pwc-archive/papers-with-abstracts huggingface dataset:
Design and Implementation of an Automatic Synchronizing and Protection Relay through Power-Hardware-in-the-Loop (PHIL) Simulation
Date: 2019-06-30 00:00:00

This paper focuses on the design and implementation of an automatic
synchronizing and protection relay to automate the synchronization process of a
Distributed Energy Resource (DER) to the Main Grid. The proposed design utilize
a cost-effective data acquisition using arduino in combination with LabVIEW
software to implement the multi-purpose synchronizing relay. The proposed
synchronizing relay is capable of synchronizing a Distributed Generator (DG) to
the power grid from black-start and fulfills the requirements imposed by the
utility. The synchronizing relay is implemented through voltage and frequency
control of an actual lab-scale synchronous generator. In the synchronization
process, frequency synchronization is done using speed control of the stepper
motor as prime mover and voltage synchronization is accomplished using
Excitation Control module through Power-Hardware-in-the-Loop (PHIL) simulation.
In grid-connected mode, active and reactive power controls and protection
schemes for the synchronous generator have also been implemented. The proposed
multi-function relay has been deployed and tested on a lab-scale test bed to
validate the proposed design and functionality.
Regularities in stock markets
Date: 2019-06-30 00:00:00

From the stock markets of six countries with high GDP, we study the stock indices, S&P 500 (NYSE, USA), SSE Composite (SSE, China), Nikkei (TSE, Japan), DAX (FSE, Germany), FTSE 100 (LSE, Britain) and NIFTY (NSE, India). The daily mean growth of the stock values is exponential. The daily price fluctuations about the mean growth are Gaussian, but with a non-zero asymptotic convergence. The growth of the monthly average of stock values is statistically self-similar to their daily growth. The monthly fluctuations of the price follow a Wiener process, with a decline of the volatility. The mean growth of the daily volume of trade is exponential. These observations are globally applicable and underline regularities across global stock markets.
Traffic Flow Characteristics and Lane Use Strategies for Connected and Automated Vehicle in Mixed Traffic Conditions
Date: 2020-09-25 00:00:00

Managed lanes, such as a dedicated lane for connected and automated vehicles
(CAVs), can provide not only technological accommodation but also desired
market incentives for road users to adopt CAVs in the near future. In this
paper, we investigate traffic flow characteristics with two configurations of
the managed lane across different market penetration rates and quantify the
benefits from the perspectives of lane-level headway distribution, fuel
consumption, communication density, and overall network performance. The
results highlight the benefits of implementing managed lane strategies for
CAVs: 1) a dedicated CAV lane significantly extends the stable region of the
speed-flow diagram and yields a greater road capacity. As the result shows, the
highest flow rate is 3,400 vehicles per hour per lane at 90% market penetration
rate with one CAV lane; 2) the concentration of CAVs in one lane results in a
narrower headway distribution (with smaller standard deviation) even with
partial market penetration; 3) a dedicated CAV lane is also able to eliminate
duel-bell-shape distribution that is caused by the heterogeneous traffic flow;
and 4) a dedicated CAV lane creates a more consistent CAV density, which
facilitates communication activity and decreases the probability of packet
dropping.
A Dynamic Game Framework for Rational and Persistent Robot Deception With an Application to Deceptive Pursuit-Evasion
Authors: Linan Huang, Quanyan Zhu
Date: 2019-06-30 00:00:00

