pwc-archive/papers-with-abstracts huggingface dataset:
Computational Experiment Design for Operations Model Simulation
Date: 2020-04-24 00:00:00
Tasks: model

Computer simulations that demonstrate the valueof novel approaches are
crucial to developing more flexibleand robust power systems operations with
high penetrations ofrenewable energy at multiple geographic and temporal
scales.However, optimization-based simulations that depend on forecastdata
often face challenges in evaluating performance, reproducingresults, and
testing under realistic simulation scenarios. In thispaper, we develop
scientific computing best-practices for thevalidation and reproduction of power
systems operational models.We then employ two case studies to demonstrate the
proposedvalidation and reproduction framework.
Tracking System for Optical Mobile Communication and the Design Rules
Date: 2020-04-24 00:00:00

Free space optical communication has been applied in many scenarios because
of its security, low cost and high rates. In such scenarios, a tracking system
is necessary to ensure an acceptable signal power. Free space optical links
were considered unable to support optical mobile communication when nodes are
randomly moving at a high speed because existing tracking schemes fail to track
the nodes accurately and rapidly. In this paper, we propose a novel tracking
system exploiting multiple beacon laser sources. At the receiver, each beacon
laser's power is measured to estimate the orientation of the target. Unlike
existing schemes which drive servo motors multiple times based on consecutive
measurements and feedback, our scheme can directly estimate the next optimal
targeting shift for the servo motors based on a single measurement, allowing
the tracking system to converge much faster. Closed-form outage probability
expression is derived for the optical mobile communication system with ideal
tracking, where pointing error and moving statistics are considered. To
maintain sufficient average power and reduce the outage probability, the
recommended size of a source spot is expressed in closed form as a function of
the target's statistics of random moving, providing insights to the system
design.
Data-based stabilization of unknown bilinear systems with guaranteed basin of attraction
Date: 2020-04-24 00:00:00

Motivated by the goal of having a building block in the direct design of
data-driven controllers for nonlinear systems, we show how, for an unknown
discrete-time bilinear system, the data collected in an offline open-loop
experiment enable us to design a feedback controller and provide a guaranteed
under-approximation of its basin of attraction. Both can be obtained by solving
a linear matrix inequality for a fixed scalar parameter, and possibly iterating
on different values of that parameter. The results of this data-based approach
are compared with the ideal case when the model is known perfectly.
Vibration control of a rotating cantilever beam using piezoelectric actuator and feedback linearization method
Date: 2020-03-23 00:00:00
Tasks: Cantilever Beam

The vibration of various structures such as blades of turbines, helicopters,
and all kinds of rotating robot arms can damage the structures and disrupt
their performance and balance. Thus, investigation of the reduction and control
of vibration of these structures is significant. In this paper, the coupled and
nonlinear vibration of a rotating beam in the presence of the piezoelectric
layer is considered. The Von Karman strain displacements are used for deriving
the potential equation. The nonlinear equations of motion are obtained based on
the Hamilton principle, and two ordinary differential equations are derived by
the Assume Mode method. Besides, a feedback linearization control strategy is
proposed for suppressing the vibration of the rotating beam. According to the
equation of motion, flexural and torsional vibrations are coupled. Therefore,
control of flexural mode can result in control of the whole system.
Iterative data-driven inference of nonlinearity measures via successive graph approximation
Date: 2020-08-12 00:00:00

In this paper, we establish an iterative data-driven approach to derive
guaranteed bounds on nonlinearity measures of unknown nonlinear systems. In
this context, nonlinearity measures quantify the strength of the nonlinearity
of a dynamical system by the distance of its input-output behaviour to a set of
linear models. First, we compute a guaranteed upper bound of these measures by
given input-output samples based on a data-based non-parametric set-membership
representation of the ground-truth system and local inferences of nonlinearity
measures. Second, we propose an algorithm to improve this bound iteratively by
further samples of the unknown input-output behaviour.
Variation of positiveness to enhance testing of specimens during an epidemic
Date: 2020-04-24 00:00:00
Tasks: Management

