Cubature formulae for the gaussian weight. Some old and new rules.

In this paper we review some of the main known facts about cubature rules to approximate integrals over domains in R-n, in particular with respect to the Gaussian weight w(x) = e(-xTx); where x = (x(1); ... ; x(n)) is an element of R-n. Some new rules are also presented. Taking into account the well-known issue of the "curse of dimensionality", our aim is at providing rules with a certain degree of algebraic precision and a reasonably small number of nodes as well as an acceptable stability. We think that the methods used to construct these new rules are of further applicability in the field of cubature formulas. The efficiency of new and old rules are compared by means of several numerical experiments

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Meta Heuristics based Machine Learning and Neural Mass Modelling Allied to Brain Machine Interface

New understanding of the brain function and increasing availability of low-cost-non-invasive electroencephalograms (EEGs) recording devices have made brain-computer-interface (BCI) as an alternative option to augmentation of human capabilities by providing a new non-muscular channel for sending commands, which could be used to activate electronic or mechanical devices based on modulation of thoughts. In this project, our emphasis will be on how to develop such a BCI using fuzzy rule-based systems (FRBSs), metaheuristics and Neural Mass Models (NMMs). In particular, we treat the BCI system as an integrated problem consisting of mathematical modelling, machine learning and classification. Four main steps are involved in designing a BCI system: 1) data acquisition, 2) feature extraction, 3) classification and 4) transferring the classification outcome into control commands for extended peripheral capability. Our focus has been placed on the first three steps. This research project aims to investigate and develop a novel BCI framework encompassing classification based on machine learning, optimisation and neural mass modelling. The primary aim in this project is to bridge the gap of these three different areas in a bid to design a more reliable and accurate communication path between the brain and external world. To achieve this goal, the following objectives have been investigated: 1) Steady-State Visual Evoked Potential (SSVEP) EEG data are collected from human subjects and pre-processed; 2) Feature extraction procedure is implemented to detect and quantify the characteristics of brain activities which indicates the intention of the subject.; 3) a classification mechanism called an Immune Inspired Multi-Objective Fuzzy Modelling Classification algorithm (IMOFM-C), is adapted as a binary classification approach for classifying binary EEG data. Then, the DDAG-Distance aggregation approach is proposed to aggregate the outcomes of IMOFM-C based binary classifiers for multi-class classification; 4) building on IMOFM-C, a preference-based ensemble classification framework known as IMOFM-CP is proposed to enhance the convergence performance and diversity of each individual component classifier, leading to an improved overall classification accuracy of multi-class EEG data; and 5) finally a robust parameterising approach which combines a single-objective GA and a clustering algorithm with a set of newly devised objective and penalty functions is proposed to obtain robust sets of synaptic connectivity parameters of a thalamic neural mass model (NMM). The parametrisation approach aims to cope with nonlinearity nature normally involved in describing multifarious features of brain signals

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Advances and Technologies in High Voltage Power Systems Operation, Control, Protection and Security

The electrical demands in several countries around the world are increasing due to the huge energy requirements of prosperous economies and the human activities of modern life. In order to economically transfer electrical powers from the generation side to the demand side, these powers need to be transferred at high-voltage levels through suitable transmission systems and power substations. To this end, high-voltage transmission systems and power substations are in demand. Actually, they are at the heart of interconnected power systems, in which any faults might lead to unsuitable consequences, abnormal operation situations, security issues, and even power cuts and blackouts. In order to cope with the ever-increasing operation and control complexity and security in interconnected high-voltage power systems, new architectures, concepts, algorithms, and procedures are essential. This book aims to encourage researchers to address the technical issues and research gaps in high-voltage transmission systems and power substations in modern energy systems

Modeling and Optimization of Dynamical Systems in Epidemiology using Sparse Grid Interpolation

Infectious diseases pose a perpetual threat across the globe, devastating communities, and straining public health resources to their limit. The ease and speed of modern communications and transportation networks means policy makers are often playing catch-up to nascent epidemics, formulating critical, yet hasty, responses with insufficient, possibly inaccurate, information. In light of these difficulties, it is crucial to first understand the causes of a disease, then to predict its course, and finally to develop ways of controlling it. Mathematical modeling provides a methodical, in silico solution to all of these challenges, as we explore in this work. We accomplish these tasks with the aid of a surrogate modeling technique known as sparse grid interpolation, which approximates dynamical systems using a compact polynomial representation. Our contributions to the disease modeling community are encapsulated in the following endeavors. We first explore transmission and recovery mechanisms for disease eradication, identifying a relationship between the reproductive potential of a disease and the maximum allowable disease burden. We then conduct a comparative computational study to improve simulation fits to existing case data by exploiting the approximation properties of sparse grid interpolants both on the global and local levels. Finally, we solve a joint optimization problem of periodically selecting field sensors and deploying public health interventions to progressively enhance the understanding of a metapopulation-based infectious disease system using a robust model predictive control scheme

Energy Harvesting and Energy Storage Systems

This book discuss the recent developments in energy harvesting and energy storage systems. Sustainable development systems are based on three pillars: economic development, environmental stewardship, and social equity. One of the guiding principles for finding the balance between these pillars is to limit the use of non-renewable energy sources

Data-Driven Approach based on Deep Learning and Probabilistic Models for PHY-Layer Security in AI-enabled Cognitive Radio IoT.

PhD Theses.Cognitive Radio Internet of Things (CR-IoT) has revolutionized almost every eld of life and reshaped the technological world. Several tiny devices are seamlessly connected in a CR-IoT network to perform various tasks in many applications. Nevertheless, CR-IoT su ers from malicious attacks that pulverize communication and perturb network performance. Therefore, recently it is envisaged to introduce higher-level Arti cial Intelligence (AI) by incorporating Self-Awareness (SA) capabilities into CR-IoT objects to facilitate CR-IoT networks to establish secure transmission against vicious attacks autonomously. In this context, sub-band information from the Orthogonal Frequency Division Multiplexing (OFDM) modulated transmission in the spectrum has been extracted from the radio device receiver terminal, and a generalized state vector (GS) is formed containing low dimension in-phase and quadrature components. Accordingly, a probabilistic method based on learning a switching Dynamic Bayesian Network (DBN) from OFDM transmission with no abnormalities has been proposed to statistically model signal behaviors inside the CR-IoT spectrum. A Bayesian lter, Markov Jump Particle Filter (MJPF), is implemented to perform state estimation and capture malicious attacks. Subsequently, GS containing a higher number of subcarriers has been investigated. In this connection, Variational autoencoders (VAE) is used as a deep learning technique to extract features from high dimension radio signals into low dimension latent space z, and DBN is learned based on GS containing latent space data. Afterward, to perform state estimation and capture abnormalities in a spectrum, Adapted-Markov Jump Particle Filter (A-MJPF) is deployed. The proposed method can capture anomaly that appears due to either jammer attacks in transmission or cognitive devices in a network experiencing di erent transmission sources that have not been observed previously. The performance is assessed using the receiver