Search

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]

Amyotrophic lateral sclerosis (ALS) is a rare neurodegenerative disease with a challenging treatment landscape, due to its complex pathogenesis and limited availability of clinical drugs. Ferroptosis, an iron-dependent form of programmed cell death (PCD), stands distinct from apoptosis, necrosis, autophagy, and other cell death mechanisms. Recent studies have increasingly highlighted the role of iron deposition, reactive oxygen species (ROS) accumulation, oxidative stress, as well as systemic Xc- and glutamate accumulation in the antioxidant system in the pathogenesis of amyotrophic lateral sclerosis. Therefore, targeting ferroptosis emerges as a promising strategy for amyotrophic lateral sclerosis treatment. This review introduces the regulatory mechanism of ferroptosis, the relationship between amyotrophic lateral sclerosis and ferroptosis, and the drugs used in the clinic, then discusses the current status of amyotrophic lateral sclerosis treatment, hoping to provide new directions and targets for its treatment. [ABSTRACT FROM AUTHOR]

Secreted protein acidic and rich in cysteine (SPARC) shows a specific colocalization with limbal epithelial stem cells (LESCs) in vivo; however, the inherent relationship between SPARC and LESCs is still unclear. This study investigated the effects of SPARC on the maintenance of LESC stemness and corneal wound healing. To test the influence of different concentration of exogenous SPARC on the proliferation of LESCs, cell counting kit-8 assay and 5-ethynyl-2′-deoxyuridine staining were performed and the results indicated that 1 μg/mL SPARC was the optimum concentration for enhanced LESC proliferation. Compared with a control group, SPARC-treated group showed a higher expression of LESC-positive markers p63α, ABCG-2, and Bmi-1, and a lower level of differentiation marker cytokeratin-3 (CK3), thereby suggesting that SPARC could maintain LESC characteristic phenotype and suppress spontaneous epithelial differentiation in vitro. In vivo, exogenous SPARC accelerated the wound-healing process by both the enhancement of LESC proliferation and promoting the migration of the proliferating cells. However, the intact epithelium impaired this function of SPARC by contact inhibition. [ABSTRACT FROM AUTHOR]

This article is the second part of a two-part article on communication-failure-resilient architecture for distributed operation and control in smart grids with hybrid producer/consumer (prosumer) agents. Part I of this article proposed the distributed architecture and algorithmic development, while this Part II provides the corresponding algorithmic implementation, technical requirements, practical applications, and simulations. We propose a general and scalable framework for implementing resilient distributed frequency regulation (DFR) on large-scale prosumer-based power grids, such as utilities, where each prosumer represents a frequency control area. We first develop a method to obtain quasi-steady state models of prosumers from the dynamic models of generators, loads, and power lines. This model allows demonstrating the dynamic evolution of prosumers under normal conditions and during communication failures. Next, we propose a framework to implement the resilient DFR algorithm on prosumer power grids. We show that under normal conditions prosumers need to solely communicate with their neighbors to obtain optimal control actions. But, during communication failures, prosumers need to extend the communication hops to maintain system-wide stability as well as fair power sharing. The resilient DFR algorithm is demonstrated on two real-world, large-scale power grids and the results show that it can indeed complement and enhance today's Automatic Generation Control systems. [ABSTRACT FROM AUTHOR]

Distributed algorithms have been proposed as options to scale control propositions to the massive number of intelligent energy devices, sub-systems, and distributed energy resources being integrated into the electricity grid. Distributed algorithms rely on the communication network for exchanging information. Failures in the communication network can jeopardize distributed decision-making and in the worst-case scenario can lead to system-level stability problems. This paper proposes a communication-failure-resilient architecture for distributed operation and control in smart grids with hybrid producer/consumer (prosumer) agents. We describe the relations between system-wide performance and communication failures and identify topological conditions on the cyber-physical network, under which prosumers can perform key operating tasks, such as distributed frequency regulation through an imperfect communication network. [ABSTRACT FROM AUTHOR]