This article studies rational and persistent deception among intelligent robots to enhance security and operational efficiency. We present an N-player K-stage game with an asymmetric information structure where each robot's private information is modeled as a random variable or its type. The deception is persistent as each robot's private type remains unknown to other robots for all stages. The deception is rational as robots aim to achieve their deception goals at minimum cost. Each robot forms a dynamic belief of others' types based on intrinsic or extrinsic information. Perfect Bayesian Nash equilibrium (PBNE) is a natural solution concept for dynamic games of incomplete information. Due to its requirements of sequential rationality and belief consistency, PBNE provides a reliable prediction of players' actions, beliefs, and expected cumulative costs over the entire K stages. The contribution of this work is fourfold. First, we identify the PBNE computation as a nonlinear stochastic control problem and characterize the structures of players' actions and costs under PBNE. We further derive a set of extended Riccati equations with cognitive coupling under the linear-quadratic (LQ) setting and extrinsic belief dynamics. Second, we develop a receding-horizon algorithm with low temporal and spatial complexity to compute PBNE under intrinsic belief dynamics. Third, we investigate a deceptive pursuit-evasion game as a case study and use numerical experiments to corroborate the results. Finally, we propose metrics, such as deceivability, reachability, and the price of deception (PoD), to evaluate the strategy design and the system performance under deception.
A Reduced Complexity Cross-correlation Interference Mitigation Technique on a Real-time Software-defined Radio GPS L1 Receiver
Date: 2019-06-30 00:00:00
Tasks: Position

The U.S. global position system (GPS) is one of the existing global
navigation satellite systems (GNSS) that provides position and time information
for users in civil, commercial and military backgrounds. Because of its
reliance on many applications nowadays, it's crucial for GNSS receivers to have
robustness to intentional or unintentional interference. Because most
commercial GPS receivers are not flexible, software-defined radio emerged as a
promising solution for fast prototyping and research on interference mitigation
algorithms. This paper provides a proposed minimum mean-squared error (MMSE)
interference mitigation technique which is enhanced for computational
feasibility and implemented on a real-time capable GPS L1 SDR receiver. The GPS
SDR receiver SW has been optimized for real-time operation on National
Instruments' LabVIEW (LV) platform in conjunction with C/C++ dynamic link
libraries (DLL) for improved efficiency. Performance results of said algorithm
with real signals and injected interference are discussed. The proposed SDR
receiver gains in terms of BER curves for several interferers are demonstrated.
Exploiting Acceleration Features of LabVIEW platform for Real-Time GNSS Software Receiver Optimization
Date: 2019-06-30 00:00:00

This paper presents the new generation of LabVIEW-based GPS receiver testbed
that is based on National Instruments' (NI) LabVIEW (LV) platform in
conjunction to C/C++ dynamic link libraries (DLL) used inside the platform for
performance execution. This GPS receiver has been optimized for real-time
operation and has been developed for fast prototyping and easiness on future
additions and implementations to the system. The receiver DLLs are divided into
three baseband modules: acquisition, tracking, and navigation. The openness of
received baseband modules allows for extensive research topics such as signal
quality improvement on GPS-denied areas, signal spoofing, and signal
interferences. The hardware used in the system was chosen with an effort to
achieve portability and mobility in the SDR receiver. Several acceleration
factors that accomplish real-time operation and that are inherent to LabVIEW
mechanisms, such as multithreading, parallelization and dedicated
loop-structures, are discussed. The proposed SDR also exploits C/C++
optimization techniques for single-instruction multiple-data (SIMD) capable
processors in software correlators for real-time operation of GNSS tracking
loops. It is demonstrated that LabVIEW-based solutions provide competitive
real-time solutions for fast prototyping of receiver algorithms.
Fast prototyping of an SDR WLAN 802.11b receiver for an indoor positioning system
Date: 2019-06-30 00:00:00

