Dynamic system linear models and Bayes classifier for time series classification in promoting sustainabilitys

Research purpose: The current work introduces a novel method for time series discriminant analysis (DA). Proposing a version for the Bayes classifier employing Dynamic Linear Models, which we denote by BCDLM This article explores the application of DLMs and the Bayes Classifier in time series classification to promote application in sustainability across diverse sectors. Method: This paper presents some computer simulation studies in which we generate four different scenarios corresponding to time series observations from various Dynamic Linear Models (DLMs). In Discriminant Analysis, we investigated strategies for estimating variance in models and compared the performance of the BCDLM with other common classifiers. Such datasets are composed of real-time series (data from SONY AIBO Robot and spectrometry of coffee types) and pseudo-time series (data from Swedish leaves adapted for time series). We also point out that algorithm was used to determine training and test sets in real-world applications. Results: Considering the real-time series examined in this paper, The results obtained indicate that the parametric approach developed represents a promising alternative for this class of DA problems, with observations of time series in a situation that is quite difficult in practice when we have series with large sizes with respect to the number of observations in the classes, even though more thorough studies are required. Conclusions: It concludes that the BCDLM performed comparably to the results of the classifiers 1NN, RDA, NBND and NBK and superior to the methods LDA and QDA. This offers a powerful combination for time series classification, enabling accurate predictions and informed decision-making in areas such as energy consumption, waste management, and resource allocation

Impact of Non-Linear Electronic Circuits and Switch of Chaotic Dynamics

Switch-mode power supply is an extremely non-linear system that can inevitably exhibit unpredictable behavior. These control laws may be insufficient for nonlinear systems because they are not robust when the requirements on the dynamic characteristics of the system are strict [10]. Control laws that are insensitive to parameter variations, disturbances, and nonlinearities must be used. In this paper, we have tested the method of the first harmonic, used to analyses servo controls with a nonlinear element, and to predict certain non-linear behaviors. It mainly allows predicting the limit cycles, but also the jump phenomena, the harmonics as well as the responses of non-linear systems to sinusoidal inputs. We apply this method for the prediction of limit cycles and the determination of their amplitude and frequency. We take as an example a Boost converter controlled by current [4]. This system is chaotic when the duty cycle is more significant than 0.5: we then eliminate the chaos by applying the slippery mode command (for the ripple of the output voltage, for the current ripple of the inductance and switching frequency) when the output is periodic (duty cycle less than or equal to 0.5). In this article, we assess that established approach provides the best outcomes: it appears that the preference between the classical mode and the sliding mode depends heavily on the variance domain of the parameters E, R, and Iref

sj-docx-1-pie-10.1177_09544089231202913 - Supplemental material for Development of cellulose nanocomposites for electromagnetic shielding applications by using dynamic network

Supplemental material, sj-docx-1-pie-10.1177_09544089231202913 for Development of cellulose nanocomposites for electromagnetic shielding applications by using dynamic network by Abdulsattar Abdullah Hamad, Faris Maher Ahmed, C. Labesh Kumar, Sumalatha Donipati, Talluri Sreekrishna, Din Bandhu, Bathina Rajesh Kumar and Alnoman Mundher Tayyeh in Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering</p