This paper studies mean-square (MS) convergent observers for estimating continuous states of randomly switched linear systems (RSLSs) with unobservable subsystems that are subject to stochastic output observation noises. When subsystems are unobservable and switching sequences are random, the classical Kalman–Bucy filters that are applied to observable sub-states are shown to be potentially divergent. It is also shown that unless the switching interval T can be selected to be sufficiently small from the outset, MS convergence may never be achieved, regardless of how the observers for the subsystems are designed. The critical threshold T m a x on T is derived for MS convergent observers to be achievable. Under the condition T < T m a x , this paper introduces design algorithms for subsystem observers to generate a globally MS convergent observer for the entire continuous state. A fundamental design tradeoff between convergence speeds and steady-state estimation errors is analyzed. This paper extends our recent new framework and algorithms for strong convergent observer design in RSLSs by including observation noises, considering multi-output systems, establishing new algorithms for MS convergence, and developing design tradeoff analysis. Examples and a practical case study are presented to illustrate the design procedures, main convergence properties, and error analysis. [ABSTRACT FROM AUTHOR]

This paper establishes exponential convergence rates for a class of primal–dual gradient algorithms in distributed optimization without strong convexity. The convergence analysis is based on a carefully constructed Lyapunov function. By evaluating metric subregularity of the primal–dual gradient map, we present a general criterion under which the algorithm achieves exponential convergence. To facilitate practical applications of this criterion, several simplified sufficient conditions are derived. We also prove that although these results are developed for the continuous-time algorithms, they carry over in a parallel manner to the discrete-time algorithms constructed by using Euler's approximation method. [ABSTRACT FROM AUTHOR]

Connected and automated vehicles mandate integrated design of communications and control to achieve coordination of highway vehicles. Random features of wireless communications introduce new types of uncertainties into networked systems and impact control performance significantly. Due to typical packet loss, erasure channels create random link interruption and switching in network topologies. This paper models such switching network topologies by Markov chains and derives their probability transition matrices from stochastic characterizations of the channels. Impact of communication erasure channels on vehicle platoon formation and robustness under a weighted and constrained consensus framework is analyzed. By comparing convergence properties of networked control algorithms under different communication channel features, we characterize some intrinsic relationships between packet delivery ratio and convergence rate. Simulation case studies are performed to verify the theoretical findings. [ABSTRACT FROM PUBLISHER]

In this paper, we investigate active on-line diagnosis in discrete event systems. Active diagnosis can be used for fault detection, fault localization, fault-tolerant control, among others. Discrete event systems are general models for complex manmade systems. For the active on-line diagnosis, we do not construct the entire diagnostic automaton off-line. Instead, we look N steps ahead to determine active diagnosability and calculate diagnostic strategies. Thus, we define active N -diagnosability and investigate the relation between active diagnosability and active N -diagnosability. We also develop an algorithm to check active N -diagnosability. If a system is actively N -diagnosable, the algorithm will also give the control that diagnoses the system. We show that there are significant computational advantages for using the on-line approach. [ABSTRACT FROM AUTHOR]

This paper investigates identification of Wiener systems with quantized inputs and binary-valued output observations. By parameterizing the static nonlinear function and incorporating both linear and nonlinear parts, we begin by investigating system identifiability under the input and output constraints. Then a three-step algorithm is proposed to estimate the unknown parameters by using the empirical measure, input persistent patterns, and information on noise statistics. Convergence properties of the algorithm, including strong convergence and mean-square convergence rate, are established. Furthermore, by selecting a suitable transformation matrix, the asymptotic efficiency of the algorithm is proved in terms of the Cramér–Rao lower bound. Finally, numerical simulations are presented to illustrate the main results of this paper. [ABSTRACT FROM AUTHOR]

This paper investigates controllability for linear time-invariant systems under irregular and random sampling, and develops adaptive control algorithms with respect to sampling intervals. Using block erasure channels as the main motivating communication platform, it first establishes a sufficient condition on sampling density that ensures controllability of sampled systems, which is necessary for feedback design and adaptation. Then, it continues with causal adaptive feedback algorithms to accommodate time-varying sampling intervals. Implementation of such algorithms encounters technical challenges because future sampling intervals are uncertain or random. Under deterministic slowly-varying and stochastic infrequent Markovian jumping sampling intervals, overall system stability is established. Simulation results are used to illustrate the algorithms and their effectiveness. [ABSTRACT FROM AUTHOR]

