Papel dos controles secundarios numa analise de estabilidade de tensão em regime permanente

Orientador : Anesio dos Santos JrDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de ComputaçãoMestrad

Data analysis and visualization techniques for project tracking: Experiences with the ITLingo-Cloud Platform

Considering the market's competitiveness and the complexity of organizations and projects, analyzing data is crucial to decision support on software development and project management processes. These practices are essential to increase performance, reduce costs and risks of failure, and guarantee the quality of results, keeping the work organized and controlled. ITLingo-Cloud is a multi-organization and multi-workspace collaborative platform to manage and analyze data that can support translating project performance knowledge into improved decision-making. This platform allows users to quickly set up their environment, manage workspaces and technical documentation, and analyze and observe statistics to aid both technical and business decisions. ITLingo-Cloud supports multiple technologies and languages, promotes data synchronization with templates and reusable libraries, as well as automation tasks, namely automatic data extraction, automatic validation, or document automation. The usability of ITLingo-Cloud was recently evaluated with two experiments and discussed with other related approaches.Comment: 19 pages, 5 figure

Accessing the Full Capabilities of Filter Functions: A Tool for Detailed Noise and Control Susceptibility Analysis

The filter function formalism from quantum control theory is typically used to determine the noise susceptibility of pulse sequences by looking at the overlap between the filter function of the sequence and the noise power spectral density. Importantly, the square modulus of the filter function is used for this method, hence directional and phase information is lost. In this work, we take advantage of the full filter function including directional and phase information. By decomposing the filter function with phase preservation before taking the modulus, we are able to consider the contributions to $x$-, $y$- and $z$-rotation separately. Continuously driven systems provide noise protection in the form of dynamical decoupling by cancelling low-frequency noise, however, generating control pulses synchronously with an arbitrary driving field is not trivial. Using the decomposed filter function we look at the controllability of a system under arbitrary driving fields, as well as the noise susceptibility, and also relate the filter function to the geometric formalism

Path integral simulation of exchange interactions in CMOS spin qubits

The boom of semiconductor quantum computing platforms created a demand for computer-aided design and fabrication of quantum devices. Path integral Monte Carlo (PIMC) can have an important role in this effort because it intrinsically integrates strong quantum correlations that often appear in these multi-electron systems. In this paper we present a PIMC algorithm that estimates exchange interactions of three-dimensional electrically defined quantum dots. We apply this model to silicon metal-oxide-semiconductor (MOS) devices and we benchmark our method against well-tested full configuration interaction (FCI) simulations. As an application, we study the impact of a single charge trap on two exchanging dots, opening the possibility of using this code to test the tolerance to disorder of CMOS devices. This algorithm provides an accurate description of this system, setting up an initial step to integrate PIMC algorithms into development of semiconductor quantum computers.Comment: 10 pages , 5 figure

Combining Behaviors with the Successor Features Keyboard

The Option Keyboard (OK) was recently proposed as a method for transferring behavioral knowledge across tasks. OK transfers knowledge by adaptively combining subsets of known behaviors using Successor Features (SFs) and Generalized Policy Improvement (GPI). However, it relies on hand-designed state-features and task encodings which are cumbersome to design for every new environment. In this work, we propose the "Successor Features Keyboard" (SFK), which enables transfer with discovered state-features and task encodings. To enable discovery, we propose the "Categorical Successor Feature Approximator" (CSFA), a novel learning algorithm for estimating SFs while jointly discovering state-features and task encodings. With SFK and CSFA, we achieve the first demonstration of transfer with SFs in a challenging 3D environment where all the necessary representations are discovered. We first compare CSFA against other methods for approximating SFs and show that only CSFA discovers representations compatible with SF&GPI at this scale. We then compare SFK against transfer learning baselines and show that it transfers most quickly to long-horizon tasks.Comment: NeurIPS 202