pwc-archive/papers-with-abstracts huggingface dataset:
Fuzzy Logic Control for Mixed conventional/braking Actuation Mobile Robots
Date: 2019-09-13 00:00:00

The use of conventional actuators in robotic systems (electric motors in
particular), while often offering advantages in terms of flexibility and
controllability, suffer from primary actuator failure,due to unexpected
complexities in their environment, which can lead to loss of controllability.
Conventional actuators can impose disadvantages on mechanical complexity,
weight, and cost. Here,the Mixed conventional/braking Actuation Mobile Robot
(MAMR), a new mobile robot platform,is proposed to tackle such drawbacks in
actuation and explore the use and control of braking actuation. This platform
substitutes the drive motors used in Ackermann steering with brakes that have
only two states, ON and OFF. Additionally, the conventional drive wheels are
replaced by a single,omni-directional wheel that only supports a driving force
in the robots longitudinal direction. The ability of braking actuators in
providing controllability under actuator failure is one of the primary
motivations of this work. To validate the reliability and accuracy of MAMR
approach, this paper studies the design of such robotic systems, the design and
synthesis of fuzzy logic controllers along with the experimental assessments of
these controllers in real-time. The experimental tests point out the controller
performance enhancement using fuzzy logic controllers and MAMR.
Ptychographic phase-retrieval by proximal algorithms
Date: 2019-09-13 00:00:00
Tasks: Retrieval

We derive a set of ptychography phase-retrieval iterative engines based on
proximal algorithms originally developed in convex optimization theory, and
discuss their connections with existing ones. The use of proximal operator
creates a simple frame work that allows us to incorporate the effect of noise
from a maximum-likelihood principle. We focus on three particular algorithms,
namely proximal minimization, alternating direction method of multiplier and
accelerated proximal gradient, and benckmark their performance with numerical
simulations and experimental x-ray data. Among them, accelerated proximal
gradient shows superior performance in terms of both accuracy and convergence
rate for a noisy dataset.
Comparing the forecasting of cryptocurrencies by Bayesian time-varying volatility models
Date: 2019-09-14 00:00:00
Tasks: Time Series, Time Series Analysis

This paper studies the forecasting ability of cryptocurrency time series.
This study is about the four most capitalized cryptocurrencies: Bitcoin,
Ethereum, Litecoin and Ripple. Different Bayesian models are compared,
including models with constant and time-varying volatility, such as stochastic
volatility and GARCH. Moreover, some crypto-predictors are included in the
analysis, such as S\&P 500 and Nikkei 225. In this paper the results show that
stochastic volatility is significantly outperforming the benchmark of VAR in
both point and density forecasting. Using a different type of distribution, for
the errors of the stochastic volatility the student-t distribution came out to
be outperforming the standard normal approach.
Interference Mitigation Scheme in 3D Topology IoT Network with Antenna Radiation Pattern
Date: 2019-09-14 00:00:00

Internet of things (IoT) is one of main paradigms for 5G wireless systems.
Due to high connection density, interference from other sources is a key
problem in IoT networks. Especially, it is more difficult to find a solution to
manage interference in uncoordinated networks than coordinated system. In this
work, we consider 3D topology of uncoordinated IoT network and propose
interference mitigation scheme with respect to 3D antenna radiation pattern. In
2D topology network, the radiation pattern of dipole antenna can be assumed as
onmi-directional. We show the variance of antenna gain on dipole antenna in 3D
topology, consider the simultaneous use of three orthogonal dipole antennas,
and compare the system performance depending on different antenna
configurations. Our simulation results show that proper altitude of IoT devices
can extensively improve the system performance.
Carbogen inhalation during Non-Convulsive Status Epilepticus: A quantitative analysis of EEG recordings
Date: 2019-09-14 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Functional Connectivity

