6,611 research outputs found
NDM-515: AN ORIGINAL MODEL OF INFRASTRUCTURE SYSTEM RESILIENCE
Infrastructure systems of transportation, water supply, telecommunications, power supply, etc. are not isolated but highly interconnected and mutually coupled. Infrastructure interdependences can increase system vulnerability and produce cascading failures at the regional or national scales. Taking the advantage of network theory structure analysis, this paper models street, water supply network, power grid and information infrastructure as network layers that are integrated into a multilayer network. The infrastructure interdependences are detailed using five basic dependence patterns of network fundamental elements. Definitions of dynamic cascading failures and recovery mechanisms of infrastructure systems are also established. The main focus of the paper is introduction of a new infrastructure network resilience measure capable of addressing infrastructure system as well as network component (layer) interdependences. The new measure is based on infrastructure network performance, proactive infrastructure network resistance capacity and reactive infrastructure network recovery capacity. With three resilience features and corresponding network properties develops paper, this the of dynamic space new quantitative measure -time resilience and a resilience simulation model resilience and network properties three dimensions of use for infrastructure network assessments. The resilience model is applicable to any type of infrastructure and its application can improve the infrastructure planning, design and maintenance decision making
Ecological models at fish community and species level to support effective river restoration
RESUMEN
Los peces nativos son indicadores de la salud de los ecosistemas acuáticos, y se han
convertido en un elemento de calidad clave para evaluar el estado ecológico de los ríos. La
comprensión de los factores que afectan a las especies nativas de peces es importante para la
gestión y conservación de los ecosistemas acuáticos. El objetivo general de esta tesis es analizar
las relaciones entre variables biológicas y de hábitat (incluyendo la conectividad) a través de
una variedad de escalas espaciales en los ríos Mediterráneos, con el desarrollo de herramientas
de modelación para apoyar la toma de decisiones en la restauración de ríos.
Esta tesis se compone de cuatro artículos. El primero tiene como objetivos modelar la
relación entre un conjunto de variables ambientales y la riqueza de especies nativas (NFSR), y
evaluar la eficacia de potenciales acciones de restauración para mejorar la NFSR en la cuenca
del río Júcar. Para ello se aplicó un enfoque de modelación de red neuronal artificial (ANN),
utilizando en la fase de entrenamiento el algoritmo Levenberg-Marquardt. Se aplicó el método
de las derivadas parciales para determinar la importancia relativa de las variables ambientales.
Según los resultados, el modelo de ANN combina variables que describen la calidad de ribera,
la calidad del agua y el hábitat físico, y ayudó a identificar los principales factores que
condicionan el patrón de distribución de la NFSR en los ríos Mediterráneos. En la segunda parte
del estudio, el modelo fue utilizado para evaluar la eficacia de dos acciones de restauración en el
río Júcar: la eliminación de dos azudes abandonados, con el consiguiente incremento de la
proporción de corrientes. Estas simulaciones indican que la riqueza aumenta con el incremento
de la longitud libre de barreras artificiales y la proporción del mesohabitat de corriente, y
demostró la utilidad de las ANN como una poderosa herramienta para apoyar la toma de
decisiones en el manejo y restauración ecológica de los ríos Mediterráneos.
El segundo artículo tiene como objetivo determinar la importancia relativa de los dos
principales factores que controlan la reducción de la riqueza de peces (NFSR), es decir, las
interacciones entre las especies acuáticas, variables del hábitat (incluyendo la conectividad
fluvial) y biológicas (incluidas las especies invasoras) en los ríos Júcar, Cabriel y Turia. Con
este fin, tres modelos de ANN fueron analizados: el primero fue construido solamente con
variables biológicas, el segundo se construyó únicamente con variables de hábitat y el tercero
con la combinación de estos dos grupos de variables. Los resultados muestran que las variables
de hábitat son los ¿drivers¿ más importantes para la distribución de NFSR, y demuestran la
importancia ecológica de los modelos desarrollados. Los resultados de este estudio destacan la
necesidad de proponer medidas de mitigación relacionadas con la mejora del hábitat
(incluyendo la variabilidad de caudales en el río) como medida para conservar y restaurar los
ríos Mediterráneos.