The reliable operation of battery management systems depends critically on the accurate estimation of the state of charge (SOC) and characterizing parameters of a battery system. SOC estimation employs models that must be robust against variations in battery cell electrochemical features, aging, and operating conditions. This paper reveals that commonly used SOC estimation schemes are fundamentally flawed in providing the robustness of SOC estimation against model uncertainties. Parameter estimation methodologies and adaptive SOC estimation design are introduced in this paper to enhance SOC estimation accuracy and robustness. By a scrutiny of the impact of parameter variations on SOC estimation accuracy, the SOC–open-circuit-voltage mapping is identified to be the most critical function that must be accurately established. Identification algorithms are introduced, and their convergence properties are established. The integration of the identification algorithms and SOC estimation schemes lead to an adaptive SOC estimation framework that is superior over the existing methods in providing much improved accuracy and robustness. Experimental studies are conducted to validate the algorithms. [ABSTRACT FROM PUBLISHER]

This paper introduces identification algorithms for finite impulse response systems under quantized output observations and general quantized inputs. While asymptotically efficient algorithms for quantized identification under periodic inputs are available, their counterpart under general inputs has encountered technical difficulties and evaded satisfactory resolutions. Under quantized inputs, this paper resolves this issue with constructive solutions. A two-step algorithm is developed, which demonstrates desired convergence properties including strong convergence, mean-square convergence, convergence rates, asymptotic normality, and asymptotical efficiency in terms of the Cramér–Rao lower bound. Some essential conditions on input excitation are derived that ensure identifiability and convergence. It is shown that by a suitable selection of the algorithm’s weighting matrix, the estimates become asymptotically efficient. The strong and mean-square convergence rates are obtained. Optimal input design is given. Also the joint identification of noise distribution functions and system parameters is investigated. Numerical examples are included to illustrate the main results of this paper. [ABSTRACT FROM AUTHOR]

Highway platooning of vehicles has been identified as a promising framework in developing intelligent transportation systems. By autonomous or semi-autonomous vehicle control and intervehicle coordination, an appropriately managed platoon can potentially offer enhanced safety, improved highway utility, increased fuel economy, and reduced emissions. This paper is focused on quantitative characterization of the impact of communication information structures and contents on platoon safety. By comparing different information structures that combine front sensors, rear sensors, and wireless communication channels, as well as different information contents such as distances, speeds, and drivers' actions, we reveal a number of intrinsic relationships between vehicle coordination and communications in platoons. Typical communication standards and related communication latency are used as benchmark cases in this paper. The findings of this paper provide useful guidelines in sensor selections, communication resource allocations, and vehicle coordination. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a new method is introduced to enhance feedback robustness against communication gain uncertainties. The method employs a fundamental property in stochastic differential equations to add a scaled stochastic dither under which tolerable gain uncertainties can be much enlarged, beyond the traditional deterministic optimal gain margin. Algorithms, stability, convergence, and robustness are presented for first-order systems. Extension to higher-dimensional systems is further discussed. Simulation results are used to illustrate the merits of this methodology. [Copyright &y& Elsevier]

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]

Reliability of battery diagnosis depends on accurate estimation of the state of charge (SOC) and battery characterizing parameters including maximum capacity, internal impedance, polarization coefficients, and their probabilistic characterizations. This paper develops a framework that employs real-time operating data to estimate jointly the SOC and parameters, performs statistical analysis to derive quantitative diagnostic procedures with error analysis. Convergence of the algorithms, asymptotic distributions, and diagnosis reliability analysis are performed rigorously by using stochastic differential equations, central limit theorems, and large deviations principles. Simulated case studies and experimental data are used to illustrate the diagnosis algorithms and their capabilities. Experimental studies are conducted to verify the results. [ABSTRACT FROM PUBLISHER]