Objective: To quantify the effect of inhaled 5% carbon-dioxide/95% oxygen on
EEG recordings from patients in non-convulsive status epilepticus (NCSE).
Methods: Five children of mixed aetiology in NCSE were given high flow of
inhaled carbogen (5% carbon dioxide/95% oxygen) using a face mask for maximum
120s. EEG was recorded concurrently in all patients. The effects of inhaled
carbogen on patient EEG recordings were investigated using band-power,
functional connectivity and graph theory measures. Carbogen effect was
quantified by measuring effect size (Cohen's d) between "before", "during" and
"after" carbogen delivery states. Results: Carbogen's apparent effect on EEG
band-power and network metrics across all patients for "before-during" and
"before-after" inhalation comparisons was inconsistent across the five
patients. Conclusion: The changes in different measures suggest a potentially
non-homogeneous effect of carbogen on the patients' EEG. Different aetiology
and duration of the inhalation may underlie these non-homogeneous effects.
Tuning the carbogen parameters (such as ratio between CO2 and O2, duration of
inhalation) on a personalised basis may improve seizure suppression in future.
Measurement-Based Wide-Area Damping of Inter-Area Oscillations based on MIMO Identification
Date: 2019-09-14 00:00:00

Interconnected power grid exhibits oscillatory response after a disturbance
in the system. One such type of oscillations, the inter-area oscillations has
the oscillation frequency in the range of 0.1 to 1 Hz. The damping of
inter-area oscillations is difficult with local controllers, but it can be
achieved using a Wide Area Damping Controller (WADC). For effective control,
the input to the WADC should be the most observable signal and the WADC output
should be sent to the most controllable generator. This paper presents a
measurement-based novel algorithm for multi-input-multi-output (MIMO) transfer
function identification of the power system based on optimization to estimate
such oscillation frequencies. Based on the MIMO transfer function the optimal
control loop for WADC is estimated. The WADC design is based on the discrete
linear quadratic regulator (DLQR) and Kalman filtering for damping of
inter-area oscillations. Since the MIMO identification is based on actual
measurements, the proposed method can accurately monitor changes in the power
grid whereas the conventional methods are based on small-signal analysis of a
linearized model which does not consider changing operating conditions. The
overall algorithm is implemented and validated on a RTDS/RSCAD and MATLAB
real-time co-simulation platform using two-area and IEEE 39 bus power system
models.
Sensorless Real-Time Reduced Order Model Based Adaptive Maximum Power Tracking Pitch Controller for Grid Connected Wind Turbines
Date: 2019-09-14 00:00:00

This paper presents a sensor-less maximum power tracking (MPT) pitch
controller for grid connected Wind Turbine (WT). The main advantage of the
proposed architecture is that the approach ensures smooth operation and thus
minimizes the mechanical stress and damage on the WT during high wind speed and
grid transient conditions. Simultaneously, it also: a) reduces transients in
Point of Common Coupling (PCC) bus voltage, b) reduces rotor speed
oscillations, and c) controls the output power of the wind turbine without
exceeding its thermal limits. The approach can work without wind speed
measurements. In order to consider the effect of grid variations at the PCC,
the affected area in the grid is modeled as a study area (area of interest),
and remaining area (external area) is modeled as frequency dependent reduced
order model (FDROM). The reduced order model (ROM) is then used to estimate the
reference speed. The proposed controller is designed using the error between
actual speed of the generator and the reference speed, to ensure smooth
operation and limit the speed and aerodynamic power at the rated values. The
architecture is evaluated using wind farm integrated Kundur's two-area and
IEEE-39 bus test systems using real-time digital simulator (RTDS).
Performance Analysis of OFDM with Peak Cancellation Under EVM and ACLR Restrictions
Date: 2019-09-15 00:00:00