El tercer artículo busca comparar la fiabilidad y relevancia ecológica de dos modelos
predictivos de NFSR, basados en redes neuronales artificiales (ANN) y random forests (RF). La
relevancia de las variables seleccionadas por cada modelo se evaluó a partir del conocimiento
ecológico y apoyado por otras investigaciones. Los dos modelos fueron desarrollados utilizando
validación cruzada k-fold y su desempeño fue evaluado a través de tres índices: el coeficiente de determinación (R2
), el error cuadrático medio (MSE) y el coeficiente de determinación ajustado
(R2
adj). Según los resultados, RF obtuvo el mejor desempeño en entrenamiento. Pero, el
procedimiento de validación cruzada reveló que ambas técnicas generaron resultados similares
(R2
= 68% para RF y R2
= 66% para ANN). La comparación de diferentes métodos de machine
learning es muy útil para el análisis crítico de los resultados obtenidos a través de los modelos.
El cuarto artículo tiene como objetivo evaluar la capacidad de las ANN para identificar los
factores que afectan a la densidad y la presencia/ausencia de Luciobarbus guiraonis en la
demarcación hidrográfica del Júcar. Se utilizó una red neuronal artificial multicapa de tipo feedforward (ANN) para representar relaciones no lineales entre descriptores de L. guiraonis con
variables biológicas y de hábitat. El poder predictivo de los modelos se evaluó con base en el
índice Kappa (k), la proporción de casos correctamente clasificados (CCI) y el área bajo la curva
(AUC) característica operativa del receptor (ROC). La presencia/ausencia de L. guiraonis fue
bien predicha por el modelo ANN (CCI = 87%, AUC = 0.85 y k = 0.66). La predicción de la
densidad fue moderada (CCI = 62%, AUC = 0.71 y k = 0.43). Las variables más importantes
que describen la presencia/ausencia fueron: radiación solar, área de drenaje y la proporción de
especies exóticas de peces con un peso relativo del 27.8%, 24.53% y 13.60% respectivamente.
En el modelo de densidad, las variables más importantes fueron el coeficiente de variación de
los caudales medios anuales con una importancia relativa del 50.5% y la proporción de especies
exóticas de peces con el 24.4%. Los modelos proporcionan información importante acerca de la
relación de L. guiraonis con variables bióticas y de hábitat, este nuevo conocimiento podría
utilizarse para apoyar futuros estudios y para contribuir en la toma de decisiones para la
conservación y manejo de especies en los en los ríos Júcar, Cabriel y Turia.Olaya Marín, EJ. (2013). Ecological models at fish community and species level to support effective river restoration [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/28853TESI
Multi-Agent Systems and Complex Networks: Review and Applications in Systems Engineering
Systems engineering is an ubiquitous discipline of Engineering overlapping industrial, chemical, mechanical, manufacturing, control, software, electrical, and civil engineering. It provides tools for dealing with the complexity and dynamics related to the optimisation of physical, natural, and virtual systems management. This paper presents a review of how multi-agent systems and complex networks theory are brought together to address systems engineering and management problems. The review also encompasses current and future research directions both for theoretical fundamentals and applications in the industry. This is made by considering trends such as mesoscale, multiscale, and multilayer networks along with the state-of-art analysis on network dynamics and intelligent networks. Critical and smart infrastructure, manufacturing processes, and supply chain networks are instances of research topics for which this literature review is highly relevant
Realtime Profiling of Fine-Grained Air Quality Index Distribution using UAV Sensing
Given significant air pollution problems, air quality index (AQI) monitoring
has recently received increasing attention. In this paper, we design a mobile
AQI monitoring system boarded on unmanned-aerial-vehicles (UAVs), called ARMS,
to efficiently build fine-grained AQI maps in realtime. Specifically, we first
propose the Gaussian plume model on basis of the neural network (GPM-NN), to
physically characterize the particle dispersion in the air. Based on GPM-NN, we
propose a battery efficient and adaptive monitoring algorithm to monitor AQI at
the selected locations and construct an accurate AQI map with the sensed data.
