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The research on tropical cyclone (TC) relieson numerical models and simulations, with the currently widely used boundary layer parameterization posing a significant challenge on accurately predicting turbulent mixing. However, machine learning has opened up new possibilities for boundary layer sub‐grid process parameterization. In this study, a deep‐learning parameterization scheme for the TC boundary layer (DeepBL) on predicting turbulent flux is proposed. DeepBL comprises a one‐dimensional convolutional structure that relies on a small number of learnable parameters that accomplishes an error reduction compared to the standard fully connected neural networks. Furthermore, a nonlinear transformation scheme is introduced to alleviate the training data's skewness and improve the DeepBL performance by affording a smaller prediction error. Specifically, the output data of a large‐eddy simulation of an idealized TC are used to train, validate, and test DeepBL, affording significantly better performance than the YSU scheme in the Weather Research and Forecast model. Interpretability analysis on DeepBL demonstrates that the deep‐learning parameterization scheme is physically reasonable. Plain Language Summary: Traditional boundary layer parameterizations explicitly program algorithms to reproduce the effects of turbulent processes in numerical models. They suffer from evident biases when simulating the tropical cyclone boundary layer. Therefore, a deep learning model with a concise structure is introduced to improve the boundary‐layer representation. The developed model is trained on the output of a large‐eddy simulation of a tropical cyclone, where the turbulent processes are explicitly simulated. Although the unbalanced training data distribution will hinder the deep learning model's performance, the nonlinear transformation proposed in this study assists the training data distribution to be more compact and reduce the prediction errors of the trained model. The results demonstrate that the proposed deep‐learning model is significantly improved compared with traditional methods in reproducing the spatial features and data distribution of turbulent fluxes. Key Points: A neural network (NN) with a one‐dimensional convolutional structure is designed to parameterize the tropical cyclone boundary layerThe proposed NN can reproduce the spatial features and data distribution of turbulent fluxes better than the traditional schemeA nonlinear transformation applied to the training data of the NN can markedly reduce errors for turbulent flux prediction [ABSTRACT FROM AUTHOR]

This study is concerned with reinforcement learning enhanced by two‐time scale approximations. Many systems arising in applications are large and complex. To treat these problems, it is often beneficial, and sometimes necessary, to reduce the dimensionality and aggregate states that are 'close' to each other. In this study, the authors propose a two‐time scale reinforcement learning method for such an aggregation process. In particular, they present how to classify states that are 'close' and demonstrate the effectiveness of the authors' state aggregation based two‐time scale methods. Thus the problem can be considered as using learning for identifying the system. A production planning problem with failure‐prone machines is used throughout this study to illustrate the main ideas, key steps and results. Monte Carlo simulations are used to generate the random environment. [ABSTRACT FROM AUTHOR]

Economic dispatch (ED) plays an important role in economic operation of the microgrid (MG). However, the communication links among distributed generators (DGs) may practically be corrupted by additive noise, resulting in erroneous deviations from ED commands. To deal with such issues, this study proposes a distributed noise‐resilient ED strategy for islanded MG. Owing to consensus‐based implementation, the proposed ED strategy is implemented in a fully distributed manner, which enables peer‐to‐peer communication among DGs without the necessity of a central controller. In addition, the proposed ED strategy involves post‐iterate averaging technique to further enhance its convergence under additive communication noise. In this way, different from the existing ED strategies, the proposed ED strategy is fully distributed and resilient to the communication noise. Furthermore, the effectiveness of the proposed ED strategy is evaluated on an islanded MG, where the communication links are corrupted by different levels of additive noise. Finally, in order to fully take account of the uncertainty and stochastic nature of different operation scenarios, the Monte‐Carlo simulations are carried out, of which the simulation results demonstrate that the proposed ED strategy is superior to the existing ED strategies, in terms of the convergence property under additive communication noise. [ABSTRACT FROM AUTHOR]

