pwc-archive/papers-with-abstracts huggingface dataset:
Modelling of Variable Speed Hydropower for Grid Integration Studies
Date: 2020-03-13 00:00:00

This paper proposes a hydraulic model based on the Euler turbine equations
suitable for the purpose of grid integration studies of variable speed
hydropower (VSHP). The work was motivated by the need to assess how the dynamic
performance might change when a hydropower plant is operated at variable speed.
The Euler model considers the water flow dependency on the turbine rotational
speed and calculates the turbine power as a non-linear function of water flow,
turbine rotational speed and guide vane opening. A waterway model is included,
based on the 1-D momentum and continuity balance for a water-filled elementary
pipe to simulate water hammer, mass oscillation and tunnel losses. These
detailed and accurate models are necessary for recognising possible limitations
in the hydraulic system, to model the turbine power and rotational speed
correctly and thereby to be able to maximise power delivery for system control
purposes. All Euler model parameters can be derived from the physical
dimensions of the turbine and waterway, ensuring easy implementation.
State-space representation of the Euler model is approximated by utilising a
lumped-parameter equivalent of the penstock dynamics. Dynamic simulations and
eigenvalue analysis show the strength of the Euler model compared to
conventional hydropower models.
BuildSenSys: Reusing Building Sensing Data for Traffic Prediction with Cross-domain Learning
Date: 2020-03-11 00:00:00
Tasks: Traffic Prediction

With the rapid development of smart cities, smart buildings are generating a
massive amount of building sensing data by the equipped sensors. Indeed,
building sensing data provides a promising way to enrich a series of
data-demanding and cost-expensive urban mobile applications. In this paper, we
study how to reuse building sensing data to predict traffic volume on nearby
roads. Nevertheless, it is non-trivial to achieve accurate prediction on such
cross-domain data with two major challenges. First, relationships between
building sensing data and traffic data are not unknown as prior, and the
spatio-temporal complexities impose more difficulties to uncover the underlying
reasons behind the above relationships. Second, it is even more daunting to
accurately predict traffic volume with dynamic building-traffic correlations,
which are cross-domain, non-linear, and time-varying. To address the above
challenges, we design and implement BuildSenSys, a first-of-its-kind system for
nearby traffic volume prediction by reusing building sensing data. First, we
conduct a comprehensive building-traffic analysis based on multi-source
datasets, disclosing how and why building sensing data is correlated with
nearby traffic volume. Second, we propose a novel recurrent neural network for
traffic volume prediction based on cross-domain learning with two attention
mechanisms. Specifically, a cross-domain attention mechanism captures the
building-traffic correlations and adaptively extracts the most relevant
building sensing data at each predicting step. Then, a temporal attention
mechanism is employed to model the temporal dependencies of data across
historical time intervals. The extensive experimental studies demonstrate that
BuildSenSys outperforms all baseline methods with up to 65.3% accuracy
improvement (e.g., 2.2% MAPE) in predicting nearby traffic volume.
Design of Robust Path-Following Control System for Self-driving Vehicles Using Extended High-Gain Observer
Date: 2020-03-13 00:00:00

In the real-world, self-driving vehicles are required to achieve steering
maneuvers in both uncontrolled and uncertain environments while maintaining
high levels of safety and passengers' comfort. Ignoring these requirements
would inherently cause a significant degradation in the performance of the
control system, and consequently, could lead to life-threatening scenarios. In
this paper, we present a robust path following control of a self-driving
vehicle under mismatched perturbations due to the effect of parametric
uncertainties, vehicle side-slip angle, and road banking. In particular, the
proposed control framework includes two parts. The first part ensures that the
lateral and the yaw dynamics behave with nominal desired dynamics by canceling
undesired dynamics. The second part is composed of two extended high-gain
observers to estimate the system state variables and the perturbation terms.
Our stability analysis of the closed-loop systems confirms exponential
stability properties under the proposed control law. To validate the proposed
control system, the controller is implemented experimentally on an autonomous
vehicle research platform and tested in different road conditions that include
flat, inclined, and banked roads. The experimental results show the
effectiveness of the controller, they also illustrate the capability of the
controller in achieving comparable performance under inclined and banked roads
as compared to flat roads under a range of longitudinal velocities.
Piecewise Digital Predistortion fo mmWave Active Antenna Arrays: Algorithms and Measurements
Date: 2020-04-30 00:00:00

