311 research outputs found
Inference and dynamic decision-making for deteriorating systems with probabilistic dependencies through Bayesian networks and deep reinforcement learning
In the context of modern environmental and societal concerns, there is an
increasing demand for methods able to identify management strategies for civil
engineering systems, minimizing structural failure risks while optimally
planning inspection and maintenance (I&M) processes. Most available methods
simplify the I&M decision problem to the component level due to the
computational complexity associated with global optimization methodologies
under joint system-level state descriptions. In this paper, we propose an
efficient algorithmic framework for inference and decision-making under
uncertainty for engineering systems exposed to deteriorating environments,
providing optimal management strategies directly at the system level. In our
approach, the decision problem is formulated as a factored partially observable
Markov decision process, whose dynamics are encoded in Bayesian network
conditional structures. The methodology can handle environments under equal or
general, unequal deterioration correlations among components, through Gaussian
hierarchical structures and dynamic Bayesian networks. In terms of policy
optimization, we adopt a deep decentralized multi-agent actor-critic (DDMAC)
reinforcement learning approach, in which the policies are approximated by
actor neural networks guided by a critic network. By including deterioration
dependence in the simulated environment, and by formulating the cost model at
the system level, DDMAC policies intrinsically consider the underlying
system-effects. This is demonstrated through numerical experiments conducted
for both a 9-out-of-10 system and a steel frame under fatigue deterioration.
Results demonstrate that DDMAC policies offer substantial benefits when
compared to state-of-the-art heuristic approaches. The inherent consideration
of system-effects by DDMAC strategies is also interpreted based on the learned
policies
Optimal Inspection and Maintenance Planning for Deteriorating Structural Components through Dynamic Bayesian Networks and Markov Decision Processes
Civil and maritime engineering systems, among others, from bridges to
offshore platforms and wind turbines, must be efficiently managed as they are
exposed to deterioration mechanisms throughout their operational life, such as
fatigue or corrosion. Identifying optimal inspection and maintenance policies
demands the solution of a complex sequential decision-making problem under
uncertainty, with the main objective of efficiently controlling the risk
associated with structural failures. Addressing this complexity, risk-based
inspection planning methodologies, supported often by dynamic Bayesian
networks, evaluate a set of pre-defined heuristic decision rules to reasonably
simplify the decision problem. However, the resulting policies may be
compromised by the limited space considered in the definition of the decision
rules. Avoiding this limitation, Partially Observable Markov Decision Processes
(POMDPs) provide a principled mathematical methodology for stochastic optimal
control under uncertain action outcomes and observations, in which the optimal
actions are prescribed as a function of the entire, dynamically updated, state
probability distribution. In this paper, we combine dynamic Bayesian networks
with POMDPs in a joint framework for optimal inspection and maintenance
planning, and we provide the formulation for developing both infinite and
finite horizon POMDPs in a structural reliability context. The proposed
methodology is implemented and tested for the case of a structural component
subject to fatigue deterioration, demonstrating the capability of
state-of-the-art point-based POMDP solvers for solving the underlying planning
optimization problem. Within the numerical experiments, POMDP and
heuristic-based policies are thoroughly compared, and results showcase that
POMDPs achieve substantially lower costs as compared to their counterparts,
even for traditional problem settings
Farm-wide virtual load monitoring for offshore wind structures via Bayesian neural networks
Offshore wind structures are subject to deterioration mechanisms throughout
their operational lifetime. Even if the deterioration evolution of structural
elements can be estimated through physics-based deterioration models, the
uncertainties involved in the process hurdle the selection of lifecycle