Indoor positioning systems (IPS) are emerging technologies due to an
increasing popularity and demand in location based service (LBS). Because
traditional positioning systems such as GPS are limited to outdoor
applications, many IPS have been proposed in literature. WLAN-based IPS are the
most promising due to its proven accuracy and infrastructure deployment.
Several WLAN-based IPS have been proposed in the past, from which the best
results have been shown by so-called fingerprint-based systems. This paper
proposes an indoor positioning system which extends traditional WLAN
fingerprinting by using received signal strength (RSS) measurements along with
channel estimates as an effort to improve classification accuracy for scenarios
with a low number of Access Points (APs). The channel estimates aim to
characterize complex indoor environments making it a unique signature for
fingerprinting-based IPS and therefore improving pattern recognition in
radio-maps. Since commercial WLAN cards offer limited measurement information,
software-defined radio (SDR) as an emerging trend for fast prototyping and
research integration is chosen as the best cost-effective option to extract
channel estimates. Therefore, this paper first proposes an 802.11b WLAN SDR
beacon receiver capable of measuring RSS and channel estimates. The SDR is
designed using LabVIEW (LV) environment and leverages several inherent platform
acceleration features that achieve real-time capturing. The receiver achieves a
fast-rate measurement capture of 9 packets per second per AP. The
classification of the propose IPS uses a support vector machine (SVM) for
offline training and online navigation. Several tests are conducted in a
cluttered indoor environment with a single AP in 802.11b legacy mode. Finally,
navigation accuracy results are discussed.
Solving the Reswitching Paradox in the Sraffian Theory of Capital
Date: 2019-11-27 00:00:00

The possibility of re-switching of techniques in Piero Sraffa's intersectoral
model, namely the returning capital-intensive techniques with monotonic changes
in the profit rate, is traditionally considered as a paradox putting at stake
the viability of the neoclassical theory of production. It is argued here that
this phenomenon can be rationalized within the neoclassical paradigm. Sectoral
interdependencies can give rise to non-monotonic effects of progressive
variations in income distribution on relative prices. The re-switching of
techniques is, therefore, the result of cost-minimizing technical choices
facing returning ranks of relative input prices in full consistency with the
neoclassical perspective.
Size matters for OTC market makers: general results and dimensionality reduction techniques
Authors: Philippe Bergault, Olivier Guéant
Date: 2019-07-02 00:00:00
Tasks: Dimensionality Reduction

In most OTC markets, a small number of market makers provide liquidity to other market participants. More precisely, for a list of assets, they set prices at which they agree to buy and sell. Market makers face therefore an interesting optimization problem: they need to choose bid and ask prices for making money while mitigating the risk associated with holding inventory in a volatile market. Many market making models have been proposed in the academic literature, most of them dealing with single-asset market making whereas market makers are usually in charge of a long list of assets. The rare models tackling multi-asset market making suffer however from the curse of dimensionality when it comes to the numerical approximation of the optimal quotes. The goal of this paper is to propose a dimensionality reduction technique to address multi-asset market making by using a factor model. Moreover, we generalize existing market making models by the addition of an important feature: the existence of different transaction sizes and the possibility for the market makers in OTC markets to answer different prices to requests with different sizes.
Improved Circuit Design of Analog Joint Source Channel Coding for Low-power and Low-complexity Wireless Sensors
Date: 2019-06-30 00:00:00

To enable low-power and low-complexity wireless monitoring, an improved
circuit design of Analog Joint Source Channel Coding (AJSCC) is proposed for
wireless sensor nodes. This innovative design is based on Analog Divider Blocks
(ADB) with tunable spacing between AJSCC levels. The ADB controls the switching
between two types of Voltage Controlled Voltage Sources (VCVS). LTSpice
simulations were performed to evaluate the performance of the circuit, and the
power consumption and circuit complexity of this new ADB-based design were
compared with our previous parallel-VCVS design. It is found that this improved
circuit design based on ADB outperforms the design based on parallel VCVS for a
large number of AJSCC levels (>= 16), both in terms of power consumption as
well as circuit complexity, thus enabling persistent and higher
temporal/spatial resolution environmental sensing.
Enabling NB-IoT on Unlicensed Spectrum
Date: 2019-06-25 00:00:00