In this paper, we describe a novel framework for digital predistortion (DPD)
based linearization of strongly nonlinear millimeter-wave active antenna
arrays. Specifically, we formulate a piecewise (PW) closed-loop (CL) DPD
solution and low-complexity gradient-adaptive parameter learning algorithms,
together with a region partitioning method, that can efficiently handle deep
compression of the PA units. The impact of beamsteering on the DPD performance
is studied, showing strong beam-dependence, thus necessitating frequent
updating of the DPD. In order to facilitate fast adaptation, an inexpensive,
non-iterative, pruning algorithm is introduced, which allows to significantly
reduce the amount of model coefficients. The proposed methods are validated
with extensive over-the-air RF measurements on a 64-element active antenna
array transmitter operating at 28 GHz carrier frequency and transmitting a 400
MHz 5G New Radio (NR) standard-compliant orthogonal frequency division
multiplexing waveform. The obtained results demonstrate the excellent
linearization capabilities of the proposed solution, conforming to the new 5G
NR requirements for frequency range 2 (FR2) in terms of both inband waveform
quality and out-of-band emissions. The proposed PW-CL DPD is shown to
outperform the state-of-the-art PW DPD based on the indirect learning
architecture, as well as the classical single-polynomial based DPD solutions in
terms of linearization performance and computational complexity by a clear
margin.
Robust and Optimal Single-loop Voltage Controller for Grid-forming Voltage Source Inverters
Date: 2020-03-13 00:00:00

Design and implementation of an optimal and robust single-loop voltage
controller is proposed for single-phase grid forming voltage source inverter
(VSI). The objective of the proposed controller is to have good reference
tracking and disturbance rejection. Uncertain nature of loads can significantly
alter system's behavior, especially during heavily loaded conditions, and
impose an uncertainty in the dynamic model. This deteriorates the robustness of
the controller severely. H-Infinity-based controller design is proposed in this
article to address this issue. The required objectives of the optimal
controller are formulated after modeling the loads as uncertain element of the
control system. Time-domain MATLAB/SIMULINK-based simulation study
substantiates the fact that the resulting controller exhibits superior
robustness in performance during varying loading, conditions of VSI than that
of the conventional multi-loop control architecture.
Distributed Stabilization of Two Interdependent Markov Jump Linear Systems with Partial Information
Date: 2020-05-22 00:00:00

In this paper, we study the stabilization of two interdependent Markov jump
linear systems (MJLSs) with partial information, where the interdependency
arises as the transition of the mode of one system depends on the states of the
other system. First, we formulate a framework for the two interdependent MJLSs
to capture the interactions between various entities in the system, where the
modes of the system cannot be observed directly. Instead, a signal which
contains information of the modes can be obtained. Then, depending on the scope
of the available system state information (global or local), we design
centralized and distributed controllers, respectively, that can stochastically
stabilize the overall interdependent MJLS. In addition, the sufficient
stabilization conditions for the system under both types of information
structure are derived. Finally, we provide a numerical example to illustrate
the effectiveness of the designed controllers.
Detecting Load Redistribution Attacks via Support Vector Models
Date: 2020-03-14 00:00:00

A machine learning-based detection framework is proposed to detect a class of
cyber-attacks that redistribute loads by modifying measurements. The detection
framework consists of a multi-output support vector regression (SVR) load
predictor that predicts loads by exploiting both spatial and temporal
correlations, and a subsequent support vector machine (SVM) attack detector to
determine the existence of load redistribution (LR) attacks utilizing loads
predicted by the SVR predictor. Historical load data for training the SVR are
obtained from the publicly available PJM zonal loads and are mapped to the IEEE
30-bus system. The SVM is trained using normal data and randomly created LR
attacks, and is tested against both random and intelligently designed LR
attacks. The results show that the proposed detection framework can effectively
detect LR attacks. Moreover, attack mitigation can be achieved by using the SVR
predicted loads to re-dispatch generations.
A Novel Bis-Coumarin Targets Multiple Tyrosine Kinases of Key Signaling Pathways in Melanoma and Inhibits Melanoma Cell Survival, Proliferation, and Migration
Date: 2020-04-20 00:00:00