This paper presents performance analysis of an adaptive peak cancellation
method to reduce the high peak-toaverage power ratio (PAPR) for OFDM systems,
while keeping the out-of-band (OoB) power leakage as well as an in-band
distortion power below the pre-determined level. In this work, the increase of
adjacent leakage power ratio (ACLR) and error vector magnitude (EVM) are
estimated recursively using the detected peak amplitude. We present analytical
framework for OFDM-based systems with theoretical bit error rate (BER)
representations and detection of optimum peak threshold based on predefined EVM
and ACLR requirements. Moreover, the optimum peak detection threshold is
selected based on the oretical design to maintain the predefined distortion
level. Thus, their degradations are automatically restricted below the
pre-defined levels which correspond to target OoB radiation. We also discuss
the practical design of peak-cancellation (PC) signal with target OoB radiation
and in-band distortion through optimizing the windowing size of the PC signal.
Numerical results show the improvements with respect to both achievable bit
error rate (BER) and PAPR with the PC method in eigen-beam space division
multiplexing (E-SDM) systems under restriction of OoB power radiation. It can
also be seen that the theoretical BER shows good agreements with simulation
results.
Statistical inference for statistical decisions
Date: 2019-09-15 00:00:00
Tasks: Decision Making

The Wald development of statistical decision theory addresses decision making
with sample data. Wald's concept of a statistical decision function (SDF)
embraces all mappings of the form [data -> decision]. An SDF need not perform
statistical inference; that is, it need not use data to draw conclusions about
the true state of nature. Inference-based SDFs have the sequential form [data
-> inference -> decision]. This paper motivates inference-based SDFs as
practical procedures for decision making that may accomplish some of what Wald
envisioned. The paper first addresses binary choice problems, where all SDFs
may be viewed as hypothesis tests. It next considers as-if optimization, which
uses a point estimate of the true state as if the estimate were accurate. It
then extends this idea to as-if maximin and minimax-regret decisions, which use
point estimates of some features of the true state as if they were accurate.
The paper primarily uses finite-sample maximum regret to evaluate the
performance of inference-based SDFs. To illustrate abstract ideas, it presents
specific findings concerning treatment choice and point prediction with sample
data.
Geometric PID-type attitude tracking control on SO(3)
Date: 2019-09-16 00:00:00
Tasks: Vocal Bursts Type Prediction

This article develops and proposes a geometric nonlinear
proportional-integral-derivative (PID) type tracking control scheme on the Lie
group of rigid body rotations, SO(3). Like PD-type attitude tracking control
schemes that have been proposed in the past, this PID-type control scheme
exhibits almost global asymptotic stability in tracking a desired attitude
profile. The stability of this PID-type tracking control scheme is shown using
a Lyapunov analysis. A numerical simulation study demonstrates the stability of
this tracking control scheme, as well as its robustness to a disturbance
torque. In addition, a numerical comparison study shows the effectiveness of
the proposed integrator term.
Novel Defense Strategy Against Zero-Dynamics Attack in Multi-Agent Systems
Date: 2019-09-16 00:00:00

This paper considers the defense strategy of strategic topology switching for
the second-order multi-agent system under zero-dynamics attack (ZDA) whose
attack-starting time is allowed to be not the initial time. We first study the
detectability of ZDA, which is a sufficient and necessary condition of
switching topologies in detecting the attack. Based on the detectability, a
Luenberger observer under switching topology is proposed to detect the stealthy
attack. The primary advantages of the attack-detection algorithm are twofold:
(i) in detecting ZDA, the algorithm allows the defender (system operator) to
have no knowledge of the attack-starting time and the misbehaving agents (i.e.,
agents under attack); (ii) in tracking system in the absence of attacks,
Luenberger observer has no constraint on the magnitudes of observer gains and
the number of observed outputs, i.e., only one agent's observed output is
sufficient. Simulations are provided to verify the effectiveness of the
strategic topology-switching algorithm.
Microstructure synthesis using style-based generative adversarial network
Date: 2019-09-16 00:00:00
Tasks: Generative Adversarial Network

Work considers the usage of StyleGAN architecture for the task of
microstructure synthesis. The task is the following: given number of samples of
structure we try to generate similar samples at the same time preserving its
properties. Since the considered architecture is not able to produce samples of
sizes larger than the training images, we propose to use image quilting to
merge fixed-sized samples. One of the key features of the considered
architecture is that it uses multiple image resolutions. We also investigate
the necessity of such an approach.
Decentralized Assignment of Electric Vehicles at Charging Stations Based on Personalized Cost Functions and Distributed Ledger Technologies
Date: 2019-09-16 00:00:00