The deployment of Internet of Things (IoT) technologies in unlicensed
spectrum is a candidate feature for 5G to support massive connections of IoT
devices. Current IoT unlicensed band technologies, such as Sigfox and LoRa, are
all at an early stage of deployment without a significant market share. In this
context, the MulteFire (MF) Alliance has envisioned to adapt the cellular
NB-IoT design to operate within the Sub-1 GHz unlicensed spectrum. However, the
diverse regulatory requirements within this unlicensed band put a hurdle to the
world-wide deployment of unlicensed band IoT technologies. To settle this
challenge, MF has designed a specific framework for narrow-band (NB)-IoT
systems operating on unlicensed spectrum (NB-IoT-U), which can be utilized
under both the Federal Communications Commission (FCC) and European
Telecommunication Standards Institute (ETSI) regulatory requirements. In this
paper, enhanced synchronization and physical broadcasting signals are proposed
based upon the framework designed by MF with the aim to allow a more robust
detection, and to fulfil the coverage targets set for this technology.
Furthermore, in order to allow the system to operate as a frequency hopping
spread spectrum (FHSS) system, a novel frequency hopping pattern generator
compliant with the regulatory requirements is designed, and its performance is
evaluated.
Analytical Modeling of UAV-to-Vehicle Propagation Channels in Built-Up Areas
Date: 2019-06-26 00:00:00

This letter presents an analytical path loss model for air-ground (AG)
propagation between unmanned aerial vehicles (UAVs) and ground-based vehicles.
We consider built-up areas, such as the ones defined by ITU-R. The
three-dimensional (3D) path loss model is based on propagation conditions and
essential parameters are derived by using geometric methods. Owing to the
generality, the analytical model is capable of arbitrary deployments of
buildings, such as suburban, urban and dense urban. The analytical model is
evaluated numerically, and validations conducted by ray-tracing simulations
show the high accuracy of the proposed model. The closed-form analytical
formulas provide a useful tool for quick and accurate prediction of
UAV-to-vehicle propagation channels.
Target Flow-Pressure Operating Range for Designing a Failing Fontan Cavopulmonary Support Device
Date: 2020-02-15 00:00:00

Fontan operation as the current standard of care for the palliation of single
ventricle defects results in significant late complications. Using a mechanical
circulatory device for the right circulation to serve the function of the
missing subpulmonary ventricle could potentially stabilize the failing Fontan
circulation. This study aims to elucidate the hydraulic operating regions that
should be targeted for designing cavopulmonary blood pumps. By integrating
numerical analysis and available clinical information, the interaction of the
cavopulmonary support via the IVC and full assist configurations with a wide
range of simulated adult failing scenarios was investigated; with IVC and full
assist corresponding to the inferior venous return or the entire venous return,
respectively, being routed through the device. We identified the desired
hydraulic operating regions for a cavopulmonary assist device by clustering all
head pressures and corresponding pump flows that result in hemodynamic
improvement for each simulated failing Fontan physiology. Results show that IVC
support can produce beneficial hemodynamics in only a small fraction of failing
Fontan scenarios. Cavopulmonary assist device could increase cardiac index by
35% and decrease the inferior vena cava pressure by 45% depending on the
patient's pre-support hemodynamic state and surgical configuration of the
cavopulmonary assist device (IVC or full support). The desired flow-pressure
operating regions we identified can serve as the performance criteria for
designing cavopulmonary assist devices as well as evaluating off-label use of
commercially available left-side blood pumps for failing Fontan cavopulmonary
support.
An Econometric Perspective on Algorithmic Subsampling
Date: 2020-04-30 00:00:00

Datasets that are terabytes in size are increasingly common, but computer
bottlenecks often frustrate a complete analysis of the data. While more data
are better than less, diminishing returns suggest that we may not need
terabytes of data to estimate a parameter or test a hypothesis. But which rows
of data should we analyze, and might an arbitrary subset of rows preserve the
features of the original data? This paper reviews a line of work that is
grounded in theoretical computer science and numerical linear algebra, and
which finds that an algorithmically desirable sketch, which is a randomly
chosen subset of the data, must preserve the eigenstructure of the data, a
property known as a subspace embedding. Building on this work, we study how
prediction and inference can be affected by data sketching within a linear
regression setup. We show that the sketching error is small compared to the
sample size effect which a researcher can control. As a sketch size that is
algorithmically optimal may not be suitable for prediction and inference, we
use statistical arguments to provide 'inference conscious' guides to the sketch
size. When appropriately implemented, an estimator that pools over different
sketches can be nearly as efficient as the infeasible one using the full
sample.
Multi-Agent Safe Policy Learning for Power Management of Networked Microgrids
Date: 2020-10-27 00:00:00
Tasks: Management, Reinforcement Learning (RL)