management decisions. In this scenario, the collection of relevant information
through an efficient monitoring system enables the reduction of uncertainties,
ultimately driving more optimal lifecycle decisions. However, a full monitoring
instrumentation implemented on all wind turbines in a farm might become
unfeasible due to practical and economical constraints. Besides, certain load
monitoring systems often become defective after a few years of marine
environment exposure. Addressing the aforementioned concerns, a farm-wide
virtual load monitoring scheme directed by a fleet-leader wind turbine offers
an attractive solution. Fetched with data retrieved from a fully-instrumented
wind turbine, a model can be trained and then deployed, thus yielding load
predictions of non-fully monitored wind turbines, from which only standard data
remains available. In this paper, we propose a virtual load monitoring
framework formulated via Bayesian neural networks (BNNs) and we provide
relevant implementation details needed for the construction, training, and
deployment of BNN data-based virtual monitoring models. As opposed to their
deterministic counterparts, BNNs intrinsically announce the uncertainties
associated with generated load predictions and allow to detect inaccurate load
estimations generated for non-fully monitored wind turbines. The proposed
virtual load monitoring is thoroughly tested through an experimental campaign
in an operational offshore wind farm and the results demonstrate the
effectiveness of BNN models for fleet-leader-based farm-wide virtual
monitoring
Infestação por Aedes aegypti estimada por armadilha de oviposição em Salvador, Bahia
OBJECTIVE: To assess infestation levels of Aedes aegypti using the oviposition trap (ovitrap) method and to compare these results with data obtained with the use of indices traditionally applied in public programs aimed at fighting this vector. METHODS: Nine sentinel areas in Northeastern, Brazil, were assessed and infestation levels were measured for a nine-month period. Egg density and container indices were estimated and compared with previous results found using the house index and Breteau index. RESULTS: The results indicated that the area studied was infested with this vector during the entire study period and that the infestation was widespread in all areas. Different results were found with the different indices studied. There were areas in which the house index and the Breteau index were negative or close to zero, whereas the container index for the same area was 11% and the egg density index was 8.3%. CONCLUSIONS: The container and egg density indices allow better assessment of infestation rates in a city than the conventionally used indices (house index and Breteau index). At lower operational costs and easier standardization, these indices can be applied as a measurement tool for assessing infestation rates during entomological surveillance in programs to fight Aedes aegypti.OBJETIVO: Estimar os índices de infestação do Aedes aegypti, utilizando ovitrampa com atrativo e comparar esse método com os tradicionalmente utilizados nos programas oficiais de combate ao vetor. MÉTODOS: Foram analisadas nove áreas sentinelas de Salvador, Estado da Bahia, durante nove meses. Foram calculados os índices de densidade de ovos e positividade de ovitrampa, e levantamento dos índices de infestação predial e de Breteau para comparação. RESULTADOS: Observou-se que o município apresentou infestação pelo vetor durante todo o período de estudo em todas as áreas sentinelas. Os índices nem sempre apresentaram resultados de infestação semelhantes. Em algumas áreas os índices de infestação predial e de Breteau foram negativos ou próximos de zero, enquanto que o índice de positividade de ovitrampa apresentou valor de 11% e o índice de densidade de ovos 8,3%. CONCLUSÕES: O índice de positividade de ovitrampa e o índice de densidade de ovos permitem avaliar melhor o quadro de infestação de uma cidade com custo operacional bastante reduzido e com maior facilidade de padronização do que os índices tradicionais (infestação predial e de Breteau). Recomenda-se, assim, sua utilização nas fases de levantamento de índices e de vigilância entomológica desenvolvidas pelo programa de combate ao Aedes aegypti
Theory of the Relativistic Brownian Motion. The (1+1)-Dimensional Case
We construct a theory for the 1+1-dimensional Brownian motion in a viscous
medium, which is (i) consistent with Einstein's theory of special relativity,
and (ii) reduces to the standard Brownian motion in the Newtonian limit case.