Rapid testing of appropriate specimens from patients suspected for a disease
during an epidemic, such as the current Coronavirus outbreak, is of a great
importance for the disease management and control. We propose a method to
enhance processing large amounts of collected samples. The method is based on
mixing samples in testing tubes in a specific configuration, as opposed to
testing single samples in each tube, and accounting for natural virus amounts
in infected patients from variation of positiveness in test tubes. To
illustrate the efficiency of the suggested method we carry out numerical tests
for actual scenarios under various tests. Applying the proposed method enhances
the number of tests by order of magnitudes, where all positives are identified
with no false negatives, and the effective testing time can be reduced
drastically even when the uncertainty in the test is relatively high.
Dynamic Reserve and Transmission Capacity Allocation in Wind-Dominated Power Systems
Date: 2020-04-24 00:00:00

The large shares of wind power generation in electricity markets motivate
higher levels of operating reserves. However, current reserve sizing practices
fail to account for important topological aspects that might hinder their
deployment, thus resulting in high operating costs. Zonal reserve procurement
mitigates such inefficiencies, however, the way the zones are defined is still
open to interpretation. This paper challenges the efficiency of predetermined
zonal setups that neglect the location of stochastic power production in the
system, as well as the availability, cost and accessibility of flexible
generating units. To this end, we propose a novel reserve procurement approach,
formulated as a two-stage stochastic bilevel model, in which the upper level
identifies a number of contiguous reserve zones using dynamic grid partitioning
and sets zonal requirements based on the total expected operating costs. Using
two standard IEEE reliability test cases, we show how the efficient
partitioning of reserve zones can reduce expected system cost and promote the
integration of stochastic renewables.
Diffusion MRI Consistent Wire Models for Efficient Solutions of the Anisotropic Forward Problem in Electroencephalography
Date: 2020-04-21 00:00:00
Tasks: Diffusion  MRI

The surface Boundary Element Method (BEM) is one of the most commonly
employed formulations to solve the forward problem in electroencephalography,
but the applicability of its classical incarnations is lamentably limited to
piece-wise homogeneous media. Several head tissues, however, are strongly
anisotropic due to their complex underlying micro-structure. This implies that
standard boundary integral formulations oversimplify the electrical properties
of the head and produce unrealistic solutions, something that drastically
limits the suitability and impact of BEM technologies to brain imaging. This
contribution addresses this issue by observing that the brain anisotropy in the
white matter is due to the presence of neuronal wire-like structures. We then
extend the well known wire integral equations used for high frequency problems
to the imperfectly conducting quasi-static case and we propose a new hybrid
wire/surface/volume integral equation. When applied on multimodal magnetic
resonance images combined with tractography, this new approach can flexibly and
realistically handle the conductivity anisotropy in any head compartment
providing high level of accuracy and efficiency. The beneficial properties of
the new formulation together with its impact on brain imaging is demonstrated
via numerical results on both canonical and realistic case scenarios.
Decentralized linear quadratic systems with major and minor agents and non-Gaussian noise
Authors: Mohammad Afshari, Aditya Mahajan
Date: 2020-04-24 00:00:00

A decentralized linear quadratic system with a major agent and a collection of minor agents is considered. The major agent affects the minor agents, but not vice versa. The state of the major agent is observed by all agents. In addition, the minor agents have a noisy observation of their local state. The noise processes is \emph{not} assumed to be Gaussian. The structures of the optimal strategy and the best linear strategy are characterized. It is shown that major agent's optimal control action is a linear function of the major agent's MMSE (minimum mean squared error) estimate of the system state while the minor agent's optimal control action is a linear function of the major agent's MMSE estimate of the system state and a "correction term" which depends on the difference of the minor agent's MMSE estimate of its local state and the major agent's MMSE estimate of the minor agent's local state. Since the noise is non-Gaussian, the minor agent's MMSE estimate is a non-linear function of its observation. It is shown that replacing the minor agent's MMSE estimate by its LLMS (linear least mean square) estimate gives the best linear control strategy. The results are proved using a direct method based on conditional independence, common-information-based splitting of state and control actions, and simplifying the per-step cost based on conditional independence, orthogonality principle, and completion of squares.
Conservation Voltage Reduction (CVR) via Two-Timescale Control in Unbalanced Power Distribution Systems
Date: 2020-04-24 00:00:00
Tasks: continuous-control, Continuous Control