Abstract: In recent years, microRNAs (miRNAs) are attracting an increasing amount of researchers’ attention, as accumulating studies show that miRNAs play important roles in various basic biological processes and that dysregulation of miRNAs is connected with diverse human diseases, particularly cancers. However, the experimental methods to identify associations between miRNAs and diseases remain costly and laborious. In this study, we developed a computational method named Network Distance Analysis for MiRNA‐Disease Association prediction (NDAMDA) which could effectively predict potential miRNA‐disease associations. The highlight of this method was the use of not only the direct network distance between 2 miRNAs (diseases) but also their respective mean network distances to all other miRNAs (diseases) in the network. The model's reliable performance was certified by the AUC of 0.8920 in global leave‐one‐out cross‐validation (LOOCV), 0.8062 in local LOOCV and the average AUCs of 0.8935 ± 0.0009 in fivefold cross‐validation. Moreover, we applied NDAMDA to 3 different case studies to predict potential miRNAs related to breast neoplasms, lymphoma, oesophageal neoplasms, prostate neoplasms and hepatocellular carcinoma. Results showed that 86%, 72%, 86%, 86% and 84% of the top 50 predicted miRNAs were supported by experimental association evidence. Therefore, NDAMDA is a reliable method for predicting disease‐related miRNAs. [ABSTRACT FROM AUTHOR]

Microgrids with distributed energy generators, controllable appliances, electric vehicle charging infrastructures, and energy storage systems introduce new technical challenges in the management of distribution networks. This paper introduces a new framework for power flow control based on the emerging consensus control for networked systems. Due to unique features of power systems, the consensus control problem becomes weighted and constrained, beyond the typical consensus formulation. Using only neighborhood communication for each bus on the microgrid, the consensus control achieves global weighted load balancing. Algorithms are introduced and their convergence properties are established. It is shown that the algorithms have the fastest convergence rates in terms of consensus error variances, by achieving asymptotically the Cramér-Rao lower bound, and hence is optimal among all algorithms. Examples and case studies demonstrate convergence, robustness, and scalability of the methodology and feasibility in distribution networks. Practical issues are further discussed. [ABSTRACT FROM AUTHOR]

SUMMARY Feedback systems with communication channels encounter unique challenges. Communication channels mandate signal sampling and quantization, and introduce errors, data losses, and delays. Consequently, transmitted signals must be estimated. The signal estimation introduces a dynamic system that interacts with communication channels and affects the stability and performance of the feedback system. This paper studies interactions among communications, sampling, quantization, signal estimation, and feedback, in terms of fundamental stability and performance limitations. Typical empirical-measure-based algorithms are used for signal estimation under quantized observations. When the sampling interval and signal estimation step size are coordinated, the ODE approach for stochastic approximations provides a suitable platform for an integrated system analysis for signal estimation, sampling and quantization, and feedback robustness. Feedback design for enhancing robustness against communication uncertainty and signal estimation dynamics is studied under the new notion of stability margin under signal averaging. Fundamental limitations on noise attenuation in such an integrated system are derived. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This work is devoted to analyzing adaptive filtering algorithms with the use of sign-regressor for randomly time-varying parameters (a discrete-time Markov chain). In accordance with different adaption and transition rates, we analyze the corresponding asymptotic properties of the algorithms. When the adaptation rate is in line with the transition rate, we obtain a limit of a Markov switched differential equation. When the Markov chain is slowly changing the parameter process is almost a constant, and we derive a limit differential equation. When the Markov chain is fast varying, the limit system is again a differential equation that is an average with respect to the stationary distribution of the Markov chain. In addition to the limit dynamic systems, we obtain asymptotic properties of centered and scaled tracking errors. We obtain mean square errors to illustrate the dependence on the stepsize as well as on the transition rate. The limit distributions in terms of scaled errors are studied by examining certain centered and scaled error sequences. [ABSTRACT FROM AUTHOR]

SUMMARY This work focuses on tracking and system identification of systems with regime-switching parameters, which are modeled by a Markov process. It introduces a framework for persistent identification problems that encompass many typical system uncertainties, including parameter switching, stochastic observation disturbances, deterministic unmodeled dynamics, sensor observation bias, and nonlinear model mismatch. In accordance with the 'frequency' of the parameter switching process, we divide the problems into two classes. For fast-switching systems, the switching parameters are stochastic processes modeled by irreducible and aperiodic Markov chains. Because accurately tracking real-time parameters in such systems is not possible because of the uncertainty principles, the effect of parameter switching is evaluated on their average by the stationary distribution of the Markovian chain and estimated by the least squares algorithms. We derive upper and lower bounds on identification errors, which characterize how identification accuracy depends on the earlier uncertainty terms. When the system parameters switch their values infrequently in a probabilistic sense, their values can be tracked based on input/output observations. Stochastic approximation algorithms with adaptive step sizes are used for such systems. Simulation studies are carried out to demonstrate that slowly varying parameters could be tracked with reasonable accuracy.Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]