Establishing real-time models for electric motors is important when capturing authentic dynamic behavior of the motors to improve control performance, enhance robustness, and support diagnosis. Quantized sensors are less expensive, and remotely controlled motors mandate signal quantization. Such limitations on observations introduce challenging issues in motor parameter estimation. This paper develops estimators for model parameters of permanent-magnet direct current (PMDC) motors using quantized speed measurements. A typical linearized model structure of PMDC motors is used as a benchmark platform to demonstrate the technology and its key properties and benefits. Convergence properties are established. Simulations and experimental studies are performed to illustrate potential applications of the technology. [ABSTRACT FROM AUTHOR]

Background and Aims: EGFR activation and PKM2 expression are instrumental in tumorigenesis. EGFR activation regulates PKM2 functions in a subcellular compartment-dependent manner and promotes gene transcription and tumor growth. In addition, PKM2 is upregulated in EGFR-induced pathways in glioma malignancies. However, we found that PKM2 could also regulate the activity of the EGF/EGFR signaling pathway in gastric cancer cells. We aimed to define the biological mechanisms for PKM2 in regulating the cell motility and invasion. Methods: We employed stable transfection with short hairpin RNA to stably silence the expression of PKM2 in the BGC823, SGC7901 and AGS gastric cancer cell lines. The effects of PKM2 in vitro were determined by assessing cell migration and invasion. Immunohistochemical analysis was used to explore the relationship among PKM2 and other proteins. Results: Our results indicate that the knockdown of PKM2 decreased the activity of E-cadherin and enhanced the EGF/EGFR signaling pathway in the gastric cell lines BGC823 and SGC7901 that were positive for E-cadherin expression. However, in the undifferentiated gastric carcinoma cell line AGS, which lacks E-cadherin expression, PKM2 promoted cell migration and invasion. Immunohistochemical analyses showed that the levels of E-cadherin expression, ERK1/2 phosphorylation, and cytoplasmic PKM2 expression were correlated with each other. Conclusion:: PKM2 may play different roles in differently differentiated gastric cancer cell types, and this finding would be consistent with the previous clinical research. The results of our study reveal an important link between PKM2 and E-cadherin during EGFR-stimulated gastric cancer cell motility and invasion. [ABSTRACT FROM AUTHOR]

Abstract: Joint estimation of states and events in linear regime-switching systems is studied under irregular sampling schemes which stem from improved sampling and quantization methods for efficient utility of communication resources. Joint observability and sampling complexity are established, extending Shannon’s sampling theorem and our recent results on sampling complexity to joint estimation problems. Observer design and convergence analysis are conducted for systems under noisy observations. It is shown that our algorithms converge strongly with an error bound of the order . Simulation examples are included to illustrate potential usages of the algorithms. [Copyright &y& Elsevier]

Accurate estimation of the state of charge in battery systems is of essential importance for battery system management. Due to nonlinearity, high sensitivity of the inverse mapping from external measurements, and measurement errors, SOC estimation has remained a challenging task. This is further compounded by the fact that battery characteristic model parameters change with time and operating conditions. This paper introduces an adaptive nonlinear observer design that compensates nonlinearity and achieves better estimation accuracy. A two-time-scale signal processing method is employed to attenuate the effects of measurement noises on SOC estimates. The results are further expanded to derive an integrated algorithm to identify model parameters and initial SOC jointly. Simulations were performed to illustrate the capability and utility of the algorithms. Experimental verifications are conducted on Li-ion battery packs of different capacities under different load profiles. [ABSTRACT FROM AUTHOR]

Abstract: This work is concerned with tracking and system identification for time-varying parameters. The parameters are Markov chains and the observations are binary valued with noise corruption. To overcome the difficulties due to the limited measurement information, Wonham-type filters are developed first. Then, based on the filters, two popular estimators, namely, mean squares estimator (MSQ) and maximum posterior (MAP) estimator are constructed. For the mean squares estimator, we derive asymptotic normality in the sense of weak convergence and in the sense of strong approximation. The asymptotic normality is then used to derive error bounds. When the Markov chain is infrequently switching, we derive error bounds for MAP estimators. When the Markovian parameters are fast varying, we show that the averaged behavior of the parameter process can be derived from the stationary measure of the Markov chain and that can be estimated using empirical measures. Upper and lower error bounds on estimation errors are also established. [Copyright &y& Elsevier]