Voltage control devices are employed in power distribution systems to reduce
the power consumption by operating the system closer to the lower acceptable
voltage limits; this technique is called conservation voltage reduction (CVR).
The different modes of operation for system's legacy devices (with binary
control) and new devices (e.g. smart inverters with continuous control) coupled
with variable photovoltaic (PV) generation results in voltage fluctuations
which makes it challenging to achieve CVR objective. This paper presents a
two-timescale control of feeder's voltage control devices to achieve CVR that
includes (1) a centralized controller operating in a slower time-scale to
coordinate voltage control devices across the feeder and (2) local controllers
operating in a faster timescale to mitigate voltage fluctuations due to PV
variability. The centralized controller utilizes a three-phase optimal power
flow model to obtain the decision variables for both legacy devices and smart
inverters. The local controllers operate smart inverters to minimize voltage
fluctuations and restore nodal voltages to their reference values by adjusting
the reactive power support. The proposed approach is validated using the
IEEE-123 bus (medium-size) and R3-12.47-2 (large-size) feeders. It is
demonstrated that the proposed approach is effective in achieving the CVR
objective for unbalanced distribution systems.
Molecular dynamics study of the competitive binding of hydrogen peroxide and water molecules with the DNA phosphate groups
Date: 2020-04-24 00:00:00

The hydrogen peroxide is present in the living cell at small concentrations
that increase under the action of the heavy ion beams in the process of
anticancer therapy. The interactions of hydrogen peroxide with DNA, proteins
and other biological molecules are poorly understood. In the present work the
competitive binding of the hydrogen peroxide and water molecules with the DNA
double helix backbone has been studied using the molecular dynamics method. The
simulations have been carried out for the DNA double helix in a water solution
with hydrogen peroxide molecules and Na$^{+}$ counterions. The obtained radial
distribution functions of counterions, H$_2$O$_2$ and H$_2$O molecules with
respect to the oxygen atoms of DNA phosphate groups have been used for the
analysis of the formation of different complexes. The calculated mean residence
times show that a hydrogen peroxide molecule stays at least twice as long near
the phosphate group (up to 7 ps) than a water molecule (about 3 ps). The
hydrogen peroxide molecules form more stable complexes with the phosphate
groups of the DNA backbone than water molecules do.
Accurate Graph Filtering in Wireless Sensor Networks
Authors: Leila Ben Saad, Baltasar Beferull-Lozano
Date: 2020-04-24 00:00:00
Tasks: Denoising, Scheduling

Wireless sensor networks (WSNs) are considered as a major technology enabling the Internet of Things (IoT) paradigm. The recent emerging Graph Signal Processing field can also contribute to enabling the IoT by providing key tools, such as graph filters, for processing the data associated with the sensor devices. Graph filters can be performed over WSNs in a distributed manner by means of a certain number of communication exchanges among the nodes. But, WSNs are often affected by interferences and noise, which leads to view these networks as directed, random and time-varying graph topologies. Most of existing works neglect this problem by considering an unrealistic assumption that claims the same probability of link activation in both directions when sending a packet between two neighboring nodes. This work focuses on the problem of operating graph filtering in random asymmetric WSNs. We show first that graph filtering with finite impulse response graph filters (node-invariant and node-variant) requires having equal connectivity probabilities for all the links in order to have an unbiased filtering, which can not be achieved in practice in random WSNs. After this, we characterize the graph filtering error and present an efficient strategy to conduct graph filtering tasks over random WSNs with node-variant graph filters by maximizing accuracy, that is, ensuring a small bias-variance trade-off. In order to enforce the desired accuracy, we optimize the filter coefficients and design a cross-layer distributed scheduling algorithm at the MAC layer. Extensive numerical experiments are presented to show the efficiency of the proposed solution as well as the cross-layer distributed scheduling algorithm for the denoising application.
From orders to prices: A stochastic description of the limit order book to forecast intraday returns
Authors: Johannes Bleher, Michael Bleher, Thomas Dimpfl
Date: 2020-04-24 00:00:00

