Self-adjusted multi-sensor information fusion electric energy measuring based on neural networks

In this article, self-adjusted Multi-sensor Information Fusion measuring method of electric energy based on neural networks has been thoroughly given. This paper studies the method of automatic error correction of electric power measurement also. The effective learning algorithm of the neural network based on gradient algorithm and Newton algorithm is combined with the LEA discriminant method.The results show that the method can improve the learning efficiency. The hardware model of adaptive real-time fast power measurement is constructed by using DSP device. The experimental results show that the adaptive power measurement model is better than the traditional power meter

Dynamics and control of marine mechatronic oscillators using electromagnetic coupling and switching power electronics

Developing force control mechanisms employing electromagnetic solutions is on the rise in active control applications for flexible mechanical systems, like marine engines and shipboard machinery. Electromagnetic control devices offer superior performance indicators compared to traditional mechanical force actuators in terms of longevity, energy efficiency, maintenance requirements, rapid control response, and high operating speeds. This article investigates the use of magnetic actuation and switching power electronics in addressing the stabilization and tracking control challenges encountered in the dynamics of a mechanical system with a single degree of freedom, comprising mass, spring, and damper elements. Particularly, a linear mechanical oscillator is nonlinearly coupled with an electromagnet and its associated driving circuit via the magnetic field. The electromagnetically actuated mechanical system exhibits characteristics of a deferentially flat nonlinear system. A control strategy is suggested for the purpose of tracking reference position trajectories using output feedback linearization. The synthetic linearized control signal is subsequently guided to a DC-DC buck converter, able to regulate the system’s input voltage in a wide range of operation, by switching the duty cycle. The converter is described using a precise electrical model of the system, accounting for parasitic resistances in the inductor, capacitor, and switches. An averaged state space approach is utilized to create a mathematical nonlinear model for the converter which is then linearized by employing the Exact Feedback Linearization technique. By applying optimal control theory, the controller’s coefficients are fine-tuned for optimal performance. To assess the proposed method’s performance, the dynamics of the compensated mechatronic system is simulated using MATLAB/Simulink. The simulation results demonstrate that the proposed control scheme choice for active control of vibrating mechanical systems using magnetic coupling and switching DC-DC converters meets the requirements and specifications. Finally, adaptations for applications including but not limited to monitoring and manipulating vibrations in marine engines and shipboard machinery are examined as well

Reinforcement Learning for optimization problems via adaptive sampling

Στόχος αυτής της πτυχιακής είναι η μελέτη και η σύγκριση της απόδοσης πολλών διαφορετικών μεθόδων Ενισχυτικής Μάθησης με σκοπό την επίλυση ενός προβλήματος ελαχιστοποίσης, χρησιμοποιώντας μια προσέγγιση προσαρμοστικής δειγματοληψίας. Η ικανότητα των μεθόδων Ενισχυτικής Μάθησης να μαθαίνουν το περιβάλλον τους και να προσαρμόζουν ανάλογα τις δράσεις τους, με σκοπό να μεγιστοποιήσουν τις ανταμοιβές τις οποίες λαμβάνουν, τις επιτρέπει να ελαχιστοποιήσουν τη δοθείσα έκφραση του προβλήματος, ενώ συγχρόνως ελαχιστοποιούν τον αριθμό των κλήσεων που απαιτούνται προς επίτευξη αυτού. Οι αλγόριθμοι οι οποίοι συμπεριλαμβάνονται σε αυτή τη μελέτη είναι οι Value Iteration, Policy Iteration, Monte Carlo Learning και Q-Learning.The goal of this thesis is to study and compare the performance of several Reinforcement Learning methods in solving a minimization problem with an adaptive sampling approach. The ability of RL methods to learn about their environment and adapt their actions accord- ingly to maximize the rewards they receive, allows them to optimize the problem’s given expression, while minimizing the number of evaluations needed to achieve that. The al- gorithms that are included in this study are Value Iteration, Policy Iteration, Monte Carlo Learning and Q-Learning

Climate drivers of global wildfire burned area

Wildfire is an integral part of the Earth system, but at the same time it can pose serious threats to human society and to certain types of terrestrial ecosystems. Meteorological conditions are a key driver of wildfire activity and extent, which led to the emergence of the use of fire danger indices that depend solely on weather conditions. The Canadian Fire Weather Index (FWI) is a widely used fire danger index of this kind. Here, we evaluate how well the FWI, its components, and the climate variables from which it is derived, correlate with observation-based burned area (BA) for a variety of world regions. We use a novel technique, according to which monthly BA are grouped by size for each Global Fire Emissions Database (GFED) pyrographic region. We find strong correlations of BA anomalies with the FWI anomalies, as well as with the underlying deviations from their climatologies for the four climate variables from which FWI is estimated, namely, temperature, relative humidity, precipitation, and wind. We quantify the relative sensitivity of the observed BA to each of the four climate variables, finding that this relationship strongly depends on the pyrographic region and land type. Our results indicate that the BA anomalies strongly correlate with FWI anomalies at a GFED region scale, compared to the strength of the correlation with individual climate variables. Additionally, among the individual climate variables that comprise the FWI, relative humidity and temperature are the most influential factors that affect the observed BA. Our results support the use of the composite fire danger index FWI, as well as its sub-indices, the Build-Up Index (BUI) and the Initial Spread Index (ISI), comparing to single climate variables, since they are found to correlate better with the observed forest or non-forest BA, for the most regions across the globe