In this paper we propose a stochastic decentralized algorithm to recommend the most convenient Charging Station (CS) to Plug-in Electric Vehicles (PEVs) that need charging. In particular, we use different cost functions to describe the possibly different priorities of PEV drivers, such as the preference to minimize charging costs, charging times, or the distance between them and the CS. For this purpose, we leverage on an IoT architecture based on a permissioned Distributed Ledger Technology (DLT) to enforce compliance of drivers and reduces the occurrence of detrimental misbehaviours of drivers. Extensive simulations performed with the mobility simulator SUMO in realistic city-wide networks have been provided to illustrate how the proposed PEV assignment procedure works in practice, and to validate its performance.
Additive normal tempered stable processes for equity derivatives and power law scaling
Authors: Michele Azzone, Roberto Baviera
Date: 2019-09-16 00:00:00

We introduce a simple model for equity index derivatives. The model generalizes well known L\`evy Normal Tempered Stable processes (e.g. NIG and VG) with time dependent parameters. It accurately fits Equity index implied volatility surfaces in the whole time range of quoted instruments, including small time horizon (few days) and long time horizon options (years). We prove that the model is an Additive process that is constructed using an Additive subordinator. This allows us to use classical L\`evy-type pricing techniques. We discuss the calibration issues in detail and we show that, in terms of mean squared error, calibration is on average two orders of magnitude better than both L\`evy processes and Self-similar alternatives. We show that even if the model loses the classical stationarity property of L\`evy processes, it presents interesting scaling properties for the calibrated parameters.
Alternative Visual Units for an Optimized Phoneme-Based Lipreading System
Date: 2019-09-16 00:00:00
Tasks: Lipreading, Management

Lipreading is understanding speech from observed lip movements. An observed
series of lip motions is an ordered sequence of visual lip gestures. These
gestures are commonly known, but as yet are not formally defined, as `visemes'.
In this article, we describe a structured approach which allows us to create
speaker-dependent visemes with a fixed number of visemes within each set. We
create sets of visemes for sizes two to 45. Each set of visemes is based upon
clustering phonemes, thus each set has a unique phoneme-to-viseme mapping. We
first present an experiment using these maps and the Resource Management
Audio-Visual (RMAV) dataset which shows the effect of changing the viseme map
size in speaker-dependent machine lipreading and demonstrate that word
recognition with phoneme classifiers is possible. Furthermore, we show that
there are intermediate units between visemes and phonemes which are better
still. Second, we present a novel two-pass training scheme for phoneme
classifiers. This approach uses our new intermediary visual units from our
first experiment in the first pass as classifiers; before using the
phoneme-to-viseme maps, we retrain these into phoneme classifiers. This method
significantly improves on previous lipreading results with RMAV speakers.
Linear Parameter-Varying Control of Nonlinear Systems based on Incremental Stability
Date: 2019-09-16 00:00:00

The Linear Parameter-Varying (LPV) framework has long been used to guarantee
performance and stability requirements of nonlinear (NL) systems mainly through
the $\mathcal{L}_2$-gain concept. However, recent research has pointed out that
current $\mathcal{L}_2$-gain based LPV synthesis methods can fail to guarantee
these requirements if stabilization of a non-zero operating condition (e.g.
reference tracking, constant disturbance rejection, etc.) is required. In this
paper, an LPV based synthesis method is proposed which is able to guarantee
incremental performance and stability of an NL system even with reference and
disturbance rejection objectives. The developed approach and the current
$\mathcal{L}_2$ LPV synthesis method are compared in a simulation study of the
position control problem of a Duffing oscillator, showing performance
improvements of the proposed method compared to the current
$\mathcal{L}_2$-based approach for tracking and disturbance rejection.
P-BOOST: Parallel Boosting of Optimal Narrow-Band Direction of Arrival Estimators
Date: 2019-09-16 00:00:00