We propose a microscopic model to describe the dynamics of the fundamental events in the limit order book (LOB): order arrivals and cancellations. It is based on an operator algebra for individual orders and describes their effect on the LOB. The model inputs are arrival and cancellation rate distributions that emerge from individual behavior of traders, and we show how prices and liquidity arise from the LOB dynamics. In a simulation study we illustrate how the model works and highlight its sensitivity with respect to assumptions regarding the collective behavior of market participants. Empirically, we test the model on a LOB snapshot of XETRA, estimate several linearized model specifications, and conduct in- and out-of-sample forecasts.The in-sample results based on contemporaneous information suggest that our model describes returns very well, resulting in an adjusted $R^2$ of roughly 80%. In the more realistic setting where only past information enters the model, we observe an adjusted $R^2$ around 15%. The direction of the next return can be predicted (out-of-sample) with an accuracy above 75% for time horizons below 10 minutes. On average, we obtain an RMSPE that is 10 times lower than values documented in the literature.
CLUE: Exact maximal reduction of kinetic models by constrained lumping of differential equations
Authors: Alexey Ovchinnikov, Isabel Cristina Pérez Verona, Gleb Pogudin, Mirco Tribastone
Date: 2020-04-24 00:00:00

Motivation: Detailed mechanistic models of biological processes can pose significant challenges for analysis and parameter estimations due to the large number of equations used to track the dynamics of all distinct configurations in which each involved biochemical species can be found. Model reduction can help tame such complexity by providing a lower-dimensional model in which each macro-variable can be directly related to the original variables. Results: We present CLUE, an algorithm for exact model reduction of systems of polynomial differential equations by constrained linear lumping. It computes the smallest dimensional reduction as a linear mapping of the state space such that the reduced model preserves the dynamics of user-specified linear combinations of the original variables. Even though CLUE works with nonlinear differential equations, it is based on linear algebra tools, which makes it applicable to high-dimensional models. Using case studies from the literature, we show how CLUE can substantially lower model dimensionality and help extract biologically intelligible insights from the reduction. Availability: An implementation of the algorithm and relevant resources to replicate the experiments herein reported are freely available for download at https://github.com/pogudingleb/CLUE. Supplementary information: enclosed.
Trading Foreign Exchange Triplets
Authors: Álvaro Cartea, Sebastian Jaimungal, Tianyi Jia
Date: 2020-04-24 00:00:00
Tasks: Triplet

We develop the optimal trading strategy for a foreign exchange (FX) broker who must liquidate a large position in an illiquid currency pair. To maximize revenues, the broker considers trading in a currency triplet which consists of the illiquid pair and two other liquid currency pairs. The liquid pairs in the triplet are chosen so that one of the pairs is redundant. The broker is risk-neutral and accounts for model ambiguity in the FX rates to make her strategy robust to model misspecification. When the broker is ambiguity neutral (averse) the trading strategy in each pair is independent (dependent) of the inventory in the other two pairs in the triplet. We employ simulations to illustrate how the robust strategies perform. For a range of ambiguity aversion parameters, we find the mean Profit and Loss (P&L) of the strategy increases and the standard deviation of the P&L decreases as ambiguity aversion increases.
Radar Accurate Localization of UAV Swarms Based on Range Super-Resolution Method
Date: 2020-04-25 00:00:00
Tasks: Super-Resolution

