pwc-archive/papers-with-abstracts huggingface dataset:
Supervised load identification of 18 fixed-speed motors based on their turn-on transient current
Date: 2019-11-26 00:00:00

Several studies have already shown that the transient current can be
successfully used to identify electric loads. However, most of the proposed
methods were validated using only a handful of loads whose electric properties
often differed significantly from each other. Therefore, a much more
challenging empirical study was carried out here using 18 different fixed-speed
motors in a real work environment. A further difficulty was introduced by
normalizing the current amplitude to a value of 1~A during steady state. It is
shown that a classifier can distinguish these 18 motors even under those
conditions with an f1-score of 97.7% after a supervised training period. In
addition to that it was tested whether the mechanical output type (pump, fan or
compressor) of the fixed-speed motors could be inferred using only the
transient current. However, the classification results indicate that this is
not possible.
Autonomous WiFi Fingerprinting for Indoor Localization
Date: 2019-11-26 00:00:00
Tasks: Indoor Localization

WiFi-based indoor localization has received extensive attentions from both
academia and industry. However, the overhead of constructing and maintaining
the WiFi fingerprint map remains a bottleneck for the wide-deployment of
WiFi-based indoor localization systems. Recently, robots are adopted as the
professional surveyor to fingerprint the environment autonomously. But the time
and energy cost still limit the coverage of the robot surveyor, thus reduce its
scalability. To fill this need, we design an AutonomousWiFi Fingerprinting
system, called AuF, which autonomously constructs the fingerprint database with
time and energy efficiency. AuF first conduct an automatic initialization
process in the target indoor environment, then constructs the WiFi fingerprint
database of in two steps: (i) surveying the site without sojourn, (ii)
recovering unreliable signals in the database with two methods. We have
implemented and evaluated AuF using a Pioneer 3-DX robot, on two sites of our
$70$$\times$$90$m$^2$ Department building with different structures and
deployments of access points (APs). The results show AuF finishes the
fingerprint database construction in 43/51 minutes, and consumes 60/82 Wh on
the two floors respectively, which is a 64%/71% and 61%/64% reduction when
compared to traditional site survey methods, without degrading the localization
accuracy.
A note on observation processes in epidemic models
Date: 2019-11-27 00:00:00

Many disease models focus on characterizing the underlying transmission
mechanism but make simple, possibly naive assumptions about how infections are
reported. In this note, we use a simple deterministic
Susceptible-Infected-Removed (SIR) model to compare two common assumptions
about disease incidence reports: individuals can report their infection as soon
as they become infected or as soon as they recover. We show that incorrect
assumptions about the underlying observation processes can bias estimates of
the basic reproduction number and lead to overly narrow confidence intervals.
Characteristics Of Post Partum Perineum Wounds With Infra Red Therapy
Authors: Bina Melvia Girsang, Eqlima Elvira
Date: 2019-11-27 00:00:00
Tasks: Descriptive

Perineal wounds other than their localization in humid feminine areas and can pose a risk of infection. This quantitative study aims to see a description of the characteristics of post partum maternal perineal wounds with infrared therapy twice a day for three consecutive days with a descriptive analytic analysis design. Sample selection technique with purposive sampling in post partum mothers with spontaneous parturition, 1-2 degrees of perineal wounds in the amount of 20 people. Perineal wound characteristics were assessed and observed using the Southampton instrument with the results of the instrument trial r = 0.99 (Karl Pearson correlation coefficient), and the reliability value of 0.99 (brown spearman). The results of this study showed that degeneration of wounds was found only until the first day of post-intervention (1.95 ), poor wound regeneration occurred until the second day of pre-intervention (1.95), and wound regeneration was being encountered changes from the first day to the second day pre-intervention (1.05). These results indicate that infrared therapy does not show significant changes if only done for one day. Key Words: localization, parturition, regeneration
Mortality for circulatory and respiratory causes associated with influenza circulation in Russia during the 2013/14 through the 2018/19 seasons
Date: 2019-11-27 00:00:00
Tasks: Epidemiology