Abstract: This work is concerned with system identification of plants using quantized output observations. We focus on relationships between identification space and time complexities. This problem is of importance for system identification in which data-flow rates are limited due to computer networking, communications, wireless channels, etc. Asymptotic efficiency of empirical measure based algorithms yields a tight lower bound on identification accuracy. This bound is employed to derive a separation principle of space and time complexities and to study sensor threshold selection. Insights gained from these understandings provide a feasible approach for optimal utility of communication bandwidth resources in enhancing identification accuracy. [Copyright &y& Elsevier]

Abstract: This paper focuses on state reconstruction problems for systems whose outputs are measured only by binary-valued sensors. A binary-valued sensor is characterized by its switching threshold or post-switching location and by its output that indicates only whether the system output is above or below the threshold. The traditional passive-type state reconstruction that does not require input design will fail in general, even if the system has a full-rank observability matrix. It is shown that under controllability conditions and bounded uncertainty on the initial state, it is always possible to construct a causal input that will cause the output to cross the sensor threshold in a designated time interval. Information on the time instants of threshold crossing is then used to reconstruct the initial state. It is proved that if the system is observable, such a reconstruction is always possible if the eigenvalues of the system are all real valued. If the eigenvalues of the system contain only purely imaginary and non-repeating values, it is sufficient that threshold crossing occurs within a relatively small time interval. In general without constraints on system eigenvalues, an input can always be randomized to ensure that the state can be reconstructed with probability one. These results lead to an active state reconstruction algorithm. [Copyright &y& Elsevier]

Abstract: This work is concerned with identification of Wiener systems whose outputs are measured by binary-valued sensors. The system consists of a linear FIR (finite impulse response) subsystem of known order, followed by a nonlinear function with a known parametrization structure. The parameters of both linear and nonlinear parts are unknown. Input design, identification algorithms, and their essential properties are presented under the assumptions that the distribution function of the noise is known and the nonlinearity is continuous and invertible. It is shown that under scaled periodic inputs, identification of Wiener systems can be decomposed into a finite number of core identification problems. The concept of joint identifiability of the core problem is introduced to capture the essential conditions under which the Wiener system can be identified with binary-valued observations. Under scaled full-rank conditions and joint identifiability, a strongly convergent algorithm is constructed. The algorithm is shown to be asymptotically efficient for the core identification problem, hence achieving asymptotic optimality in its convergence rate. For computational simplicity, recursive algorithms are also developed. [Copyright &y& Elsevier]

Abstract: This paper studies identification of systems in which only quantized output observations are available. An identification algorithm for system gains is introduced that employs empirical measures from multiple sensor thresholds and optimizes their convex combinations. Strong convergence of the algorithm is first derived. The algorithm is then extended to a scenario of system identification with communication constraints, in which the sensor output is transmitted through a noisy communication channel and observed after transmission. The main results of this paper demonstrate that these algorithms achieve the Cramér–Rao lower bounds asymptotically, and hence are asymptotically efficient algorithms. Furthermore, under some mild regularity conditions, these optimal algorithms achieve error bounds that approach optimal error bounds of linear sensors when the number of thresholds becomes large. These results are further extended to finite impulse response and rational transfer function models when the inputs are designed to be periodic and full rank. [Copyright &y& Elsevier]

Abstract: System identification of plants with binary-valued output observations is of importance in understanding modeling capability and limitations for systems with limited sensor information, establishing relationships between communication resource limitations and identification complexity, and studying sensor networks. This paper resolves two issues arising in such system identification problems. First, regression structures for identifying a rational model contain non-smooth nonlinearities, leading to a difficult nonlinear filtering problem. By introducing a two-step identification procedure that employs periodic signals, empirical measures, and identifiability features, rational models can be identified without resorting to complicated nonlinear searching algorithms. Second, by formulating a joint identification problem, we are able to accommodate scenarios in which noise distribution functions are unknown. Convergence of parameter estimates is established. Recursive algorithms for joint identification and their key properties are further developed. [Copyright &y& Elsevier]