The proposed adaptive monitoring algorithm is evaluated in two typical
scenarios, a two-dimensional open space like a roadside park, and a
three-dimensional space like a courtyard inside a building. Experimental
results demonstrate that our system can provide higher prediction accuracy of
AQI with GPM-NN than other existing models, while greatly reducing the power
consumption with the adaptive monitoring algorithm
Knowledge Discovery in the SCADA Databases Used for the Municipal Power Supply System
This scientific paper delves into the problems related to the develop-ment of
intellectual data analysis system that could support decision making to manage
municipal power supply services. The management problems of mu-nicipal power
supply system have been specified taking into consideration modern tendencies
shown by new technologies that allow for an increase in the energy efficiency.
The analysis findings of the system problems related to the integrated
computer-aided control of the power supply for the city have been given. The
consideration was given to the hierarchy-level management decom-position model.
The objective task targeted at an increase in the energy effi-ciency to
minimize expenditures and energy losses during the generation and
transportation of energy carriers to the Consumer, the optimization of power
consumption at the prescribed level of the reliability of pipelines and
networks and the satisfaction of Consumers has been defined. To optimize the
support of the decision making a new approach to the monitoring of engineering
systems and technological processes related to the energy consumption and
transporta-tion using the technologies of geospatial analysis and Knowledge
Discovery in databases (KDD) has been proposed. The data acquisition for
analytical prob-lems is realized in the wireless heterogeneous medium, which
includes soft-touch VPN segments of ZigBee technology realizing the 6LoWPAN
standard over the IEEE 802.15.4 standard and also the segments of the networks
of cellu-lar communications. JBoss Application Server is used as a server-based
plat-form for the operation of the tools used for the retrieval of data
collected from sensor nodes, PLC and energy consumption record devices. The KDD
tools are developed using Java Enterprise Edition platform and Spring and ORM
Hiber-nate technologies
Models for the modern power grid
This article reviews different kinds of models for the electric power grid
that can be used to understand the modern power system, the smart grid. From
the physical network to abstract energy markets, we identify in the literature
different aspects that co-determine the spatio-temporal multilayer dynamics of
power system. We start our review by showing how the generation, transmission
and distribution characteristics of the traditional power grids are already
subject to complex behaviour appearing as a result of the the interplay between
dynamics of the nodes and topology, namely synchronisation and cascade effects.
When dealing with smart grids, the system complexity increases even more: on
top of the physical network of power lines and controllable sources of
electricity, the modernisation brings information networks, renewable
intermittent generation, market liberalisation, prosumers, among other aspects.
In this case, we forecast a dynamical co-evolution of the smart grid and other
kind of networked systems that cannot be understood isolated. This review
compiles recent results that model electric power grids as complex systems,
going beyond pure technological aspects. From this perspective, we then
indicate possible ways to incorporate the diverse co-evolving systems into the
smart grid model using, for example, network theory and multi-agent simulation.Comment: Submitted to EPJ-ST Power Grids, May 201
A survey on multilayer networks modelled to assess robustness in infrastructure systems
The development of modern societies places particular demands on the consistent performance of infrastructure systems. Because multilayer network models are capable of representing the interdependencies between infrastructure components, they have been widely used to analyse the robustness of infrastructure systems. This present study is a systematic review of literature, published since 2010. It aims to investigate how multilayer network models have been used in analysing the robustness of infrastructure systems. According to findings, percolation theory was the most popular method used in about 57% of papers. Regarding the properties, coupling strength and node degree were the most common while directed links and feedback conditions were the least common. The following gaps were identified which provide opportunities for further research. These include the absence of models based on real-world data and the need for models that make fewer simplifying assumptions about complex systems. No papers considered all potential properties, and their effect on boosting or weakening each other’s effect. By considering all properties, the importance of different properties on the robustness of infrastructure systems can be quantified and compared in future studies
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