pwc-archive/papers-with-abstracts huggingface dataset:
Bayesian Parameter Estimation Applied to the Li-ion Battery Single Particle Model with Electrolyte Dynamics
Date: 2020-01-27 00:00:00
Tasks: parameter estimation

This paper presents a Bayesian parameter estimation approach and
identifiability analysis for a lithium-ion battery model, to determine the
uniqueness, evaluate the sensitivity and quantify the uncertainty of a subset
of the model parameters. The analysis was based on the single particle model
with electrolyte dynamics, rigorously derived from the Doyle-Fuller-Newman
model using asymptotic analysis including electrode-average terms. The Bayesian
approach allows complex target distributions to be estimated, which enables a
global analysis of the parameter space. The analysis focuses on the
identification problem (i) locally, under a set of discrete quasi-steady states
of charge, and in comparison (ii) globally with a continuous excursion of state
of charge. The performance of the methodology was evaluated using synthetic
data from multiple numerical simulations under diverse types of current
excitation. We show that various diffusivities as well as the transference
number may be estimated with small variances in the global case, but with much
larger uncertainty in the local estimation case. This also has significant
implications for estimation where parameters might vary as a function of state
of charge or other latent variables.
On the anti-cancer effect of cold atmospheric plasma and the possible role of catalase-dependent apoptotic pathways
Authors: Charlotta Bengtson, Annemie Bogaerts
Date: 2020-01-27 00:00:00

Cold atmospheric plasma (CAP) is a promising new agent for (selective) cancer treatment, but the underlying cause of the anti-cancer effect of CAP is not well understood yet. Among different theories and observations, one theory in particular has been postulated in great detail and consists of a very complex network of reactions that are claimed to account for the anti-cancer effect of CAP. Here, the key concept is a reactivation of two specific apoptotic cell signaling pathways through catalase inactivation caused by CAP. Thus, it is postulated that the anti-cancer effect of CAP is due to its ability to inactivate catalase, either directly or indirectly. A theoretical investigation of the proposed theory, especially the role of catalase inactivation, can contribute to the understanding of the underlying cause of the anti-cancer effect of CAP. In the present study, we develop a mathematical model to analyze the proposed catalase-dependent anti-cancer effect of CAP. Our results show that a catalase-dependent reactivation of the two apoptotic pathways of interest is unlikely to contribute to the observed anti-cancer effect of CAP. Thus, we believe that other theories of the underlying cause should be considered and evaluated to gain knowledge about the principles of CAP-induced cancer cell death.
Frame-based overlapping speech detection using Convolutional Neural Networks
Date: 2020-02-13 00:00:00

Naturalistic speech recordings usually contain speech signals from multiple
speakers. This phenomenon can degrade the performance of speech technologies
due to the complexity of tracing and recognizing individual speakers. In this
study, we investigate the detection of overlapping speech on segments as short
as 25 ms using Convolutional Neural Networks. We evaluate the detection
performance using different spectral features, and show that pyknogram features
outperforms other commonly used speech features. The proposed system can
predict overlapping speech with an accuracy of 84\% and Fscore of 88\% on a
dataset of mixed speech generated based on the GRID dataset.
A Python based automated tracking routine for myosin II filaments
Authors: Lewis Mosby, Marco Polin, Darius V. Köster
Date: 2020-01-27 00:00:00

The study of motor protein dynamics within cytoskeletal networks is of high interest to physicists and biologists to understand how the dynamics and properties of individual motors lead to cooperative effects and control of overall network behaviour. Here, we report a method to detect and track muscular myosin II filaments within an actin network tethered to supported lipid bilayers. Based on the characteristic shape of myosin II filaments, this automated tracking routine allowed us to follow the position and orientation of myosin II filaments over time, and to reliably classify their dynamics into segments of diffusive and processive motion based on the analysis of displacements and angular changes between time steps. This automated, high throughput method will allow scientists to efficiently analyse motor dynamics in different conditions, and will grant access to more detailed information than provided by common tracking methods, without any need for time consuming manual tracking or generation of kymographs.
OMAP-L138 LCDK Development Kit
Date: 2020-01-13 00:00:00

Low cost and low power consumption processor play a vital role in the field
of Digital Signal Processing (DSP). The OMAP-L138 development kit which is low
cost, low power consumption, ease and speed, with a wide variety of
applications includes Digital signal processing, Image processing and video
processing. This paper represents the basic introduction to OMAP-L138 processor
and quick procedural steps for real time and non-real time implementations with
a set of programs. The real time experiments are based on audio in the
applications of audio loopback, delay and echo. Whereas the non-real time
experiments are generation of a sine wave, low pass and high pass filter.
Two-Step Phase Shifting Algorithms: Where Are We?
Date: 2020-01-27 00:00:00
Tasks: Vocal Bursts Valence Prediction