In radar accurate localization of unmanned aerial vehicle (UAV) swarms, the
high density, similar motion parameters, small radar cross-section (RCS),
strong noise and far range put forward high requirements on radar resolution
and transmitting power. In this paper, by using advantages of the long-time
integration (LTI) technique and gridless sparse method, we construct a
super-resolution framework for radar accurate localization of UAV swarms
without changing radar hardware and system parameters. Thereafter, based on
this framework, a range super-resolution method is proposed to realize the
radar accurate localization of UAV swarms. Mathematical analyses and numerical
simulations are performed and demonstrate that, compared to the keystone
transform (KT)-based LTI method, MUSIC-based method and reweighted atomic-norm
minimization (RAM)-based method, the range super-resolution method is more
robust and practical for radar accurate localization of UAV swarms under the
noisy environment. Additionally, the real experiment with X-band radar is also
conducted to verify the effectiveness of the range super-resolution method.
Sensitivity to Calibrated Parameters
Authors: Thomas H. Jørgensen
Date: 2020-04-25 00:00:00
Tasks: Sensitivity

A common approach to estimation of economic models is to calibrate a sub-set of model parameters and keep them fixed when estimating the remaining parameters. Calibrated parameters likely affect conclusions based on the model but estimation time often makes a systematic investigation of the sensitivity to calibrated parameters infeasible. I propose a simple and computationally low-cost measure of the sensitivity of parameters and other objects of interest to the calibrated parameters. In the main empirical application, I revisit the analysis of life-cycle savings motives in Gourinchas and Parker (2002) and show that some estimates are sensitive to calibrations.
Common trends in the epidemic of Covid-19 disease
Date: 2020-06-10 00:00:00

The discovery of SARS-CoV-2, the responsible virus for the Covid-19 epidemic,
has sparked a global health concern with many countries affected. Developing
models that can interpret the epidemic and give common trend parameters are
useful for prediction purposes by other countries that are at an earlier phase
of the epidemic; it is also useful for future planning against viral
respiratory diseases. One model is developed to interpret the fast-growth phase
of the epidemic and another model for an interpretation of the entire data set.
Both models agree reasonably with the data. It is shown by the first model that
during the fast phase, the number of new infected cases depends on the total
number of cases by a power-law relation with a scaling exponent equal to 0.82.
The second model gives a duplication time in the range 1 to 3 days early in the
start of the epidemic, and another parameter alpha = 0.1-0.5) that deviates the
progress of the epidemic from an exponential growth. Our models may be used for
data interpretation and for guiding predictions regarding this disease, e.g.
the onset of the maximum in the number of new cases.
Wind Data Analysis for Assessing the Potential of Off-Grid Direct EV Charging Stations
Date: 2020-04-25 00:00:00

The integration of large-scale wind farms and large-scale charging stations
for electric vehicles with the electricity grids necessitate energy storage
support for both technologies. Matching the energy variability of the wind
farms with the demand variability of the electric vehicles (EVs) off-grid could
potentially eliminate the need for expensive energy storage technologies
required to stabilize the grid. The objective of this paper is to investigate
the feasibility of using wind generation as direct energy source to power the
EV charging stations. An interval-based approach corresponding to the time slot
of EV charging is introduced for wind energy conversion and analyzed using
different constrains and criteria including, wind speed averaging time
interval, various turbines manufacturers, and standard high-resolution wind
speed data sets. We performed a piecewise recursive of wind turbines' output
energy to measure the EV charging efficiency. Wind averaging results show that
the three minutes intervals have increased the total number of EVs by more than
80% compared to one-- and two -minute intervals. The potential cost reduction
due to decoupling of both technologies from the utility grid, energy storage
systems, and the associated energy conversion power electronics has merit and
research in this direction is worth pursuing