Melanoma is one of the most dangerous skin malignancies due to its high
metastatic tendency and high mortality. Activation of key signaling pathways
enforcing melanoma progression depends on phosphorylation of tyrosine kinases,
and oxidative stress. We here investigated the effect of the new bis-coumarin
derivative (3,5-DCPBC) on human melanoma cell survival, growth, proliferation,
migration, and intracellular redox state, and deciphered associated signal
pathways. This novel derivative was found to be toxic for melanoma cells, and
non-toxic for their benign counterparts, melanocytes and fibroblasts. 3,5-DCPBC
inhibited cell survival, migration and proliferation of different metastatic,
and non-metastatic melanoma cell lines through the profound suppression of
phosphorylation of the Epidermal Growth Factor receptor, and related downstream
pathways. Suppression of phosphorylation of key downstream transcription
factors and different tyrosine kinases comprise JAK/STAT, SRC kinases, ERK and
MAP kinases (p38alpha), all involved in melanoma progression. Simultaneous and
specific targeting of multiple tyrosine kinases and corresponding key genes in
melanoma cells makes 3,5-DCPBC a highly interesting anti-melanoma, and
anti-metastatic drug candidate which may in the long term hold promise in the
therapy of advanced melanoma.
A Stackelberg Game Approach to Resource Allocation for Intelligent Reflecting Surface Aided Communications
Date: 2020-03-14 00:00:00
Tasks: Management

It is known that the capacity of the intelligent reflecting surface (IRS)
aided cellular network can be effectively improved by reflecting the incident
signals from the transmitter in a low-cost passive reflecting way. In this
paper, we study the adoption of an IRS for downlink multi-user communication
from a multi-antenna base station (BS). Nevertheless, in the actual network
operation, the IRS operator can be selfish or have its own objectives due to
competing/limited resources as well as deployment/maintenance cost. Therefore,
in this paper, we develop a Stackelbeg game model to analyze the interaction
between the BS and the IRS operator. Specifically, different from the existing
studies on IRS that merely focus on tuning the reflection coefficient of all
the reflection elements, we consider the reflection resource (elements)
management, which can be realized via trigger module selection under our
proposed IRS architecture that all the reflection elements are partially
controlled by independent switches of controller. A Stackelberg game-based
alternating direction method of multipliers (ADMM) is proposed to jointly
optimize the transmit beamforming at the BS and the passive beamforming of the
triggered reflection modules. Numerical examples are presented to verify the
proposed studies. It is shown that the proposed scheme is effective in the
utilities of both the BS and IRS.
What happens to a ToF LiDAR in fog?
Authors: You Li, Pierre Duthon, Michèle Colomb, Javier Ibanez-Guzman
Date: 2020-03-14 00:00:00
Tasks: BIG-bench Machine Learning

This article focuses on analyzing the performance of a typical time-of-flight (ToF) LiDAR under fog environment. By controlling the fog density within CEREMA Adverse Weather Facility 1 , the relations between the ranging performance and fogs are both qualitatively and quantitatively investigated. Furthermore, based on the collected data, a machine learning based model is trained to predict the minimum fog visibility that allows successful ranging for this type of LiDAR. The revealed experimental results and methods are helpful for ToF LiDAR specifications from automotive industry.
Predicting COVID-19 distribution in Mexico through a discrete and time-dependent Markov chain and an SIR-like model
Date: 2020-03-15 00:00:00