This paper presents a supervised multi-agent safe policy learning (SMAS-PL)
method for optimal power management of networked microgrids (MGs) in
distribution systems. While conventional reinforcement learning (RL) algorithms
are black-box decision models that could fail to satisfy grid operational
constraints, our proposed method is constrained by AC power flow equations and
other operational limits. Accordingly, the training process employs the
gradient information of operational constraints to ensure that the optimal
control policy functions generate safe and feasible decisions. Furthermore, we
have developed a distributed consensus-based optimization approach to train the
agents' policy functions while maintaining MGs' privacy and data ownership
boundaries. After training, the learned optimal policy functions can be safely
used by the MGs to dispatch their local resources, without the need to solve a
complex optimization problem from scratch. Numerical experiments have been
devised to verify the performance of the proposed method.
Dominance, Sharing, and Assessment in an Iterated Hawk--Dove Game
Date: 2019-10-31 00:00:00

Animals use a wide variety of strategies to reduce or avoid aggression in
conflicts over resources. These strategies range from sharing resources without
outward signs of conflict to the development of dominance hierarchies, in which
initial fighting is followed by the submission of subordinates. Although models
have been developed to analyze specific strategies for resolving conflicts over
resources, little work has focused on trying to understand why particular
strategies are more likely to arise in certain situations. In this paper, we
use a model based on an iterated Hawk--Dove game to analyze how resource
holding potentials (RHPs) and other factors affect whether sharing, dominance
relationships, or other behaviours are evolutionarily stable. We find through
extensive numerical simulations that sharing is stable only when the cost of
fighting is low and the animals in a contest have similar RHPs, whereas
dominance relationships are stable in most other situations. We also explore
what happens when animals are unable to assess each other's RHPs without
fighting, and we compare a range of strategies for this problem using
simulations. We find (1) that the most successful strategies involve a limited
period of assessment followed by a stable relationship in which fights are
avoided and (2) that the duration of assessment depends both on the costliness
of fighting and on the difference between the animals' RHPs. Along with our
direct work on modeling and simulations, we develop extensive software to
facilitate further testing; it is available at
\url{https://bitbucket.org/CameronLHall/dominancesharingassessmentmatlab/}.
A Cognition-Affect Integrated Model of Emotion
Date: 2020-04-28 00:00:00
Tasks: Event Detection, model, Transfer Learning

The focus of the efforts for defining and modelling emotion is broadly
shifting from classical definite marker theory to statistically context
situated conceptual theory. However, the role of context processing and its
interaction with the affect is still not comprehensively explored and modelled.
With the help of neural decoding of functional networks, we have decoded
cognitive functions for 12 different basic and complex emotion conditions.
Using transfer learning in deep neural architecture, we arrived at the
conclusion that the core affect is unable to provide varieties of emotions
unless coupled with cortical cognitive functions such as autobiographical
memory, dmn, self-referential, social, tom and salient event detection.
Following our results, in this article, we present a 'cognition-affect
integrated model of emotion' which includes many cortical and subcortical
regions and their interactions. Our model suggests three testable hypotheses.
First, affect and physiological sensations alone are inconsequential in
defining or classifying emotions until integrated with the domain-general
cognitive systems. Second, cognition and affect modulate each other throughout
the generation of meaningful instance which is situated in the current context.
And, finally, the structural and temporal hierarchies in the brain's
organization and anatomical projections play an important role in emotion
responses in terms of hierarchical activities and their durations. The model,
along with the analytical and anatomical support, is presented. The article
concludes with the future research questions.