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The ranking of nodes in a network according to their centrality or ''importance'' is a classic problem that has attracted the interest of different scientific communities in the last decades. The COVID-19 pandemic has recently rejuvenated the interest in this problem, as the ranking may be used to decide who should be tested, or vaccinated, first, in a population of asymptomatic individuals. In this paper, we review classic methods for node ranking and compare their performance in a benchmark network that considers the community-based structure of society. The outcome of the ranking procedure is then used to decide which individuals should be tested, and possibly quarantined, first. Finally, we also review the extension of these ranking methods to weighted graphs and explore the importance of weights in a contact network by providing a toy model and comparing node rankings for this case in the context of disease spread. [ABSTRACT FROM AUTHOR]

In this paper, we examine the factors that influence the success of false data injection (FDI) attacks in the context of both cyber and physical styles of reinforcement. Existing research considers the FDI attack in the context of the ability to change a measurement in a static system only. However, successful attacks will require first intrusion into a system followed by construction of an attack vector that can bypass bad data detection to cause a consequence (such as overloading). Furthermore, the recent development of moving target defences (MTD) introduces dynamically changing system topology, which is beyond the capability of existing research to assess. In this way, we develop a full service framework for FDI risk assessment. The framework considers both the costs of system intrusion via a weighted graph assessment in combination with a physical, line overload-based vulnerability assessment under the existence of MTD. We present our simulations on a IEEE 14-bus system with an overlain RTU network to model the true risk of intrusion. The cyber model considers multiple methods of entry for the FDI attack including meter intrusion, RTU intrusion and combined style attacks. Post-intrusion, our physical reinforcement model analyses the required level of topology divergence to protect against a branch overload from an optimised attack vector. The combined cyber and physical index is used to represent the system vulnerability against FDIA. [ABSTRACT FROM AUTHOR]

We consider an intersection zone where autonomous vehicles (AVs) and human-driven vehicles (HDVs) can be simulteneously present. As a new vehicle arrives, the traffic controller needs to decide and suggest an optimal sequence of the vehicles which will exit the intersection zone. The traffic controller can inform the time at which an AV can cross the intersection; however, the traffic controller can not communicate with the HDVs, rather the HDVs can only be controlled using the traffic lights. We formulate the problem as an integer constrained nonlinear optimization problem. Since the number of possible combinations increases exponentially with the number of vehicles in the traffic system, we relax the original problem and proposes an algorithm which gives the optimal solution of the relaxed problem and yet only scales linearly with the number of vehicles in the system. The numerical validation shows that our algorithm outperforms the First-In-First-Out (FIFO) algorithm. [ABSTRACT FROM AUTHOR]

Harmonic current estimation is required in active power filters for compensation purposes. The most efficient way of calculating the total harmonic current up to the infinite order is to subtract the fundamental component from the distorted current. Direct determination of the fundamental component of a distorted current of mains frequency was realized recently with a third-order modulation integral observer. This article shows that using an exponential modulation function (Exp-MF) for the integral observer will: 1) significantly enhance the robustness of the observer against noise and 2) automatically remove the low-frequency envelope arising from the D/A and A/C sampling processes compared to previous polynomial modulation function. These new and advantageous features are supported with detailed analysis and experimental verification. The robust observer can be implemented in grid-connected power electronics circuits that require the instantaneous information of the fundamental and/or harmonic currents. Practical comparative tests with the adaptive notch filter and recursive discrete Fourier transform (DFT) methods in an active power filter have confirmed the good performance under both the steady and dynamic states of the proposed Exp-MF integral observer. [ABSTRACT FROM AUTHOR]

COVID-19 abatement strategies have risks and uncertainties which could lead to repeating waves of infection. We show—as proof of concept grounded on rigorous mathematical evidence—that periodic, high-frequency alternation of into, and out-of, lockdown effectively mitigates second-wave effects, while allowing continued, albeit reduced, economic activity. Periodicity confers (i) predictability, which is essential for economic sustainability, and (ii) robustness, since lockdown periods are not activated by uncertain measurements over short time scales. In turn—while not eliminating the virus—this fast switching policy is sustainable over time, and it mitigates the infection until a vaccine or treatment becomes available, while alleviating the social costs associated with long lockdowns. Typically, the policy might be in the form of 1-day of work followed by 6-days of lockdown every week (or perhaps 2 days working, 5 days off) and it can be modified at a slow-rate based on measurements filtered over longer time scales. Our results highlight the potential efficacy of high frequency switching interventions in post lockdown mitigation. All code is available on Github at https://github.com/V4p1d/FPSP%5fCovid19. A software tool has also been developed so that interested parties can explore the proof-of-concept system. Author summary: Why? The design of post-lockdown mitigation policies while vaccines are still not available is pressing now as new secondary waves of the virus have emerged in many countries (for example, in Spain, France, UK, Italy, Israel, and others), and as several of these countries grapple with the reintroduction of full lockdown measures. What do we do and find? We propose efficacious and realisable methods based on control theory to tame the complex behaviour of COVID-19 in well mixed populations. We achieve this through a policy of fast intermittent lockdown intervals with regular period. We illustrate how our approach offers a fundamentally new perspective on ways to design COVID-19 exit strategies from policies of total lockdown. Our theoretical results are also very general and apply to a wide range of epidemiological models. What do these findings mean? Unlike many other proposed abatement strategies, which have risks and uncertainties possibly leading to multiple waves of infection, we demonstrate that our proposed policies have the potential to suppress the virus outbreak, while at the same time allowing continued economic activity. These policies, while of practical significance, are built on rigorous theoretical results, which are to the best of our knowledge, new in mathematical epidemiology. An extensive validation is carried out using a detailed epidemic model validated on real COVID-19 data from Italy and published very recently in Nature Medicine. [ABSTRACT FROM AUTHOR]

