2,122 research outputs found
Capturing flight system test engineering expertise: Lessons learned
Within a few years, JPL will be challenged by the most active mission set in history. Concurrently, flight systems are increasingly more complex. Presently, the knowledge to conduct integration and test of spacecraft and large instruments is held by a few key people, each with many years of experience. JPL is in danger of losing a significant amount of this critical expertise, through retirement, during a period when demand for this expertise is rapidly increasing. The most critical issue at hand is to collect and retain this expertise and develop tools that would ensure the ability to successfully perform the integration and test of future spacecraft and large instruments. The proposed solution was to capture and codity a subset of existing knowledge, and to utilize this captured expertise in knowledge-based systems. First year results and activities planned for the second year of this on-going effort are described. Topics discussed include lessons learned in knowledge acquisition and elicitation techniques, life-cycle paradigms, and rapid prototyping of a knowledge-based advisor (Spacecraft Test Assistant) and a hypermedia browser (Test Engineering Browser). The prototype Spacecraft Test Assistant supports a subset of integration and test activities for flight systems. Browser is a hypermedia tool that allows users easy perusal of spacecraft test topics. A knowledge acquisition tool called ConceptFinder which was developed to search through large volumes of data for related concepts is also described and is modified to semi-automate the process of creating hypertext links
Quantum Generative Adversarial Networks for Learning and Loading Random Distributions
Quantum algorithms have the potential to outperform their classical
counterparts in a variety of tasks. The realization of the advantage often
requires the ability to load classical data efficiently into quantum states.
However, the best known methods require gates to
load an exact representation of a generic data structure into an -qubit
state. This scaling can easily predominate the complexity of a quantum
algorithm and, thereby, impair potential quantum advantage. Our work presents a
hybrid quantum-classical algorithm for efficient, approximate quantum state
loading. More precisely, we use quantum Generative Adversarial Networks (qGANs)
to facilitate efficient learning and loading of generic probability
distributions -- implicitly given by data samples -- into quantum states.
Through the interplay of a quantum channel, such as a variational quantum
circuit, and a classical neural network, the qGAN can learn a representation of
the probability distribution underlying the data samples and load it into a
quantum state. The loading requires
gates and can, thus, enable the
use of potentially advantageous quantum algorithms, such as Quantum Amplitude
Estimation. We implement the qGAN distribution learning and loading method with
Qiskit and test it using a quantum simulation as well as actual quantum
processors provided by the IBM Q Experience. Furthermore, we employ quantum
simulation to demonstrate the use of the trained quantum channel in a quantum
finance application.Comment: 14 pages, 13 figure
Unlocking the Potential of Flexible Energy Resources to Help Balance the Power Grid
Flexible energy resources can help balance the power grid by providing
different types of ancillary services. However, the balancing potential of most
types of resources is restricted by physical constraints such as the size of
their energy buffer, limits on power-ramp rates, or control delays. Using the
example of Secondary Frequency Regulation, this paper shows how the flexibility
of various resources can be exploited more efficiently by considering multiple
resources with complementary physical properties and controlling them in a
coordinated way. To this end, optimal adjustable control policies are computed
based on robust optimization. Our problem formulation takes into account power
ramp-rate constraints explicitly, and accurately models the different
timescales and lead times of the energy and reserve markets. Simulations
demonstrate that aggregations of select resources can offer significantly more
regulation capacity than the resources could provide individually.Comment: arXiv admin note: text overlap with arXiv:1804.0389
Earth Radiation budget satellite system studies
The scientific objectives and the associated mission analysis, instrument definition, and data analysis methods are discussed
Estimating Drift Parameters in a Fractional Ornstein Uhlenbeck Process with Periodic Mean
We construct a least squares estimator for the drift parameters of a
fractional Ornstein Uhlenbeck process with periodic mean function and long
range dependence. For this estimator we prove consistency and asymptotic
normality. In contrast to the classical fractional Ornstein Uhlenbeck process
without periodic mean function the rate of convergence is slower depending on
the Hurst parameter , namely
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