The essential issues of time complexity and probing signal selection are studied for persistent identification of linear time-invariant systems in a closed-loop setting. By establishing both upper and lower bounds on identification accuracy as functions of the length of observation, size of unmodeled dynamics, and stochastic disturbances, we demonstrate the inherent impact of unmodeled dynamics on identification accuracy, reduction of time complexity by stochastic averaging on disturbances, and probing capability of full rank periodic signals for closed-loop persistent identification. These findings indicate that the mixed formulation, in which deterministic uncertainty of system dynamics is blended with random disturbances, is beneficial to reduction of identification complexity. [Copyright &y& Elsevier]

The proliferation and myogenic differentiation of muscle stem cells (MuSCs) are important factors affecting muscle development and beef quality. There is increasing evidence that circRNAs can regulate myogenesis. We found a novel circRNA, named circRRAS2 that is significantly upregulated in the differentiation phase of bovine MuSCs. Here, we aimed to determine its roles in the proliferation and myogenic differentiation of these cells. The results showed that circRRAS2 was expressed in several bovine tissues. CircRRAS2 inhibited MuSCs proliferation and promoted myoblast differentiation. In addition, chromatin isolation by using RNA purification and mass spectrometry in differentiated muscle cells identified 52 RNA-binding proteins that could potentially bind to circRRAS2, in order to regulate their differentiation. The results suggest that circRRAS2 could be a specific regulator of myogenesis in bovine muscle. CircRRAS2 expression is higher in DM cells than in GM cells. CircRRAS2 could significantly inhibit the proliferation and apoptosis of bovine MuSCs. CircRRAS2 promotes the differentiation of bovine MuSCs into myotubes. CircRRAS2 may exert regulatory effects through multiple RNA binding proteins. [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]

This paper proposes a learning-based distributed optimization framework to enhance the resilience of distributed power system algorithms against communication failures. The autoregressive-moving-average (ARMA) model is used to estimate the missing states and control actions of neighboring agents during communication contingencies. The proposed framework allows agents to predict the future control actions of neighbors. Thus, even during complete loss of communication, agents can efficiently perform distributed optimization. We use the Distributed Optimal Frequency Control (DOFC) algorithm, which includes optimal power sharing to achieve frequency stability, as a benchmark application platform to show the effectiveness of the proposed framework. The theoretical findings are evaluated on two practical power systems. The results show that the ARMA-based DOFC algorithm can asymptotically reach the same convergence rate as the power system without communication interruptions. • Developing ARMA model to estimate the states and control actions of neighboring agents. • Embedding ARMA model into DOFC algorithm to mitigate communication failures. • Characterizing relationship between estimation errors and convergence rates of ARMA-based DOFC. • Developing a practical criterion for reliability of DOFC under communication uncertainties. • Illustrating the convergence rate of ARMA-based DOFC can asymptotically reach the CR lower bound. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of communication packet loss on Distributed Optimal Frequency Control (DOFC) in Alternating Current (AC) electric energy systems, populated with multiple clusters of hybrid producer–consumer (prosumer) agents. The paper first establishes rigorous relationships between the communication packet delivery ratio and the convergence rate of the proposed DOFC algorithm. This provides a foundation for resource allocation on communication systems to enhance the convergence speed of distributed optimization and control algorithms, such as DOFC, under noisy and disrupted communication systems. The paper develops a systematic approach to identify the best possible convergence rate over all possible algorithms, by introducing an algorithm that can achieve asymptotically the Cramér-Rao (CR) lower bound. This fundamental result links the information contents of data to the best possible mean-square estimation error. Simulation studies on an electric energy system validate the theoretical results. [ABSTRACT FROM AUTHOR]

