599 research outputs found
Floating car data augmentation based on infrastructure sensors and neural networks
The development of new-generation intelligent vehicle technologies will lead to a better level of road safety and CO2 emission reductions. However, the weak point of all these systems is their need for comprehensive and reliable data. For traffic data acquisition, two sources are currently available: 1) infrastructure sensors and 2) floating vehicles. The former consists of a set of fixed point detectors installed in the roads, and the latter consists of the use of mobile probe vehicles as mobile sensors. However, both systems still have some deficiencies. The infrastructure sensors retrieve information fromstatic points of the road, which are spaced, in some cases, kilometers apart. This means that the picture of the actual traffic situation is not a real one. This deficiency is corrected by floating cars, which retrieve dynamic information on the traffic situation. Unfortunately, the number of floating data vehicles currently available is too small and insufficient to give a complete picture of the road traffic. In this paper, we present a floating car data (FCD) augmentation system that combines information fromfloating data vehicles and infrastructure sensors, and that, by using neural networks, is capable of incrementing the amount of FCD with virtual information. This system has been implemented and tested on actual roads, and the results show little difference between the data supplied by the floating vehicles and the virtual vehicles
Deep ensemble multitask classification of emergency medical call incidents combining multimodal data improves emergency medical dispatch
[EN] The objective of this work was to develop a predictive model to aid non-clinical dispatchers to classify emergency medical call incidents by their life-threatening level (yes/no), admissible response delay (undelayable, minutes, hours, days) and emergency system jurisdiction (emergency system/primary care) in real time. We used a total of 1 244 624 independent incidents from the Valencian emergency medical dispatch service in Spain, compiled in retrospective from 2009 to 2012, including clinical features, demographics, circumstantial factors and free text dispatcher observations. Based on them, we designed and developed DeepEMC2, a deep ensemble multitask model integrating four subnetworks: three specialized to context, clinical and text data, respectively, and another to ensemble the former. The four subnetworks are composed in turn by multi-layer perceptron modules, bidirectional long short-term memory units and a bidirectional encoding representations from transformers module. DeepEMC2 showed a macro F1-score of 0.759 in life-threatening classification, 0.576 in admissible response delay and 0.757 in emergency system jurisdiction. These results show a substantial performance increase of 12.5 %, 17.5 % and 5.1 %, respectively, with respect to the current in-house triage protocol of the Valencian emergency medical dispatch service. Besides, DeepEMC2 significantly outperformed a set of baseline machine learning models, including naive bayes, logistic regression, random forest and gradient boosting (¿ = 0.05). Hence, DeepEMC2 is able to: 1) capture information present in emergency medical calls not considered by the existing triage protocol, and 2) model complex data dependencies not feasible by the tested baseline models. Likewise, our results suggest that most of this unconsidered information is present in the free text dispatcher observations. To our knowledge, this study describes the first deep learning model undertaking emergency medical call incidents classification. Its adoption in medical dispatch centers would potentially improve emergency dispatch processes, resulting in a positive impact in patient wellbeing and health services sustainability.This work has been supported by the Valencian agency for security and emergency response project A1800173041, the Ministry of Science, Innovation and Universities of Spain program FPU18/06441 and the EU Horizon 2020 project InAdvance 825750Ferri-Borredà, P.; Sáez Silvestre, C.; Felix-De Castro, A.; Juan-Albarracín, J.; Blanes-Selva, V.; Sánchez-Cuesta, P.; Garcia-Gomez, JM. (2021). Deep ensemble multitask classification of emergency medical call incidents combining multimodal data improves emergency medical dispatch. Artificial Intelligence in Medicine. 117:1-13. https://doi.org/10.1016/j.artmed.2021.102088S11311
Terrain classification using machine learning algorithms in a multi-temporal approach A QGIS plug-in implementation
Land cover and land use (LCLU) maps are essential for the successful administration
of a nation’s topography, however, conventional on-site data gathering methods are costly
and time-consuming. By contrast, remote sensing data can be used to generate up-to-date
maps regularly with the help of machine learning algorithms, in turn, allowing for the
assessment of a region’s dynamics throughout time.
The present dissertation will focus on the implementation of an automated land
use and land cover classifier based on remote sensing imagery provided by the mod ern sentinel-2 satellite constellation. The project, with Portugal at its focus, will expand
on previous approaches by utilizing temporal data as an input variable in order to harvest
the contextual information contained in the vegetation cycles.
The pursued solution investigated the implementation of a 9-class classifier plug-in
for an industry standard, open-source geographic information system. In the course of
the testing procedure, various processing techniques and machine learning algorithms
were evaluated in a multi-temporal approach. Resulting in a final overall accuracy of
65,9% across the targeted classes.Mapas de uso e ocupação do solo são cruciais para o entendimento e administração
da topografia de uma nação, no entanto, os métodos convencionais de aquisição local de
dados são caros e demorados. Contrariamente, dados provenientes de métodos de senso riamento remoto podem ser utilizados para gerar regularmente mapas atualizados com
a ajuda de algoritmos de aprendizagem automática. Permitindo, por sua vez, a avaliação
da dinâmica de uma região ao longo do tempo.
Utilizando como base imagens de sensoriamento remoto fornecidas pela recente cons telação de satélites Sentinel-2, a presente dissertação concentra-se na implementação de
um classificador de mapas de uso e ocupação do solo automatizado. O projeto, com foco
em Portugal, irá procurar expandir abordagens anteriores através do aproveitamento de
informação contextual contida nos ciclos vegetativos pela utilização de dados temporais
adicionais.