Optimal Maximum Likelihood (ML), narrow-band direction finding cannot be
easily initialized in coherent and low signal to noise ratio environments.
Sparse under-determined solvers are considered as viable solutions to this
problem, since they drastically reduce the dimensionality of the search space
by exploiting the array model sparseness. However, because of quantized
locations, conventional sparse solvers present some ambiguity problems. In this
work, we propose a novel boosting scheme for ML-type estimators, referred to as
Parallel BOOSTer (P-BOOST), where a set of generalized MUSIC solutions provides
pre-estimates of the directions and the number of coherent paths for arbitrary
sensor array geometry and noise covariance. P-BOOST delivers improved and
reliable coarse parameter estimates to a further ML or sparse optimization
stage even in coherent and/or high noise scenarios. Moreover, its dataflow is
highly parallel, which is essential in foreseen remote sensing and
telecommunication applications and fully justifies its acronym.
Reliability and Safety Modeling of a Digital Feed Water Control System
Authors: Shawkat S. Khairullah, Ahmed A. Mostfa
Date: 2019-09-12 00:00:00

Much digital instrumentation and control systems embedded in the critical medical healthcare equipment aerospace devices and nuclear industry have obvious consequence of different failure modes. These failures can affect the behavior of the overall safety critical digital system and its ability to deliver its dependability attributes if any defected area that could be a hardware component or software code embedded inside the digital system is not detected and repaired appropriately. The safety and reliability analysis of safety critical systems can be accomplished with Markov modeling techniques which could express the dynamic and regenerative behavior of the digital control system. Certain states in the system represent system failure while others represent fault free behavior or correct operation in the presence of faults. This paper presents the development of a safety and reliability modeling of a digital feedwater control system using Markov based chain models. All the Markov states and the transitions between these states were assumed and calculated from the control logic for the digital control system. Finally based on the simulation results of modeling the digital feedwater control system the system does meet its reliability requirement with the probability of being in fully operational states is 0.99 over a 6 months time.
A Weighted $\ell_1$-Minimization Approach For Wavelet Reconstruction of Signals and Images
Date: 2019-09-16 00:00:00

In this effort, we propose a convex optimization approach based on weighted
$\ell_1$-regularization for reconstructing objects of interest, such as signals
or images, that are sparse or compressible in a wavelet basis. We recover the
wavelet coefficients associated to the functional representation of the object
of interest by solving our proposed optimization problem. We give a specific
choice of weights and show numerically that the chosen weights admit efficient
recovery of objects of interest from either a set of sub-samples or a noisy
version. Our method not only exploits sparsity but also helps promote a
particular kind of structured sparsity often exhibited by many signals and
images. Furthermore, we illustrate the effectiveness of the proposed convex
optimization problem by providing numerical examples using both orthonormal
wavelets and a frame of wavelets. We also provide an adaptive choice of weights
which is a modification of the iteratively reweighted $\ell_1$-minimization
method.
An SFP--FCC Method for Pricing and Hedging Early-exercise Options under Lévy Processes
Authors: Tat Lung, Chan
Date: 2019-09-16 00:00:00

This paper extends the Singular Fourier--Pad\'e (SFP) method proposed by Chan (2018) to pricing/hedging early-exercise options--Bermudan, American and discrete-monitored barrier options--under a L\'evy process. The current SFP method is incorporated with the Filon--Clenshaw--Curtis (FCC) rules invented by Dom\'inguez et al. (2011), and we call the new method SFP--FCC. The main purpose of using the SFP--FCC method is to require a small number of terms to yield fast error convergence and to formulate option pricing and option Greek curves rather than individual prices/Greek values. We also numerically show that the SFP--FCC method can retain a global spectral convergence rate in option pricing and hedging when the risk-free probability density function is piecewise smooth. Moreover, the computational complexity of the method is $\mathcal{O}((L-1)(N+1)(\tilde{N} \log \tilde{N}) )$ with $N$ a (small) number of complex Fourier series terms, $\tilde{N}$ a number of Chebyshev series terms and $L$, the number of early-exercise/monitoring dates. Finally, we show that our method is more favourable than existing techniques in numerical experiments.