Motivated by the seminal work of Dupire (2009) on functional Itô formulas, this work investigates asymptotic properties of systems represented by stochastic functional differential equations (SFDEs). Stability of general delay-dependent SFDEs is investigated using degenerate Lyapunov functionals, which are only positive semi-definite rather than positive definite as used in the classical work. This paper first establishes boundedness and regularity of SFDEs by using degenerate Lyapunov functionals. Then moment and almost sure exponential stabilities are obtained based on degenerate Lyapunov functionals and the semi-martingale convergence theorem. As an application of the stability criteria, consentability of stochastic multi-agent systems with nonlinear dynamics is studied. [ABSTRACT FROM AUTHOR]

The incremental capacity (IC) analysis method is widely used to analyze the aging origins and state of health (SOH) of lithium ion batteries. This paper analyzes the technical difficulties during the application of the IC analysis method at first. A universal capacity model based on charging curve is then proposed, which not only inherits the advantages of IC analysis method but also avoids the tedious data preprocessing procedure, to estimate SOH of lithium ion batteries. The feasibility and accuracy of the model are demonstrated. To verify the accuracy and flexibility of the proposed capacity model, it is applied on different types of lithium ion batteries including LiFePO 4 , LiNi 1 / 3 Co 1 / 3 Mn 1 / 3 O 2 , and Li 4 / 3 Ti 5 / 3 O 4 . Furthermore, the proposed capacity model is applied on the aged cells to validate the model accuracy during the whole life span of lithium ion batteries. The results show that the model error is less than 4% of the nominal capacity for each case. [ABSTRACT FROM AUTHOR]

This paper proposes an architectural solution to enhance the resilience of distributed decision-making and control in microgrids and smart grids with respect to communication delays. The paper develops a resource allocation framework to optimally place the multiple-input multiple-output (MIMO) communication technology on critical channels to reduce the communication bottle neck and expedite the convergence speed of distributed online optimization algorithms, which require convergence in the cyber network before the solution can be implemented on the physical grid. The paper shows that optimal placement of the MIMO technology can decrease communication transmission delays, which in turn improves the reliability of power system operational protocols, such as distributed frequency regulation. Illustrative case studies using data from real-world power systems show that only upgrading the critical communication channels is sufficient to achieve system-wide performance improvement. • Proposing an architectural solution to enhance the resilience of distributed optimization in smart grids with respect to communication delays. • Determining an upper bound on the communication delays under different MIMO communication architectures. • Developing a resource allocation algorithm to optimally place the MIMO technology and to increase the resilience of communication networks. [ABSTRACT FROM AUTHOR]

This work develops a generalization of the Goodwin–Lotka–Volterra models. The traditional model describes the evolution of the employment rate and the labor share. Motivated by recent developments on business cycles and Goodwin-type models, we consider a generalized Goodwin business cycle model with noise. We aim at investigating the impact of telegraph noise and white noise on the evolution of trajectories and the energy on trajectories of the model. It is proved that with the presence of the noises, all trajectories exit any compact subset of the open unit square D = ( 0 , 1 ) × ( 0 , 1 ) and the energy along trajectories can be arbitrarily large. Moreover, under certain additional conditions, the boundary of D is the stable limit cycle of the perturbed model by white noise. Noise impact on the period of business cycles is also discussed. Numerical examples are provided to demonstrate our findings. [ABSTRACT FROM AUTHOR]

This paper considers the problem of identifiability and parameter estimation of single-input–single-output, linear, time-invariant, stable, continuous-time systems under irregular and random sampling schemes. Conditions for system identifiability are established under inputs of exponential polynomial types and a tight bound on sampling density. Identification algorithms of Gauss–Newton iterative types are developed to generate convergent estimates. When the sampled output is corrupted by observation noises, input design, sampling times, and convergent algorithms are intertwined. Persistent excitation (PE) conditions for strongly convergent algorithms are derived. Unlike the traditional identification, the PE conditions under irregular and random sampling involve both sampling times and input values. Under the given PE conditions, iterative and recursive algorithms are developed to estimate the original continuous-time system parameters. The corresponding convergence results are obtained. Several simulation examples are provided to verify the theoretical results. [ABSTRACT FROM AUTHOR]