10,942 research outputs found
Beam scanning by liquid-crystal biasing in a modified SIW structure
A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium
Control-mode as a Grid Service in Software-defined Power Grids: GFL vs GFM
In power systems with high penetration of power electronics, grid-forming
control is proposed to replace traditional Grid-Following Converter (GFL) in
order to improve the overall system strength and resist small-signal
instability in weak grids by directly forming the terminal voltage. However,
sufficient headroom of both active and reactive power must be made available
for Grid-Forming Converter (GFM) to operate, potentially leading to sub-optimal
operation in steady states. This presents a new research problem to optimally
allocate between GFM and GFL to balance the ability of GFMs to improve the grid
strength and the potential economic loss resulting from reserved headroom. An
optimization framework under software-defined grids is proposed, for the first
time, to dynamically determine the optimal allocation of GFMs and GFLs in power
systems at each time step of system scheduling according to system conditions,
which ensures both system stability and minimum operational cost. To achieve
this, the system scheduling model is expanded to simultaneously consider the
constraints related to active and reactive power reserves for GFMs, as well as
the system level stability. Case studies conducted on the modified IEEE 30-bus
system demonstrate significant economic benefits in that the optimal proportion
of GFMs in the power system can be dynamically determined while ensuring power
reserve and grid stability constraints
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A Survey of Quantum-Cognitively Inspired Sentiment Analysis Models
Quantum theory, originally proposed as a physical theory to describe the motions of microscopic particles, has been applied to various non-physics domains involving human cognition and decision-making that are inherently uncertain and exhibit certain non-classical, quantum-like characteristics. Sentiment analysis is a typical example of such domains. In the last few years, by leveraging the modeling power of quantum probability (a non-classical probability stemming from quantum mechanics methodology) and deep neural networks, a range of novel quantum-cognitively inspired models for sentiment analysis have emerged and performed well. This survey presents a timely overview of the latest developments in this fascinating cross-disciplinary area. We first provide a background of quantum probability and quantum cognition at a theoretical level, analyzing their advantages over classical theories in modeling the cognitive aspects of sentiment analysis. Then, recent quantum-cognitively inspired models are introduced and discussed in detail, focusing on how they approach the key challenges of the sentiment analysis task. Finally, we discuss the limitations of the current research and highlight future research directions
Detection of entangled states supported by reinforcement learning
Discrimination of entangled states is an important element of quantum
enhanced metrology. This typically requires low-noise detection technology.
Such a challenge can be circumvented by introducing nonlinear readout process.
Traditionally, this is realized by reversing the very dynamics that generates
the entangled state, which requires a full control over the system evolution.
In this work, we present nonlinear readout of highly entangled states by
employing reinforcement learning (RL) to manipulate the spin-mixing dynamics in
a spin-1 atomic condensate. The RL found results in driving the system towards
an unstable fixed point, whereby the (to be sensed) phase perturbation is
amplified by the subsequent spin-mixing dynamics. Working with a condensate of
10900 {87}^Rb atoms, we achieve a metrological gain of 6.97 dB beyond the
classical precision limit. Our work would open up new possibilities in
unlocking the full potential of entanglement caused quantum enhancement in
experiments
Learning Over All Contracting and Lipschitz Closed-Loops for Partially-Observed Nonlinear Systems
This paper presents a policy parameterization for learning-based control on
nonlinear, partially-observed dynamical systems. The parameterization is based
on a nonlinear version of the Youla parameterization and the recently proposed
Recurrent Equilibrium Network (REN) class of models. We prove that the
resulting Youla-REN parameterization automatically satisfies stability
(contraction) and user-tunable robustness (Lipschitz) conditions on the
closed-loop system. This means it can be used for safe learning-based control
with no additional constraints or projections required to enforce stability or
robustness. We test the new policy class in simulation on two reinforcement
learning tasks: 1) magnetic suspension, and 2) inverting a rotary-arm pendulum.
We find that the Youla-REN performs similarly to existing learning-based and
optimal control methods while also ensuring stability and exhibiting improved
robustness to adversarial disturbances
When to be critical? Performance and evolvability in different regimes of neural Ising agents
It has long been hypothesized that operating close to the critical state is
beneficial for natural, artificial and their evolutionary systems. We put this
hypothesis to test in a system of evolving foraging agents controlled by neural
networks that can adapt agents' dynamical regime throughout evolution.
Surprisingly, we find that all populations that discover solutions, evolve to
be subcritical. By a resilience analysis, we find that there are still benefits
of starting the evolution in the critical regime. Namely, initially critical
agents maintain their fitness level under environmental changes (for example,
in the lifespan) and degrade gracefully when their genome is perturbed. At the
same time, initially subcritical agents, even when evolved to the same fitness,
are often inadequate to withstand the changes in the lifespan and degrade
catastrophically with genetic perturbations. Furthermore, we find the optimal
distance to criticality depends on the task complexity. To test it we introduce
a hard and simple task: for the hard task, agents evolve closer to criticality
whereas more subcritical solutions are found for the simple task. We verify
that our results are independent of the selected evolutionary mechanisms by
testing them on two principally different approaches: a genetic algorithm and
an evolutionary strategy. In summary, our study suggests that although optimal
behaviour in the simple task is obtained in a subcritical regime, initializing
near criticality is important to be efficient at finding optimal solutions for
new tasks of unknown complexity.Comment: arXiv admin note: substantial text overlap with arXiv:2103.1218
A Koopman Operator-Based Prediction Algorithm and its Application to COVID-19 Pandemic
The problem of prediction of behavior of dynamical systems has undergone a
paradigm shift in the second half of the 20th century with the discovery of the
possibility of chaotic dynamics in simple, physical, dynamical systems for
which the laws of evolution do not change in time. The essence of the paradigm
is the long term exponential divergence of trajectories. However, that paradigm
does not account for another type of unpredictability: the ``Black Swan" event.
It also does not account for the fact that short-term prediction is often
possible even in systems with exponential divergence. In our framework, the
Black Swan type dynamics occurs when an underlying dynamical system suddenly
shifts between dynamics of different types. A learning and prediction system
should be capable of recognizing the shift in behavior, exemplified by
``confidence loss". In this paradigm, the predictive power is assessed
dynamically and confidence level is used to switch between long term prediction
and local-in-time prediction. Here we explore the problem of prediction in
systems that exhibit such behavior. The mathematical underpinnings of our
theory and algorithms are based on an operator-theoretic approach in which the
dynamics of the system are embedded into an infinite-dimensional space. We
apply the algorithm to a number of case studies including prediction of
influenza cases and the COVID-19 pandemic. The results show that the predictive
algorithm is robust to perturbations of the available data, induced for example
by delays in reporting or sudden increase in cases due to increase in testing
capability. This is achieved in an entirely data-driven fashion, with no
underlying mathematical model of the disease
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