Testing, tracking and tracing abilities have been identified as pivotal in helping countries to safely reopen activities after the first wave of the COVID-19 virus. Contact tracing apps give the unprecedented possibility to reconstruct graphs of daily contacts, so the question is: who should be tested? As human contact networks are known to exhibit community structure, in this paper we show that the Kemeny constant of a graph can be used to identify and analyze bridges between communities in a graph. Our 'Kemeny indicator' is the value of the Kemeny constant in the new graph that is obtained when a node is removed from the original graph. We show that testing individuals who are associated with large values of the Kemeny indicator can help in efficiently intercepting new virus outbreaks, when they are still in their early stage. Extensive simulations provide promising results in early identification and in blocking the possible 'super-spreaders' links that transmit disease between different communities. [ABSTRACT FROM AUTHOR]

Over the past few decades, intelligentization, supported by artificial intelligence (AI) technologies, has become an important trend for industrial manufacturing, accelerating the development of smart manufacturing. In modern industries, standard AI has been endowed with additional attributes, yielding the so-called industrial artificial intelligence (IAI) that has become the technical core of smart manufacturing. AI-powered manufacturing brings remarkable improvements in many aspects of closed-loop production chains from manufacturing processes to end product logistics. In particular, IAI incorporating domain knowledge has benefited the area of production monitoring considerably. Advanced AI methods such as deep neural networks, adversarial training, and transfer learning have been widely used to support both diagnostics and predictive maintenance of the entire production process. It is generally believed that IAI is the critical technologies needed to drive the future evolution of industrial manufacturing. This article offers a comprehensive overview of AI-powered manufacturing and its applications in monitoring. More specifically, it summarizes the key technologies of IAI and discusses their typical application scenarios with respect to three major aspects of production monitoring: fault diagnosis, remaining useful life prediction, and quality inspection. In addition, the existing problems and future research directions of IAI are also discussed. This article further introduces the papers in this focused section on AI-based monitoring in smart manufacturing by weaving them into the overview, highlighting how they contribute to and extend the body of literature in this area. [ABSTRACT FROM AUTHOR]

This paper presents a novel framework for the problem of target localization based on the range information collected by a single mobile agent. The proposed methodology exploits the algebra of Volterra integral operators to annihilate the influence of initial conditions on the transient phase, thus achieving a deadbeat performance. The robustness properties against additive measurement perturbations are analyzed, and the bias caused by the time discretization is characterized as well. Extensive simulation results and comparisons are provided showing the effectiveness of the proposed technique in coping with both stationary and drifting targets. [ABSTRACT FROM AUTHOR]

Parallel operation of multiple grid-connected power converters through LCL filters is known to have the potential problem of triggering oscillations in the ac mains. Such oscillatory frequencies are not integral multiples of the fundamental frequency and, hence, form a new source of interharmonics. Early detection of such oscillations is essential for the parallel power converters to move out of the unstable zone. This paper presents an online observer-based algorithm that can perform fast detection of interharmonics within a specified frequency band. The algorithm has been adopted in a specific and reduced form from an integral observer algorithm for detection of fundamental and interharmonic voltage components in the ac mains. A new method based on the kernel signal for fast interharmonic detection is proposed and practically verified. It has been implemented in a digital controller to detect oscillations such as those occurring between two grid-connected power converters. The practical results indicate that the algorithm can locate such frequency within the specific frequency band within one mains cycle. [ABSTRACT FROM AUTHOR]