COVID-19 is an emergent viral infection which rose in December 2019 in a city
in the Chinese province of Hubei, Wuhan; the viral aetiology of this infection
is now known as COVID-19 virus, which belongs to the Betacoronavirus genus.
This virus produces the syndrome of acute respiratory stress that h as been
witnessed in other coronaviruses, such as that MERS-CoV in Middle East
countries or SARS-CoV which was seen in 2002 and 2003 in China. This virus
mediates its entry through its spike (S) proteins interacting with ACE2
receptors in lung epithelial cells, and may promote an inflammatory response by
means of inflammasome NLRP3 activation and unfolded protein response (these are
possibly consequence of the envelope E protein of COVID-19 virus). Efforts have
been made worldwide to prevent further spread of the disease, but in March 2020
the WHO declared it a pandemic emergency and Mexico started to report its first
cases. In this paper we attempt to summarize the biological features of the
virus and the possible pathophysiological mechanisms of its disease, as well as
a stochastic model characterizing the probability distribution of cases in
Mexico by states and the estimated number of cases in Mexico through a
differential equation model (modified SIR model), thus will we be able to
characterize the disease and its course in Mexico in order to display more
preparedness and promote more logical actions by both the policy makers as well
as the general population.
Bistability and oscillations in cooperative microtubule and kinetochore dynamics in the mitotic spindle
Authors: Felix Schwietert, Jan Kierfeld
Date: 2020-03-15 00:00:00

In the mitotic spindle microtubules attach to kinetochores via catch bonds during metaphase, and microtubule depolymerization forces give rise to stochastic chromosome oscillations. We investigate the cooperative stochastic microtubule dynamics in spindle models consisting of ensembles of parallel microtubules, which attach to a kinetochore via elastic linkers. We include the dynamic instability of microtubules and forces on microtubules and kinetochores from elastic linkers. A one-sided model, where an external force acts on the kinetochore is solved analytically employing a mean-field approach based on Fokker-Planck equations. The solution establishes a bistable force-velocity relation of the microtubule ensemble in agreement with stochastic simulations. We derive constraints on linker stiffness and microtubule number for bistability. The bistable force-velocity relation of the one-sided spindle model gives rise to oscillations in the two-sided model, which can explain stochastic chromosome oscillations in metaphase (directional instability). We derive constraints on linker stiffness and microtubule number for metaphase chromosome oscillations. Including poleward microtubule flux into the model we can provide an explanation for the experimentally observed suppression of chromosome oscillations in cells with high poleward flux velocities. Chromosome oscillations persist in the presence of polar ejection forces, however, with a reduced amplitude and a phase shift between sister kinetochores. Moreover, polar ejection forces are necessary to align the chromosomes at the spindle equator and stabilize an alternating oscillation pattern of the two kinetochores. Finally, we modify the model such that microtubules can only exert tensile forces on the kinetochore resulting in a tug-of-war between the two microtubule ensembles. Then, induced microtubule catastrophes after reaching the...
Robust Constraint Satisfaction in Data-Driven MPC
Authors: Julian Berberich, Johannes Köhler, Matthias A. Müller, Frank Allgöwer
Date: 2020-03-15 00:00:00
Tasks: Model Predictive Control

We propose a purely data-driven model predictive control (MPC) scheme to control unknown linear time-invariant systems with guarantees on stability and constraint satisfaction in the presence of noisy data. The scheme predicts future trajectories based on data-dependent Hankel matrices, which span the full system behavior if the input is persistently exciting. This paper extends previous work on data-driven MPC by including a suitable constraint tightening which ensures that the closed-loop trajectory satisfies desired pointwise-in-time output constraints. Furthermore, we provide estimation procedures to compute system constants related to controllability and observability, which are required to implement the constraint tightening. The practicality of the proposed approach is illustrated via a numerical example.
Understanding Epidemic Data and Statistics: A case study of COVID-19
Date: 2020-03-15 00:00:00

