Performance Analysis of MCOD Algorithm With Varying Parameters

Outlier detection has become one of the prominent and most needed technologies these days. Outliers can be anything in our daily life like credit card fraud, intrusion in a network, aberrant condition detection in condition monitoring data. There are numerous methodologies to detect outliers. In the past few years many tools have come up in the outlier detection in data streams. In this chapter, the authors discuss the tool MOA (massive online analysis) to detect anomalies and the best performing algorithm amongst the prescribed algorithms of MOA. The authors elaborately discuss that MCOD (micro-cluster-based algorithm) is one of the best in the prescribed algorithms of the MOA (massive online analysis) tool which outperforms all other algorithms. In this paper, the authors will deeply discuss the performance of MCOD algorithm. The authors will also discuss which factor of MCOD separates its performance from others and also what the different parameters that influence the performance of MCOD are.

Almost everywhere convergence and divergence of Cesàro means with varying parameters of Walsh–Fourier series

In the present paper, we prove the almost everywhere convergence and divergence of subsequences of Cesàro means with zero tending parameters of Walsh–Fourier series.

Online Identification of Time-Variant Structural Parameters Under Unknown Inputs Basing On Extended Kalman Filter

To date, a number of parameter identification methods have been developed for the purpose of structural health monitoring and vibration control. Among them, the extended Kalman filter (EKF) series methods are attractive in view of the efficient unbiased estimation in recursive manner. However, most of these methods are performed on the premise that the parameters are time-invariant and/or the loadings are known. To circumvent the aforementioned limitations, an online EKF with unknown input (OEKF-UI) approach is proposed in this paper for the identification of time-varying parameters and the unknown excitation. A revised observation equation is obtained with the aid of projection matrix. To capture the changes of structural parameters in real-time, an online tracking matrix (OTM) associated with the time-varying parameters is introduced and determined via an optimization procedure. Then, based on the principle of EKF, the recursive solution of structural states including the time-variant parameters can be analytically derived. Finally, using the estimated structural states, the unknown inputs are identified by means of least-squares estimation (LSE) at the same time-step. The effectiveness of the proposed approach is validated via linear and nonlinear numerical examples with the consideration of parameters being varied abruptly.

Spectral graph theory of brain oscillations -- revisited and improved

Mathematical modeling of the relationship between the functional activity and the structural wiring of the brain has largely been undertaken using non-linear and biophysically detailed mathematical models with regionally varying parameters. While this approach provides us a rich repertoire of multistable dynamics that can be displayed by the brain, it is computationally demanding. Moreover, although neuronal dynamics at the microscopic level are nonlinear and chaotic, it is unclear if such detailed nonlinear models are required to capture the emergent meso- (regional population ensemble) and macro-scale (whole brain) behavior, which is largely deterministic and reproducible across individuals. Indeed, recent modeling effort based on spectral graph theory has shown that an analytical model without regionally varying parameters can capture the empirical magnetoencephalography frequency spectra and the spatial patterns of the alpha and beta frequency bands accurately. In this work, we demonstrate an improved hierarchical, linearized, and analytic spectral graph theory-based model that can capture the frequency spectra obtained from magnetoencephalography recordings of resting healthy subjects. We reformulated the spectral graph theory model in line with classical neural mass models, therefore providing more biologically interpretable parameters, especially at the local scale. We demonstrated that this model performs better than the original model when comparing the spectral correlation of modeled frequency spectra and that obtained from the magnetoencephalography recordings. This model also performs equally well in predicting the spatial patterns of the empirical alpha and beta frequency bands.

Swarm Behaviour Optimisation Methods Based on an Original Algorithm

This article presents an authorial swarm algorithm that performs coverage tasks using the Sweep Coverage method. The presented solution assumes stochastic movement of the objects in the swarm which allows them to be simple ones. Our goal was to find an optimal number of objects in the swarm. The main evaluated factors are time and energy consumption. Changing input data allowed us to designate different cases and to examine the influence of varying parameters of a single boid on a whole swarm behaviour.