167 research outputs found
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 -,
- and -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
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