Two steps phase shifting interferometry has been a hot topic in the recent
years. We present a comparison study of 12 representative self--tunning
algorithms based on two-steps phase shifting interferometry. We evaluate the
performance of such algorithms by estimating the phase step of synthetic and
experimental fringe patterns using 3 different normalizing processes: Gabor
Filters Bank (GFB), Deep Neural Networks (DNNs) and Hilbert Huang Transform
(HHT); in order to retrieve the background, the amplitude modulation and noise.
We present the variants of state-of-the-art phase step estimation algorithms by
using the GFB and DNNs as normalization preprocesses, as well as the use of a
robust estimator such as the median to estimate the phase step. We present
experimental results comparing the combinations of the normalization processes
and the two steps phase shifting algorithms. Our study demonstrates that the
quality of the retrieved phase from of two-step interferograms is more
dependent of the normalizing process than the phase step estimation method.
Simultaneous State and Unknown Input Set-Valued Observers for Some Classes of Nonlinear Dynamical Systems
Date: 2020-01-27 00:00:00
Tasks: Form

In this paper, we propose fixed-order set-valued (in the form of l2-norm hyperballs) observers for some classes of nonlinear bounded-error dynamical systems with unknown input signals that simultaneously find bounded hyperballs of states and unknown inputs that include the true states and inputs. Necessary and sufficient conditions in the form of Linear Matrix Inequalities (LMIs) for the stability (in the sense of quadratic stability) of the proposed observers are derived for ($\mathcal{M},\gamma$)- Quadratically Constrained (($\mathcal{M},\gamma$)-QC) systems, which includes several classes of nonlinear systems: (I) Lipschitz continuous, (II) ($\mathcal{A},\gamma$)-QC* and (III) Linear Parameter-Varying (LPV) systems. This new quadratic constraint property is at least as general as the incremental quadratic constraint property for nonlinear systems and is proven in the paper to embody a broad range of nonlinearities. In addition, we design the optimal $\mathcal{H}_{\infty}$ observer among those that satisfy the quadratic stability conditions and show that the design results in Uniformly Bounded-Input Bounded-State (UBIBS) estimate radii/error dynamics and uniformly bounded sequences of the estimate radii. Furthermore, we provide closed-form upper bound sequences for the estimate radii and sufficient condition for their convergence to steady state. Finally, the effectiveness of the proposed set-valued observers is demonstrated through illustrative examples, where we compare the performance of our observers with some existing observers.
Coalitional predictive control: consensus-based coalition forming with robust regulation
Date: 2020-01-28 00:00:00
Tasks: Model Predictive Control

This paper is concerned with the problem of controlling a system of
constrained dynamic subsystems in a way that balances the performance
degradation of decentralized control with the practical cost of centralized
control. We propose a coalitional control scheme in which controllers of
subsystems may, as the need arises, group together into coalitions and operate
as a single entity. The scheme employs a robust form of distributed model
predictive control for which recursive feasibility and stability are
guaranteed, yet---uniquely---the reliance on robust invariant sets is merely
implicit, thus enabling applicability to higher-order systems. The robust
control algorithm is combined with an algorithm for coalition forming based on
consensus theory and potential games; we establish conditions under which
controllers reach a consensus on the sets of coalitions. The recursive
feasibility and closed-loop stability of the overall time-varying coalitional
control scheme are established under a sufficient dwell time, the existence of
which is guaranteed.
Path following of a class of underactuated mechanical systems via immersion and invariance-based orbital stabilization
Date: 2020-01-28 00:00:00

This paper aims to provide a new problem formulation of path following for
mechanical systems without time parameterization nor guidance laws, namely, we
express the control objective as an orbital stabilization problem. It is shown
that, it is possible to adapt the immersion and invariance technique to design
static state-feedback controllers that solve the problem. In particular, we
select the target dynamics adopting the recently introduced Mexican sombrero
energy assignment method. To demonstrate the effectiveness of the proposed
method we apply it to control underactuated marine surface vessels.
Unexpected parameter ranges of the 2009 A(H1N1) epidemic for Istanbul and the Netherlands
Date: 2020-01-28 00:00:00

The data of the 2009 A(H1N1) epidemic in Istanbul, Turkey is unique in terms
of the collected data, which include not only the hospitalization but also the
fatality information recorded during the pandemic. The analysis of this data
displayed an unexpected time shift between the hospital referrals and
fatalities. This time shift, which does not conform to the SIR and SEIR models,
was explained by multi-stage SIR and SEIR models [21]. In this study we prove
that the delay for these models is half of the infectious period within a
quadratic approximation, and we determine the epidemic parameters $R_0$, $T$
and $I_0$ of the 2009 A(H1N1) Istanbul and Netherlands epidemics.These epidemic
parameters were estimated by comparing the normalized cumulative fatality data
with the solutions of the SIR model. Two different error criteria, the $L_2$
norms of the error over the whole observation period and over the initial
portion of the data, were used in order to obtain the best-fitting models. It
was observed that, with respect to both criteria, the parameters of "good"
models were agglomerated along a line in the $T$-$R_0$ plane, instead of being
scattered uniformly around a "best" model. As this fact indicates the existence
of a nearly invariant quantity, interval estimates for the parameters were
given. As the initial phase of the epidemics were less influenced by the
effects of medical interventions, the error norm based on the initial portion
of the data was preferred. However, the presented parameter ranges are well out
of the range for the usual influenza epidemic parameter values. To confirm our
observations on the Istanbul data, the same error criteria were also used for
the 2009 A(H1N1) epidemic for the Netherlands, which has a similar population
density as in Istanbul. As in the Istanbul case, the parameter ranges do not
match the usual influenza epidemic parameter values.
Improving operational flexibility of integrated energy system with uncertain renewable generations considering thermal inertia of buildings
Date: 2020-01-25 00:00:00
Tasks: Scheduling

Insufficient flexibility in system operation caused by traditional "heat-set"
operating modes of combined heat and power (CHP) units in winter heating
periods is a key issue that limits renewable energy consumption. In order to
reduce the curtailment of renewable energy resources through improving the
operational flexibility, a novel optimal scheduling model based on
chance-constrained programming (CCP), aiming at minimizing the lowest
generation cost, is proposed for a small-scale integrated energy system (IES)
with CHP units, thermal power units, renewable generations and representative
auxiliary equipments. In this model, due to the uncertainties of renewable
generations including wind turbines and photovoltaic units, the probabilistic
spinning reserves are supplied in the form of chance-constrained; from the
perspective of user experience, a heating load model is built with
consideration of heat comfort and inertia in buildings. To solve the model, a
solution approach based on sequence operation theory (SOT) is developed, where
the original CCP-based scheduling model is tackled into a solvable
mixed-integer linear programming (MILP) formulation by converting a chance
constraint into its deterministic equivalence class, and thereby is solved via
the CPLEX solver. The simulation results on the modified IEEE 30-bus system
demonstrate that the presented method manages to improve operational
flexibility of the IES with uncertain renewable generations by comprehensively
leveraging thermal inertia of buildings and different kinds of auxiliary
equipments, which provides a fundamental way for promoting renewable energy
consumption.
Change of measure under the hard-to-borrow model
Date: 2020-01-28 00:00:00

As the Securities and Exchange Commission(SEC) has implemented a new
regulation on short-sellings, short-sellers are required to repurchase stocks
once the clearing risk rises to a certain level. Avellaneda and Lipkin proposed
a fully coupled SDE system to describe the mechanism which is referred as
Hard-To-Borrow(HTB) models. Guiyuan Ma obtained the PDE system for both
American and European options. There is a technical error in Guiyuan Ma where
two correlated Brownian motion should be converted before change of measure. In
this paper, I will provide supplement conditions.
Predicting the Impact of Electric Field Stimulation in a Detailed Computational Model of Cortical Tissue
Date: 2020-01-28 00:00:00

Neurostimulation using weak electric fields has generated excitement in
recent years due to its potential as a medical intervention. However, study of
this stimulation modality has been hampered by inconsistent results and large
variability within and between studies. In order to begin addressing this
variability we need to properly characterise the impact of the current on the
underlying neuron populations. To develop and test a computational model
capable of capturing the impact of electric field stimulation on networks of
neurons. We construct a cortical tissue model with distinct layers and explicit
neuron morphologies. We then apply a model of electrical stimulation and carry
out multiple test case simulations. The cortical slice model is compared to
experimental literature and shown to capture the main features of the
electrophysiological response to stimulation. Namely, the model showed 1) a
similar level of depolarisation in individual pyramidal neurons, 2)
acceleration of intrinsic oscillations, and 3) retention of the spatial profile
of oscillations in different layers. We then apply alternative electric fields
to demonstrate how the model can capture differences in neuronal responses to
the electric field. We demonstrate that the tissue response is dependent on
layer depth, the angle of the apical dendrite relative to the field, and
stimulation strength. We present publicly available computational modelling
software that predicts the neuron network population response to electric field
stimulation.
Investment behavior and firms' financial performance: A comparative analysis using firm-level data from the wine industry
Authors: Claudiu Albulescu
Date: 2020-01-28 00:00:00

This paper assesses the role of financial performance in explaining firms' investment dynamics in the wine industry from the three European Union (EU) largest producers. The wine sector deserves special attention to investigate firms' investment behavior given the high competition imposed by the latecomers. More precisely, we investigate how the capitalization, liquidity and profitability influence the investment dynamics using firm-level data from the wine industry from France (331 firms), Italy (335) firms and Spain (442) firms. We use data from 2007 to 2014, drawing a comparison between these countries, and relying on difference-and system-GMM estimators. Specifically, the impact of profitability is positive and significant, while the capitalization has a significant and negative impact on the investment dynamics only in France and Spain. The influence of the liquidity ratio is negative and significant only in the case of Spain. Therefore, we notice different investment strategies for wine companies located in the largest producer countries. It appears that these findings are in general robust to different specifications of liquidity and profitability ratios, and to the different estimators we use.
Differentiation of neural-type cells on multi-scale ordered collagen-silica bionanocomposites
Date: 2020-01-28 00:00:00

Cells respond to biophysical and biochemical signals. We developed a
composite filament from collagen and silica particles modified to interact with
collagen and/or present a laminin epitope (IKVAV) crucial for cell-matrix
adhesion and signal transduction. This combines scaffolding and signaling and
shows that local tuning of collagen organization enhances cell differentiation.
Range-Max Enhanced Ultra-Wideband Micro-Doppler Signatures of Behind Wall Indoor Human Activities
Date: 2020-01-29 00:00:00
Tasks: Classification, Decision Making

Penetrating detection and recognition of behind wall indoor human activities
has drawn great attentions from social security and emergency service
department in recent years since intelligent surveillance aforehand could avail
the proper decision making before operations being carried out. However, due to
the influence of the wall effects, the obtained micro-Doppler signatures would
be severely degenerated by strong near zero-frequency DC components, which
would inevitably smear the detailed characteristic features of different behind
wall motions in time-frequency (TF) map and further hinder the motion
recognition and classification. In this paper, an ultra-wideband (UWB) radar
system is first employed to probe through the opaque wall to detect the behind
wall motions, which often span a certain number of range bin cells. By
employing such a system, a high resolution range map can be obtained, in which
the embedded rich range information is expected to be fully exploited to
improve the subsequent recognition and classification performance. Secondly, a
high-pass filter is applied to remove the effect of the wall in the raw range
map. Then, with the aim of enhancing the characteristic features of different
behind wall motions in TF maps, a novel range-max enhancement strategy is
proposed to extract the most significant micro-Doppler feature of each TF cell
along all range bins for a specific motion. Lastly, the effectiveness of the
proposed micro-Doppler signature enhancement strategy is investigated by means
of onsite experiments and comparative classification. Both the feature enhanced
TF maps and classification results show that the proposed approach outperforms
other state-of-art Short-Time Fourier Transform (STFT) based TF feature
extraction methods.
Localization in Autonomous Vehicles Using a Generalized Inner Product
Date: 2020-01-28 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles

Fine localization in autonomous driving platforms is a task of broad
interest, receiving much attention in recent years. Some localization
algorithms use the Euclidean distance as a similarity measure between the local
image acquired by a camera and a global map, which acts as side information.
The global map is typically expressed in terms of the coordinate system of the
road plane. Yet, a road image captured by a camera is subject to distortion in
that nearby features on the road have much larger footprints on the focal plane
of the camera compared with those of equally-sized features that lie farther
ahead of the vehicle. Using commodity computational tools, it is
straightforward to execute a transformation and, thereby, bring the distorted
image into the frame of reference of the global map. However, this nonlinear
transformation results in unequal noise amplification. The noise profile
induced by this transformation should be accounted for when trying to match an
acquired image to a global map, with more reliable regions being given more
weight in the process. This physical reality presents an algorithmic
opportunity to improve existing localization algorithms, especially in harsh
conditions. This article reviews the physics of road feature acquisition
through a camera, and it proposes an improved matching method rooted in
statistical analysis. Findings are supported by numerical simulations.
Closed-loop frequency analyses of reset systems
Date: 2020-08-28 00:00:00

Today, linear controllers cannot satisfy requirements of high-tech industry
due to fundamental limitations like the waterbed effect. This is one of the
reasons why nonlinear controllers, such as reset elements, are receiving
increased attention. To analyze reset elements in the frequency domain,
researchers use the Describing Function (DF) method. However, it cannot
accurately predict the closed-loop frequency responses of the system because it
neglects high order harmonics. To overcome these barriers, this paper proposes
a mathematical framework to model the closed-loop frequency responses of reset
systems including the closed-loop high order harmonics. Furthermore,
pseudo-sensitivities for reset systems are defined to make their analyses more
straight-forward. In addition, a user-friendly toolbox is developed based on
the proposed approach to facilitate frequency analyses of reset systems. To
show the effectiveness of the method, multiple illustrative examples on a
high-tech precision positioning stage are used to compare the results of the
closed-loop frequency responses obtained using our proposed method with DF
method. The results demonstrated that the proposed method is significantly more
precise than the DF method. Indeed, this developed toolbox can enable reset
controllers to be widely-used in industry and academia.