The 2019-Novel-Coronavirus (COVID-19) has affected 116 countries (By March
12) and out of more than 118,000 confirmed cases. Understanding the
transmission dynamics of the infection in each country which affected on a
daily basis and evaluating the effectiveness of control policies is critical
for our further actions. To date, the statistics of COVID-19 reported cases
show more than 80 percent of infected had a mild case of disease, while around
14 percent of infected experienced a severe one and about 5 percent are
categorized as critical disease victims. Today's report (2020-03-12; daily
updates in the prepared website) shows the confirmed cases of COVID-19 in
China, South Korea, Italy, and Iran are 80932, 7869, 12462 and 10075;
respectively. Calculating the total Case Fatality Rate (CFR) of Italy
(2020-03-04), about 7.9% of confirmed cases passed away. Compared to South
Korea's rate of 0.76% (10 times lower than Italy) and China's 3.8% (50% lower
than Italy), the CFR of Italy is too high. There are some effective policies
that yield significant changes in the trend of cases. The lockdown policy in
China and Italy (the effect observed after 11 days), Shutdown of all
nonessential companies in Hubei (the effect observed after 5 days), combined
policy in South Korea and reducing working hours in Iran.
Bounded-Rational Pursuit-Evasion Games
Date: 2020-03-16 00:00:00

We present a framework that incorporates the idea of bounded rationality into
dynamic stochastic pursuit-evasion games. The solution of a stochastic game is
characterized, in general, by its (Nash) equilibria in feedback form. However,
computing these Nash equilibrium strategies may require extensive computational
resources. In this paper, the agents are modeled as bounded rational entities
having limited computational resources. We illustrate the framework by applying
it to a pursuit-evasion game between two vehicles in a stochastic wind field,
where both the pursuer and the evader are bounded rational. We show how such a
game may be analyzed by properly casting it as an iterative sequence of
finite-state Markov Decision Processes (MDPs). Leveraging tools and algorithms
from cognitive hierarchy theory ("level-$k$ thinking") we compute the solution
of the ensuing discrete game, while taking into consideration the rationality
level of each agent. We also present an online algorithm for each agent to
infer its opponent rationality level.
Online Algorithms for Dynamic Matching Markets in Power Distribution Systems
Date: 2020-07-16 00:00:00

This paper proposes online algorithms for dynamic matching markets in power
distribution systems, which at any real-time operation instance decides about
matching -- or delaying the supply of -- flexible loads with available
renewable generation with the objective of maximizing the social welfare of the
exchange in the system. More specifically, two online matching algorithms are
proposed for the following generation-load scenarios: (i) when the mean of
renewable generation is greater than the mean of the flexible load, and (ii)
when the condition (i) is reversed. With the intuition that the performance of
such algorithms degrades with increasing randomness of the supply and demand,
two properties are proposed for assessing the performance of the algorithms.
First property is convergence to optimality (CO) as the underlying randomness
of renewable generation and customer loads goes to zero. The second property is
deviation from optimality, is measured as a function of the standard deviation
of the underlying randomness of renewable generation and customer loads. The
algorithm proposed for the first scenario is shown to satisfy CO and a
deviation from optimal that varies linearly with the variation in the standard
deviation. But the same algorithm is shown to not satisfy CO for the second
scenario. We then show that the algorithm proposed for the second scenario
satisfies CO and a deviation from optimal that varies linearly with the
variation in standard deviation plus an offset.
Market states: A new understanding
Authors: Hirdesh K. Pharasi, Eduard Seligman, Thomas H. Seligman
Date: 2020-03-16 00:00:00
Tasks: Clustering, Time Series, Time Series Analysis

We present the clustering analysis of the financial markets of S&P 500 (USA) and Nikkei 225 (JPN) markets over a period of 2006-2019 as an example of a complex system. We investigate the statistical properties of correlation matrices constructed from the sliding epochs. The correlation matrices can be classified into different clusters, named as market states based on the similarity of correlation structures. We cluster the S&P 500 market into four and Nikkei 225 into six market states by optimizing the value of intracluster distances. The market shows transitions between these market states and the statistical properties of the transitions to critical market states can indicate likely precursors to the catastrophic events. We also analyze the same clustering technique on surrogate data constructed from average correlations of market states and the fluctuations arise due to the white noise of short time series. We use the correlated Wishart orthogonal ensemble for the construction of surrogate data whose average correlation equals the average of the real data.