A solução adotada investigou a produção e implementação de um classificador geral
de 9 classes num plug-in de um sistema de informação geográfico de código aberto.
Durante o processo de teste, diversas técnicas de processamento e múltiplos algoritmos de
aprendizagem automática foram avaliados numa abordagem multi-temporal, culminando
num resultado final de precisão geral de 65,9% nas classes avaliadas
A Tool for Fast Development of Modular and Hierarchic Neural Network-based Systems
This paper presents PyramidNet tool as a fast and easy way to develop Modular and Hierarchic NeuralNetwork-based Systems. This tool facilitates the fast emergence of autonomous behaviors in agents becauseit uses a hierarchic and modular control methodology of heterogeneous learning modules: the pyramid.Using the graphical resources of PyramidNet the user is able to specify a behavior system even having little understanding of artificial neural networks. Experimental tests have shown that a very significant speedup is attained in the development of modular and hierarchic neural network-based systems by using this tool
Towards the improvement of machine learning peak runoff forecasting by exploiting ground- and satellite-based precipitation data: A feature engineering approach
La predicción de picos de caudal en sistemas montañosos complejos presenta desafíos en
hidrología debido a la falta de datos y las limitaciones de los modelos físicos. El aprendizaje
automático (ML) ofrece una solución al permitir la integración de técnicas y productos satelitales
de precipitación (SPPs). Sin embargo, se ha debatido sobre la efectividad del ML debido a su
naturaleza de "caja negra" que dificulta la mejora del rendimiento y la reproducibilidad de los
resultados. Para abordar estas preocupaciones, se han propuesto estrategias de ingeniería de
características (FE) para incorporar conocimiento físico en los modelos de ML, mejorando la
comprensión y precisión de las predicciones. Esta investigación doctoral tiene como objetivo
mejorar la predicción de picos de caudal mediante la integración de conceptos hidrológicos a
través de técnicas de FE y el uso de datos de precipitación in-situ y SPPs. Se exploran técnicas
y estrategias de ML para mejorar la precisión en sistemas hidrológicos macro y mesoescala.
Además, se propone una estrategia de FE para aprovechar la información de SPPs y superar la
escasez de datos espaciales y temporales. La integración de técnicas avanzadas de ML y FE
representa un avance en hidrología, especialmente para sistemas montañosos complejos con
limitada o nula red de monitoreo. Los hallazgos de este estudio serán valiosos para tomadores
de decisiones e hidrólogos, facilitando la mitigación de los impactos de los picos de caudal.
Además, las metodologías desarrolladas se pueden adaptar a otros sistemas de macro y
mesoescala, beneficiando a la comunidad científica en general.Peak runoff forecasting in complex mountain systems poses significant challenges in hydrology
due to limitations in traditional physically-based models and data scarcity. However, the
integration of machine learning (ML) techniques offers a promising solution by balancing
computational efficiency and enabling the incorporation of satellite precipitation products (SPPs).
However, debates have emerged regarding the effectiveness of ML in hydrology, as its black-box
nature lacks explicit representation of hydrological processes, hindering performance
improvement and result reproducibility. To address these concerns, recent studies emphasize the
inclusion of FE strategies to incorporate physical knowledge into ML models, enabling a better
understanding of the system and improved forecasting accuracy. This doctoral research aims to
enhance the effectiveness of ML in peak runoff forecasting by integrating hydrological concepts
through FE techniques, utilizing both ground-based and satellite-based precipitation data. For
this, we explore ML techniques and strategies to enhance accuracy in complex macro- and mesoscale
hydrological systems.
Additionally, we propose a FE strategy for a proper utilization of SPP information which is crucial for overcoming spatial and temporal data scarcity.
The integration of advanced ML techniques and FE represents a significant advancement in hydrology,
particularly for complex mountain systems with limited or inexistent monitoring networks.
The findings of this study will provide valuable insights for decision-makers and hydrologists, facilitating effective mitigation of the impacts of peak runoffs. Moreover, the developed methodologies can be adapted
to other macro- and meso-scale systems, with necessary adjustments based on available data
and system-specific characteristics, thus benefiting the broader scientific community.0000-0002-7683-37680000-0002-6206-075XDoctor (PhD) en Recursos HídricosCuenc
Can biological quantum networks solve NP-hard problems?
There is a widespread view that the human brain is so complex that it cannot
be efficiently simulated by universal Turing machines. During the last decades
the question has therefore been raised whether we need to consider quantum
effects to explain the imagined cognitive power of a conscious mind.
This paper presents a personal view of several fields of philosophy and
computational neurobiology in an attempt to suggest a realistic picture of how
the brain might work as a basis for perception, consciousness and cognition.
The purpose is to be able to identify and evaluate instances where quantum
effects might play a significant role in cognitive processes.
Not surprisingly, the conclusion is that quantum-enhanced cognition and
intelligence are very unlikely to be found in biological brains. Quantum
effects may certainly influence the functionality of various components and
signalling pathways at the molecular level in the brain network, like ion
ports, synapses, sensors, and enzymes. This might evidently influence the
functionality of some nodes and perhaps even the overall intelligence of the
brain network, but hardly give it any dramatically enhanced functionality. So,
the conclusion is that biological quantum networks can only approximately solve
small instances of NP-hard problems.
On the other hand, artificial intelligence and machine learning implemented
in complex dynamical systems based on genuine quantum networks can certainly be
expected to show enhanced performance and quantum advantage compared with
classical networks. Nevertheless, even quantum networks can only be expected to
efficiently solve NP-hard problems approximately. In the end it is a question
of precision - Nature is approximate.Comment: 38 page
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