Summary: This paper presents a distributed integrated fault diagnosis and accommodation scheme for leader‐following formation control of a class of nonlinear uncertain second‐order multi‐agent systems. The fault model under consideration includes both process and actuator faults, which may evolve abruptly or incipiently. The time‐varying leader communicates with a small subset of follower agents, and each follower agent communicates to its directly connected neighbors through a bidirectional network with possibly asymmetric weights. A local fault diagnosis and accommodation component are designed for each agent in the distributed system, which consists of a fault detection and isolation module and a reconfigurable controller module comprised of a baseline controller and two adaptive fault‐tolerant controllers, activated after fault detection and after fault isolation, respectively. By using appropriately the designed Lyapunov functions, the closed‐loop stability and asymptotic convergence properties of the leader‐follower formation are rigorously established under different modes of the fault‐tolerant control system. [ABSTRACT FROM AUTHOR]

The derivative estimation problem is addressed in this paper by using Volterra integral operators which allow to obtain the estimates of the time derivatives with fast convergence rate. A deadbeat state observer is used to provide the estimates of the derivatives with a given fixed-time convergence. The estimation bias caused by modelling error is characterised herein as well as the ISS property of the estimation error with respect to the measurement perturbation. A number of numerical examples are carried out to show the effectiveness of the proposed differentiator also including comparisons with some existing methods. [ABSTRACT FROM AUTHOR]

This paper proposes a rigorous anomaly detection scheme, developed to spot power system operational changes which are inconsistent with the models used by operators. This novel technique relies on a state observer, with guaranteed estimation error convergence, suitable to be implemented in real time, and it has been developed to fully address this important issue in power systems. The proposed method is fitted to the highly nonlinear characteristics of the network, with the states of the nonlinear generator model being estimated by means of a linear time-varying estimation scheme. Given the reliance of the existing dynamic security assessment tools in industry on nominal power system models, the suggested methodology addresses cases when there is deviation from assumed system dynamics, enhancing operators’ awareness of system operation. It is based on a decision scheme relying on analytical computation of thresholds, not involving empirical criteria which are likely to introduce inaccurate outcomes. Since false-alarms are guaranteed to be absent, the proposed technique turns out to be very useful for system monitoring and control. The effectiveness of the anomaly detection algorithm is shown through detailed realistic case studies in two power system models. [ABSTRACT FROM AUTHOR]

Harmonics detection is a critical element of active power filters. A previous review has shown that the recursive discrete Fourier transform and the instantaneous p-q theory are effective solutions to extracting power harmonics in single-phase and three-phase power systems, respectively. This paper presents the operating principle of a new modulation function integral observer algorithm that offers a fast solution for the extraction of the fundamental current and the total harmonic current when compared with existing methods. The proposed method can be applied to both single- and three-phase systems. The observer-based algorithm has an advantageous feature of being able to be tuned offline for a specific application, having fast convergence, and producing estimated fundamental component with high circularity. It has been tested with both simulations and practical experiments for extracting the total harmonic current in a highly efficient manner. The results have confirmed that the proposed tool offers a new and highly effective alternative for the smart grid industry. [ABSTRACT FROM AUTHOR]

In this paper, a new method to estimate the mutual inductance and load resistance in a series–series compensated wireless power transfer system is presented. Reasonably accurate estimations can be obtained from measurements of the input voltage and current obtained at one operating frequency only. The proposal can be used to dynamically monitor both the coupling relationship between the transmitter and receiver coils and the load conditions without any direct measurement on the receiver side. It can also be used as a simple method to measure the mutual inductance of any pair of coupled coils. A novel impedance spectrum analysis method is further presented to show that series–series compensation has special characteristics in its input impedance spectrum. Experimental results with acceptable tolerance are included to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper describes a method for monitoring multiple loads from the front end of a wireless power transfer system without using any wireless communication systems. A mathematical approach based on scanning the frequency around the resonant frequency has been developed for deriving the load conditions. The proposal requires only information of the input voltage and current, thereby eliminating the requirements of using wireless communication systems for feedback control. The proposal has been practically confirmed in hardware prototype with good results. [ABSTRACT FROM PUBLISHER]

The problem of estimating the amplitude, frequency, and phase of an unknown sinusoidal signal from a noisy-biased measurement is addressed in this paper by a family of parallel prefiltering schemes. The proposed methodology consists in using a pair of linear filters of specified order to generate a suitable number of auxiliary signals that are used to estimate-in an adaptive way-the frequency, the amplitude, and the phase of the sinusoid. Increasing the order of the prefilters improves the noise immunity of the estimator, at the cost of an increase of the computational complexity. Among the whole family of estimators realizable by varying the order of the filters, the simple parallel prefilters of orders 2 + 2 and 3 + 3 are discussed in detail, being the most attractive from the implementability point of view. The behavior of the two algorithms with respect to bounded external disturbances is characterized by input-to-state stability arguments. Finally, the effectiveness of the proposed technique is shown both by comparative numerical simulations and by a real experiment addressing the estimation of the frequency of the electrical mains from a noisy voltage measurement. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]