In the first part of this work the classical Langevin equations of motion,
governing the nonrelativistic dynamics of a free Brownian particle in the
presence of a heat bath (white noise), are generalized in the framework of
special relativity. Subsequently, the corresponding relativistic Langevin
equations are discussed in the context of the generalized Ito (pre-point
discretization rule) vs. the Stratonovich (mid-point discretization rule)
dilemma: It is found that the relativistic Langevin equation in the
Haenggi-Klimontovich interpretation (with the post-point discretization rule)
is the only one that yields agreement with the relativistic Maxwell
distribution. Numerical results for the relativistic Langevin equation of a
free Brownian particle are presented.Comment: see cond-mat/0607082 for an improved theor
An Augmented Interface to Display Industrial Robot Faults
Technology advancement is changing the way industrial factories have to face an increasingly complex and competitive market. The fourth industrial revolution (known as industry 4.0) is also changing how human workers have to carry out tasks and actions. In fact, it is no longer impossible to think of a scenario in which human operators and industrial robots work side-by-side, sharing the same environment and tools. To realize a safe work environment, workers should trust robots as well as they trust human operators. Such goal is indeed complex to achieve, especially when workers are under stress conditions, such as when a fault occurs and the human operators are no longer able to understand what is happening in the industrial manipulator. Indeed, Augmented Reality (AR) can help workers to visualize in real-time robots’ faults. This paper proposes an augmented system that assists human workers to recognize and visualize errors, improving their awareness of the system. The system has been tested using both an AR see-through device and a smartphone
Urban Climate Action. The urban content of the NDCs: Global review 2022
This report was prepared by United Nations Human Settlement Programme (UN-Habitat) and the UNESCO Chair on Urban Resilience at the University of Southern Denmark (SDU.Resilience). It offers a global analysis of the urban content of 193 Nationally Determined Contributions (NDCs) submitted to the Secretariat of the United Nations Framework Convention on Climate Change (UNFCCC) before the 19th of June 2022. For this report, more than 200 indicators were used to analyse external data (e.g., Human Development Index and income categorisation) and data within the NDCs, including climate mitigation and adaptation challenges and responses, as well as specific sectors. This analysis is instrumental to supporting Parties’ efforts in further integrating national climate policies and urban climate actions, which is considered fundamental to raising ambition and developing adequate and timely actions as required by the current climate emergency. This review can be instrumental for advocacy and direct support to countries by partner organisations. The work was supported by a group of experts from bilateral and multilateral organisations and academia. Three expert group meetings were convened, and a peer review was organised for the final report
Paternity testing and behavioral ecology: a case study of jaguars (Panthera onca) in Emas National Park, Central Brazil.
We used microsatellite loci to test the paternity of two male jaguars involved in an infanticide event recorded during a long-term monitoring program of this species. Seven microsatellite primers originally developed for domestic cats and previously selected for Panthera oncawere used. In order to deal with uncertainty in the mother?s genotypes for some of the loci, 10000 values of Wwere derived by simulation procedures. The male that killed the two cubs was assigned as the true sire. Although the reasons for this behavior remain obscure, it shows, in principle, a low recognition of paternity and kinship in the species. Since the two cubs were not very young, one possibility is that the adult male did not recognize the cubs and killed them for simple territorial reasons. Thus, ecological stress in this local population becomes a very plausible explanation for this infanticide, without further sociobiological implications
The Schroedinger Problem, Levy Processes Noise in Relativistic Quantum Mechanics
The main purpose of the paper is an essentially probabilistic analysis of
relativistic quantum mechanics. It is based on the assumption that whenever
probability distributions arise, there exists a stochastic process that is
either responsible for temporal evolution of a given measure or preserves the
measure in the stationary case. Our departure point is the so-called
Schr\"{o}dinger problem of probabilistic evolution, which provides for a unique
Markov stochastic interpolation between any given pair of boundary probability
densities for a process covering a fixed, finite duration of time, provided we
have decided a priori what kind of primordial dynamical semigroup transition
mechanism is involved. In the nonrelativistic theory, including quantum
mechanics, Feyman-Kac-like kernels are the building blocks for suitable
transition probability densities of the process. In the standard "free" case
(Feynman-Kac potential equal to zero) the familiar Wiener noise is recovered.
In the framework of the Schr\"{o}dinger problem, the "free noise" can also be
extended to any infinitely divisible probability law, as covered by the
L\'{e}vy-Khintchine formula. Since the relativistic Hamiltonians
and are known to generate such laws, we focus on
them for the analysis of probabilistic phenomena, which are shown to be
associated with the relativistic wave (D'Alembert) and matter-wave
(Klein-Gordon) equations, respectively. We show that such stochastic processes
exist and are spatial jump processes. In general, in the presence of external
potentials, they do not share the Markov property, except for stationary
situations. A concrete example of the pseudodifferential Cauchy-Schr\"{o}dinger
evolution is analyzed in detail. The relativistic covariance of related waveComment: Latex fil
Peace and the environment at the crossroads: Elections in a conflict-troubled biodiversity hotspot
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