656 research outputs found
Distributed two-time-scale methods over clustered networks
In this paper, we consider consensus problems over a network of nodes, where
the network is divided into a number of clusters. We are interested in the case
where the communication topology within each cluster is dense as compared to
the sparse communication across the clusters. Moreover, each cluster has one
leader which can communicate with other leaders in different clusters. The goal
of the nodes is to agree at some common value under the presence of
communication delays across the clusters.
Our main contribution is to propose a novel distributed two-time-scale
consensus algorithm, which pertains to the separation in network topology of
clustered networks. In particular, one scale is to model the dynamic of the
agents in each cluster, which is much faster (due to the dense communication)
than the scale describing the slowly aggregated evolution between the clusters
(due to the sparse communication). We prove the convergence of the proposed
method in the presence of uniform, but possibly arbitrarily large,
communication delays between the leaders. In addition, we provided an explicit
formula for the convergence rate of such algorithm, which characterizes the
impact of delays and the network topology. Our results shows that after a
transient time characterized by the topology of each cluster, the convergence
of the two-time-scale consensus method only depends on the connectivity of the
leaders. Finally, we validate our theoretical results by a number of numerical
simulations on different clustered networks
Particle Kalman Filtering: A Nonlinear Bayesian Framework for Ensemble Kalman Filters
This paper investigates an approximation scheme of the optimal nonlinear
Bayesian filter based on the Gaussian mixture representation of the state
probability distribution function. The resulting filter is similar to the
particle filter, but is different from it in that, the standard weight-type
correction in the particle filter is complemented by the Kalman-type correction
with the associated covariance matrices in the Gaussian mixture. We show that
this filter is an algorithm in between the Kalman filter and the particle
filter, and therefore is referred to as the particle Kalman filter (PKF). In
the PKF, the solution of a nonlinear filtering problem is expressed as the
weighted average of an "ensemble of Kalman filters" operating in parallel.
Running an ensemble of Kalman filters is, however, computationally prohibitive
for realistic atmospheric and oceanic data assimilation problems. For this
reason, we consider the construction of the PKF through an "ensemble" of
ensemble Kalman filters (EnKFs) instead, and call the implementation the
particle EnKF (PEnKF). We show that different types of the EnKFs can be
considered as special cases of the PEnKF. Similar to the situation in the
particle filter, we also introduce a re-sampling step to the PEnKF in order to
reduce the risk of weights collapse and improve the performance of the filter.
Numerical experiments with the strongly nonlinear Lorenz-96 model are presented
and discussed.Comment: Accepted manuscript, to appear in Monthly Weather Revie
К проблеме методологического статуса персонализированной медицины как практикоориентарованной учебной дисциплины
МЕДИЦИНСКИЕ УЧЕБНЫЕ ЗАВЕДЕНИЯОБРАЗОВАНИЕ МЕДИЦИНСКОЕСТУДЕНТЫУЧЕБНЫЕ ДИСЦИПЛИНЫПЕРСОНАЛИЗИРОВАННАЯ МЕДИЦИНАПРАКТИКО-ОРИЕНТИРОВАННАЯ ОБРАЗОВАТЕЛЬНАЯ СРЕДАПРАКТИКО-ОРИЕНТИРОВАННОЕ ОБУЧЕНИЕМЕТОДОЛОГИЧЕСКИЕ ПОДХОД
Unveiling the role of artificial intelligence for wound assessment and wound healing prediction
Wound healing is a very dynamic and complex process as it involves the patient, wound-level parameters, as well as biological, environmental, and socioeconomic factors. Its process includes hemostasis, inflammation, proliferation, and remodeling. Evaluation of wound components such as angiogenesis, inflammation, restoration of connective tissue matrix, wound contraction, remodeling, and re-epithelization would detail the healing process. Understanding key mechanisms in the healing process is critical to wound research. Elucidating its healing complexity would enable control and optimize the processes for achieving faster healing, preventing wound complications, and undesired outcomes such as infection, periwound dermatitis and edema, hematomas, dehiscence, maceration, or scarring. Wound assessment is an essential step for selecting an appropriate treatment and evaluating the wound healing process. The use of artificial intelligence (AI) as advanced computer-assisted methods is promising for gaining insights into wound assessment and healing. As AI-based approaches have been explored for various applications in wound care and research, this paper provides an overview of recent studies exploring the application of AI and its technical developments and suitability for accurate wound assessment and prediction of wound healing. Several studies have been done across the globe, especially in North America, Europe, Oceania, and Asia. The results of these studies have shown that AI-based approaches are promising for wound assessment and prediction of wound healing. However, there are still some limitations and challenges that need to be addressed. This paper also discusses the challenges and limitations of AI-based approaches for wound assessment and prediction of wound healing. The paper concludes with a discussion of future research directions and recommendations for the use of AI-based approaches for wound assessment and prediction of wound healing
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