking Neural Networks (SNN) are currently the closest approximation to biological neural networks. SNNs make use of temporal spike trains to deal with inputs and outputs, thus allowing a faster and more complex computation. In this paper, a controller based on an SNN is proposed to perform the control of an anti-lock braking system (ABS) in vehicles. To this end, two neural networks are used to regulate the braking force. The first one is devoted to estimating the optimal slip while the second one is in charge of setting the optimal braking pressure. The latter resembles biological reflex arcs to ensure stability during operation. This neural structure is used to control the fast regulation cycles that occur during ABS operation. Furthermore, an algorithm has been developed to train the network while driving. On-line learning is proposed to update the response of the controller. Hence, to cope with real conditions, a control algorithm based on neural networks that learn by making use of neural plasticity, similar to what occurs in biological systems, has been implemented. Neural connections are modulated using Spike-Timing-Dependent Plasticity (STDP) by means of a supervised learning structure using the slip error as input. Road-type detection has been included in the same neural structure. To validate and to evaluate the performance of the proposed algorithm, simulations as well as experiments in a real vehicle were carried out. The algorithm proved to be able to adapt to changes in adhesion conditions rapidly. This way, the capability of spiking neural networks to perform the full control logic of the ABS has been verified.Funding for open access charge: Universidad de Málaga / CBUA This work was partly supported by the Ministry of Science and Innovation under grant PID2019-105572RB-I00, partly by the Regional Government of Andalusia under grant UMA18-FEDERJA-109, and partly by the University of Malaga as well as the KTH Royal Institute of Technology and its initiative, TRENoP

Análisis y estimación de superficie basada en Mapas Auto-Organizados

En este trabajo se propone la utilización de Mapas Auto-Organizados para llevar a cabo la tarea de clasificación de superficies y estimación de adherencia. Este tipo de redes neuronales se caracteriza por emplear el paradigma del Aprendizaje No Supervisado, logrando un aprendizaje autónomo de las características de los datos que le permitan elaborar la separación de los datos. La información de partida sobre la cual se desarrolla este trabajo es la vibración producida por la rodadura del neumático en distintas superficies. El análisis previo de los datos permite la extracción de características estadísticas sobre las que el SOM zealizará su trabajo. Éstos mapas agrupan conjuntos similares de datos en zonas próximas y además generan una reducción dimensional del problema al mostrarse sobre un plano bidimensional. Este hecho, facilita el análisis de problemas con numerosas variables de entrada al poder trabajarse de manera visual y sencilla. Además, permite la validación de los datos para su uso directo o para ser empleados en otros sistemas en etapas posteriores, así como la inferencia de nueva información.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Practices in Vehicle Engineering: MATLAB programming for processing experimental data in tire tests

[ES] En este trabajo, se pretende involucrar al estudiante en el proceso de medición y procesamiento de los datos en las prácticas de laboratorio de neumáticos en la asignatura de ingeniería de vehículos. El procesamiento de los datos, debido a su complejidad suele omitirse, sin embargo, esta propuesta enseña a los estudiantes a tratar con el ruido de los sensores, el alcance y la precisión de cada medición. Partiendo de una plantilla MATLAB diseñaran métodos de filtrado y procesamiento de datos.[EN] The aim of this work is to involve the student in the measurement and data processing of tire laboratory experiments in the course of vehicle engineering. Data processing, due to its complexity, is often omitted, however, this proposal teaches students to deal with sensor noise, range, and accuracy of each measurement. By using a MATLAB template, they will design data filtering and processing methods.Pérez Fernández, J.; Alcázar Vargas, M.; Sánchez Andrades, I.; Carabias Acosta, E.; Castillo Aguilar, JJ. (2023). Prácticas en Ingeniería de Vehículos: Programación en MATLAB del procesado de datos experimentales en ensayos de neumáticos. Modelling in Science Education and Learning. 16(2):13-19. https://doi.org/10.4995/msel.2023.19078131916

Successful development and clinical translation of a novel anterior lamellar artificial cornea

We thank the Andalusian Public Foundation Progress and Health, through the Andalusian Initiative for Advanced Therapies, for assuming the roles and responsibilities of sponsoring this clinical trial. We thank Dr. Manuel de la Rosa and Dr. Salvador Arias Santiago for providing insight and expertise that assisted the research.The datasets generated and/or analyzed during the current study are available in the Gene Expression Omnibus (GEO) public repository, ref. GSE86584 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE86584Blindness due to corneal diseases is a common pathology affecting up to 23 million individuals worldwide. The tissue‐engineered anterior human cornea, which is currently being tested in a Phase I/II clinical trial to treat severe corneal trophic ulcers with preliminary good feasibility and safety results. This bioartificial cornea is based on a nanostructured fibrin–agarose biomaterial containing human allogeneic stromal keratocytes and cornea epithelial cells, mimicking the human native anterior cornea in terms of optical, mechanical, and biological behavior. This product is manufactured as a clinical‐grade tissue engineering product, fulfilling European requirements and regulations. The clinical translation process included several phases: an initial in vitro and in vivo preclinical research plan, including preclinical advice from the Spanish Medicines Agency followed by additional preclinical development, the adaptation of the biofabrication protocols to a good manufacturing practice manufacturing process, including all quality controls required, and the design of an advanced therapy clinical trial. The experimental development and successful translation of advanced therapy medicinal products for clinical application has to overcome many obstacles, especially when undertaken by academia or SMEs. We expect that our experience and research strategy may help future researchers to efficiently transfer their preclinical results into the clinical settings.This study was supported by the Spanish National Plan for Scientific and Technical Research and Innovation (I + D + I) from the Spanish Ministry of Economy and Competitiveness (Carlos III Institute of Health), grants FIS PI14/0955 and FIS PI17/0391 (both cofinanced by ERDF‐FEDER, European Union); by the Spanish Ministry of Health, Social Policy and Equity, grant EC10‐285; and by preclinical research funds from the Regional Ministry of Health through the Andalusian Initiative for Advanced Therapies

Prácticas en Ingeniería de Vehículos: Programación en MATLAB del procesado de datos experimentales en ensayos de neumáticos.

En ingeniería de vehículos, la experimentación es clave para comprender la interacción del vehículo con el asfalto. El estudio de la dinámica no lineal del contacto es motivo continuo de investigación, donde el enfoque empírico tiene un mayor peso en los modelos matemáticos utilizados en la industria. En este trabajo, se pretende involucrar al estudiante en el proceso de medición y procesamiento de los datos mediante la asistencia al laboratorio o a través de vídeos en su modalidad online. El procesamiento de los datos, debido a su complejidad y a la necesidad de conocimientos previos por parte del estudiante, suele omitirse, proporcionando al estudiante los datos ya procesados. Obviar este paso por parte del estudiante implica la pérdida de información clave para entender la dinámica del vehículo. En esta propuesta, se enseña a los estudiantes a tratar los problemas relacionados con el ruido de los sensores, el alcance y la precisión de cada medición, así como a diseñar métodos de filtrado y procesamiento adecuados para obtener las curvas de comportamiento de los neumáticos utilizadas en la literatura. Las prácticas de laboratorio presentadas en este trabajo, implementadas desde el curso 2019-2020 hasta la actualidad, pretenden mediante la utilización de código MATLAB® que el estudiante se familiarice con el procesado de datos sin necesidad de realizar todo el código. Con la realización de los ensayos el estudiante pasa de familiarizarse con el código y el procesamiento de datos con muy poca interacción en el primer ensayo hasta ser capaz de realizar todo el procesado en el último.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech