The neutral grounding in power distribution system is an important aspect for earth fault protection, power supply reliability and safety. The performance varies greatly with different grounding methods by which the protective effect presents various results with identical impedance of single phase earth fault. Arguments for better neutral protection has been continued in the distribution field for decades, unfortunately, there is still not a conclusion due to the discussions lacking of a unified modelling or theory of neutral groundings. Thus, the understanding of neutral grounding in most countries differs considerably. Surprisingly solid/isolated grounding in some countries is still considered as a mainstream grounding method in today’s distribution grids, likewise, some utilities are still persisting on adopting resistance grounding to pursue to improve detection sensitivity and reliability, and so on. In this paper, a unified theory is proposed to shed light on the neutral groundings within one unprecedented modelling by which neutral groundings can be compared and evaluated quantitatively for the first time in the history of power distribution field perhaps.

Corrective transmission system operation can help integrate more renewable energy sources and save redispatch costs by providing a higher utilization of the power grid. However, reliable and fast provision of flexibility are key to achieve corrective operation. This work develops a new method to determine if flexibility from distribution grids is available on transmission corridors when needed. An analysis of the German energy system in the year 2030 is performed to estimate the potential of different flexibility options and shows the potential flexibility distribution systems can contribute to a corrective transmission system operation.

Currently, the COVID‐19 has directly affected the millions of humans lives. The symptoms of the disease involving fever, malaise, chest infection, and breathing difficulties, were identified, and its existence is continuously becoming restructured. The World Health Organization (WHO) had mentioned the wide diagnostics test besides COVID-19 that would also assist medical facilities to recognize infectious diseases as well as currently focusing efficiently on preventing and afterward defeating this viral disease. The infection is usually transmitted among human beings in direct contact, greatest through the liquid bubbles generated through cough, sneeze, or speaking. This paper reviews the COVID 19 pandemic, its history, current updates, contact tracing applications, and use of emerging technologies like the Internet of Things (IoT) and Blockchain for stopping the spreading and provide service online to the patient from a distance.

Graphs are important data representations for describing objects and their relationships, which appear in a wide diversity of real-world scenarios. As one of a critical problem in this area, graph generation considers learning the distributions of given graphs and generating more novel graphs. Owing to its wide range of applications, generative models for graphs have a rich history, which, however, are traditionally hand-crafted and only capable of modeling a few statistical properties of graphs. Recent advances in deep generative models for graph generation is an important step towards improving the fidelity of generated graphs and paves the way for new kinds of applications. This article provides an extensive overview of the literature in the field of deep generative models for graph generation. Firstly, the formal definition of deep generative models for the graph generation as well as preliminary knowledge is provided. Secondly, two taxonomies of deep generative models for unconditional, and conditional graph generation respectively are proposed; the existing works of each are compared and analyzed. After that, an overview of the evaluation metrics in this specific domain is provided. Finally, the applications that deep graph generation enables are summarized and five promising future research directions are highlighted.

Events are occurrences in specific locations, time, and semantics that nontrivially impact either our society or the nature, such as earthquakes, civil unrest, system failures, pandemics, and crimes. It is highly desirable to be able to anticipate the occurrence of such events in advance in order to reduce the potential social upheaval and damage caused. Event prediction, which has traditionally been prohibitively challenging, is now becoming a viable option in the big data era and is thus experiencing rapid growth, also thanks to advances in high performance computers and new Artificial Intelligence techniques. There is a large amount of existing work that focuses on addressing the challenges involved, including heterogeneous multi-faceted outputs, complex (e.g., spatial, temporal, and semantic) dependencies, and streaming data feeds. Due to the strong interdisciplinary nature of event prediction problems, most existing event prediction methods were initially designed to deal with specific application domains, though the techniques and evaluation procedures utilized are usually generalizable across different domains. However, it is imperative yet difficult to cross-reference the techniques across different domains, given the absence of a comprehensive literature survey for event prediction. This paper aims to provide a systematic and comprehensive survey of the technologies, applications, and evaluations of event prediction in the big data era. First, systematic categorization and summary of existing techniques are presented, which facilitate domain experts’ searches for suitable techniques and help model developers consolidate their research at the frontiers. Then, comprehensive categorization and summary of major application domains are provided to introduce wider applications to model developers to help them expand the impacts of their research. Evaluation metrics and procedures are summarized and standardized to unify the understanding of model performance among stakeholders, model developers, and domain experts in various application domains. Finally, open problems and future directions for this promising and important domain are elucidated and discussed.

This paper deals with the classical question of estimating the achievable resolution in terms of the configuration parameters in inverse source problems. In particular, the study focuses on the case of a planar surface magnetic current which is to be reconstructed from near-field observed over a bounded rectangular aperture parallel to the source domain. Here, the plan is to work out a resolution estimation that precisely captures the spatially varying behaviour entailed by the near-field and aspect-limited configuration. To this end, the pertinent radiation operator is inverted by an adjoint inversion scheme (a backpropagation- like method) and the corresponding point-spread function is analytically estimated. Numerical examples show that the derived resolution estimation clearly points out the role of the geometrical parameters of the configuration and it is more accurate than other literature results.

Preprint of paper On the usefulness of map-based dashboards for decision making. Submitted to IEEE Transactions on visualizations and computer graphics, July 2020

Mobile chargers have greatly promoted the wireless rechargeable sensor networks (WRSNs). While most recent works have focused on recharging the WRSNs in an on-demand fashion, little attention has been paid on joint consideration of multiple mobile chargers (MCs) and multi-node energy transfer for determining the charging schedule of energy-hungry nodes. Moreover, most of the schemes leave out the contemplation of multiple network parameters while making scheduling decisions and even they overlook the issue of ill-timed charging response to the nodes with uneven energy consumption rates. In this paper, we address the aforesaid issues together and propose a novel scheduling scheme for on-demand charging in WRSNs. We first present an efficient network partitioning method for distributing the MCs so as to fairly balance their workload. We next adopt the fuzzy logic which blends various network parameters for determining the charging schedule of the MCs. We also formulate an expression to determine the charging threshold for the nodes that vary depending on their energy consumption rate. Extensive simulations are conducted to demonstrate the effectiveness and competitiveness of our scheme. The comparison results reveal that the proposed scheme improves charging performance compared to the state-of-the-art schemes with respect to various performance metrics.

In this paper, for minimizing the cost from the ocean generator power production by optimizing the operation and maintenance (O&M) policy over an infinite time horizon, while considering the uncertainty of the renewable sources and components failure behaviors, we develop a self-healing framework for ocean energy systems. It consists of three major modules: data manipulation, health assessment, and decision-making. Specifically, a graph-theoretic approach is first proposed for ocean generator health monitoring utilizing multivariate time-series data, then, reinforcement learning (RL) based technique exploits the health states of the system that provides decision support for optimal O&M management.

This paper presents a decentralized control scheme for voltage balancing and power sharing in bipolar dc microgrids. This relies on utilizing a converter topology which offers three levels of output voltage availability with the key features of boosting the input voltage and balancing the output voltages. This converter makes it possible to further improve the structure of bipolar dc microgrids as it does not require a central voltage balancer. Small-signal analysis is done and system transfer functions are derived. Based on the RGA concept the highly coupled input-output pairs are found which helps with replacing the MIMO control system of the converter by two SISO systems. The appropriate voltage and current controllers are designed based on SISO principles. Moreover, a double droop control method is proposed which fulfills the simultaneous power sharing and voltage regulation of DG units in the host microgrid. The effectiveness of the proposed control strategy is demonstrated through simulation studies conducted on an islanded bipolar dc microgrid involving unbalanced loads, while the voltage balancing of the bipolar dc microgrid and the power sharing accuracy are evaluated.

The clustered delay line channel model, in which each cluster consists of a number of rays, is widely used for link-level evaluations in mobile communications. Multiple parameters of each ray, including delay, amplitude, cross polarization ratio (XPR), initial phases of four polarization combinations and the azimuth and elevation angles of arrival and departure, shall be known. These parameters are measured using a channel sounder. The number of rays in every cluster is usually greater than the number of elements in the antenna array of the channel sounder, which represents a challenging issue in multipolarized channel measurements. A new subspace estimation method based on the broadband extended array response of an electromagnetic vector antenna array is proposed to resolve a large number of rays. The interelement spacing of the array can be greater than half the carrier wavelength, which reduces interelement coupling and simplifies the array design, especially for millimeter wave bands. First, the delay of each cluster is estimated using the reference antenna element. Then, the 2D angles of every ray are estimated using the classic rank-deficient multiple signal classification (MUSIC). Lastly, the initial phases, XPR and amplitude of every ray is estimated. Simulation results validate the proposed method.

The well-known Lighthill-Whitham-Richards (LWR) theory is the fundamental pillar for most macroscopic traffic models. In the past, many methods were developed to numerically derive solutions for LWR problems. Examples for such numerical solution schemes are the cell transmission model, the link transmission model, and the variational theory (VT) of traffic flow. So far, the latter framework found applications in the fields of traffic modelling, macroscopic fundamental diagram estimation, multi-modal traffic analyses, and data fusion. However, these studies apply VT only at the link or corridor level. To the best of our knowledge, there is no methodology yet to apply VT at the network level. We address this gap by developing a VT-based framework applicable to networks. Our model allows us to account for source terms (e.g. inflows and outflows at intersections) and the propagation of spillbacks between adjacent corridors consistent with kinematic wave theory. We show that the trajectories extracted from a microscopic simulation fit the predicted traffic states from our model for a simple intersection with both source terms and spillbacks. We also use this simple example to illustrate the accuracy of the proposed model. Additionally, we apply our model to the Sioux Falls network and again compare the results to those from a microscopic simulation. Our results indicate a close fit of traffic states, but with substantially lower computational cost. The developed methodology is useful for network-wide traffic state estimations in real-time, or other applications within a model-based optimization framework.

This paper investigates the consequences of the information-theoretic result that representations of numbers in base-e are most efficient. Since theories on complex system behavior in both natural and physical systems assume that Nature is optimal, as is done, for example, in the principle of least action, natural representations must be to the base e. Another way to interpret this fact is to take e as the information dimension of the data space. Some implications of this noninteger dimensionality are investigated. The approximate equivalent to such a space is the Menger sponge in which the recursion is taken to be random.

Artificial Intelligence is employed by various scientists, universities, private firms to fight COVID – 19. AI has started to enter healthcare at a dramatic pace, owing to the current pandemic. Methods like CT scan to detect COVID related Pneumonia in the lungs, to search for new molecules, and to aid the epidemiologists who tracked the disease early on. New tracking methods are being utilized to contain the spread but are we depending too much? Will AI help or fight against us? Is this the wolf of data collection dressed in the lamb’s fleece of a solution to the pandemic? Artificial Intelligence (AI) is helping to fight COVID – 19 but shouldn’t be expected to solve the crisis on its own.

This work exploits Riemannian manifolds to build a sequential-clustering framework able to address a wide variety of clustering tasks in dynamic multilayer (brain) networks via the information extracted from their nodal time-series. The discussion follows a bottom-up path, starting from feature extraction from time-series and reaching up to Riemannian manifolds (feature spaces) to address clustering tasks such as state clustering, community detection (a.k.a. network-topology identification), and subnetwork-sequence tracking. Kernel autoregressive-moving-average modeling and kernel (partial) correlations serve as case studies of generating features in the Riemannian manifolds of Grassmann and positive-(semi)definite matrices, respectively. Feature point-clouds form clusters which are viewed as submanifolds according to Riemannian multi-manifold modeling. A novel sequential-clustering scheme of Riemannian features is also established: feature points are first sampled in a non-random way to reveal the underlying geometric information, and, then, a fast sequential-clustering scheme is brought forth that takes advantage of Riemannian distances and the angular information on tangent spaces. By virtue of the landmark points and the sequential processing of the Riemannian features, the computational complexity of the framework is rendered free from the length of the available time-series data. The effectiveness and computational efficiency of the proposed framework is validated by extensive numerical tests against several state-of-the-art manifold-learning and brain-network-clustering schemes on synthetic as well as real functional-magnetic-resonance-imaging (fMRI) and electro-encephalogram (EEG) data.

Distributed Denial of Service attack (DDoS) is recognized to be one of the catastrophic attacks against various digital communication entities. Software-defined networking (SDN) is an emerging technology for computer networks that uses open protocols for controlling switches and routers placed at the network edges by using specialized open programmable interfaces. In this paper, a detailed study on DDoS threats prevalent in SDN is presented. Firstly, SDN features are examined from the perspective of security, and then, a discussion on assessment of SDN security features is done. Further, two viewpoints towards protecting the networks against DDoS attacks are elaborated. In the first view, SDN utilizes its abilities to secure the conventional networks. In the second view, SDN may become a victim of the threats itself because of the centralized control mechanism. The main focus of this research work is towards discovering critical security implications in SDN while reviewing the current ongoing research studies. By emphasizing the available state of the art techniques, an extensive review towards the advancement of the SDN security is provided to the researchers and IT communities.

Artificial Intelligence is a cutting-edge technology expanding very quickly into every industry. It has made its way into structural engineering and it has shown its benefits in predicting structural performance as well as saving modelling and experimenting time. This paper is the first one (out of three) of a broader research where artificial intelligence was applied to the stability and dynamic analyzes of steel grid-shells. In that study, three Artificial Neural Networks (ANN) with 8 inputs were independently designed for the prediction of a single target variable, namely: (i) the critical buckling factor for uniform loading (i.e. over the entire roof), (ii) the critical buckling factor for uniform loading over half of the roof, and (iii) the fundamental frequency of the structure. This paper addresses target variable (i). The ANN simulations were based on 1098-point datasets obtained via thorough finite element analyzes. The proposed ANN for the prediction of the critical buckling factor in steel grid-shells under uniform loading yields mean and maximum errors of 1.1% and 16.3%, respectively, for all 1098 data points. Only in 10.6% of those examples (points), the prediction error exceeds 3%.

An adaptive lighting system can operate in real-time by adjusting its output through a decision-making algorithm based on data mining techniques.