Background: Information on influenza-associated mortality in Russia is
limited.
  Methods: Using previously developed methodology (Goldstein et al.,
Epidemiology 2012), we regressed the monthly rates of mortality for respiratory
causes, as well as circulatory causes linearly against the monthly proxies for
the incidence of influenza A/H3N2, A/H1N1 and B (obtained using data from the
Smorodintsev Research Institute of Influenza (RII)), adjusting for the baseline
rates of mortality not associated with influenza circulation and temporal
trends.
  Results: For the 2013/14 through the 2018/19 seasons, influenza circulation
was associated with an average annual 17636 (95% CI (9482,25790)) deaths for
circulatory causes and 4179 (3250,5109) deaths for respiratory causes, with the
largest number of deaths (32298 (18071,46525) for circulatory causes and 6689
(5019,8359) for respiratory causes) estimated during the 2014/15 influenza
season. The biggest contributor to both circulatory and respiratory
influenza-associated deaths was influenza A/H3N2, followed by influenza B and
A/H1N1. Compared to the 2013/14 through the 2015/16 seasons, during the 2016/17
through the 2018/19 seasons (when levels of influenza vaccination were
significantly higher), the number of influenza-associated deaths declined by
about 16.1%, or 3809 annual respiratory and circulatory deaths.
  Conclusions: Influenza circulation is associated with a substantial mortality
burden in Russia, particularly for circulatory deaths, with some reduction
observed following the major increase in vaccination coverage. Those results
support the potential utility of further extending the levels of influenza
vaccination, the use of quadrivalent influenza vaccines, and extra efforts for
protecting individuals with circulatory disease in Russia, including
vaccination and the use of antiviral medications.
Data-Driven Recursive Least Squares Estimation for Model Predictive Current Control of Permanent Magnet Synchronous Motors
Date: 2019-11-27 00:00:00

The performance of model predictive controllers (MPC) strongly depends on the
model quality. In the field of electric drive control, white-box (WB) modeling
approaches derived from first-order physical principles are most common. This
procedure typically does not cover parasitic effects and parameter deviations
are frequent. These issues are particularly crucial in the domain of
self-commissioning drives when a hand-tailored, accurate WB plant model is not
available. In order to compensate for such modeling errors and, therefore, to
improve the control performance during transients and steady-state, this paper
proposes a data-driven, real-time capable recursive least squares (RLS)
estimation method for the current control of a permanent magnet synchronous
motor (PMSM). The effect of the flux linkage and voltage harmonics due to the
winding scheme can also be taken into account. Moreover, a compensating scheme
for the interlocking time of the inverter is proposed. The proposed
identification algorithm is investigated using the well-known
finite-control-set MPC (FCS-MPC) in the rotor-oriented coordinate system. The
extensive experimental results show the superior performance of the presented
scheme compared to a FCS-MPC-based on a state-of-the-art WB motor model using
look-up tables for adressing (cross-)saturation.
Demand Side Management for Homes in Smart Grids
Date: 2019-11-26 00:00:00
Tasks: energy management, Management, Scheduling

Electricity usage is a major portion of utility bills and the best place to
start lowering them. An effective home energy management approach is introduced
to decrease customers' electricity bills by determining the optimal appliance
scheduling under hourly pricing based demand response (DR) strategies. The
proposed approach specifically addresses consumer comfort through acceptable
appliance deferral times, within which their consumption can be shifted from
the normal schedules. Electric vehicles (EVs), with vehicle to grid (V2G) and
vehicle-to-home (V2H) abilities, Energy storage systems (ESS), and Renewable
Energy Resource as a distributed generation are considered at the end-user
premises to improve the energy management efficiency. A mixed-integer linear
programming (MILP) framework is implemented to solve the proposed problem.
Numerical results show that the proposed approach can reduce customers'
electricity bill without sacrificing their comfort and fully exploit EVs and
ESS capacities.
Intelligent Reflecting Surface for Massive Device Connectivity: Joint Activity Detection and Channel Estimation
Date: 2019-11-12 00:00:00
Tasks: Action Detection, Activity Detection, Matrix Completion

Intelligent Reflecting Surface (IRS) has been a promising solution to enhance
wireless networks both spectral-efficiently and energy-efficiently. This paper
considers an IRS-assisted the Internet of Things network for massive
connectivity. We aim to solve the IRS-related activity detection and channel
estimation problem which has not been studied before. In this paper, we
formulate the IRS-related activity detection and channel estimation problem as
sparse matrix factorization, matrix completion and Multiple Measurement Vector
problem and, we propose a three-stage framework based on the approximate
message passing. Simulation results verify the effectiveness of the proposed
algorithm.
Introduction to Solving Quant Finance Problems with Time-Stepped FBSDE and Deep Learning
Authors: Bernhard Hientzsch
Date: 2019-11-27 00:00:00

In this introductory paper, we discuss how quantitative finance problems under some common risk factor dynamics for some common instruments and approaches can be formulated as time-continuous or time-discrete forward-backward stochastic differential equations (FBSDE) final-value or control problems, how these final value problems can be turned into control problems, how time-continuous problems can be turned into time-discrete problems, and how the forward and backward stochastic differential equations (SDE) can be time-stepped. We obtain both forward and backward time-stepped time-discrete stochastic control problems (where forward and backward indicate in which direction the Y SDE is time-stepped) that we will solve with optimization approaches using deep neural networks for the controls and stochastic gradient and other deep learning methods for the actual optimization/learning. We close with examples for the forward and backward methods for an European option pricing problem. Several methods and approaches are new.
Robust Economic Model Predictive Control without Terminal Conditions
Date: 2020-07-27 00:00:00
Tasks: Model Predictive Control

In this paper, a novel tube-based economic Model Predictive Control (MPC)
scheme for uncertain systems that uses neither terminal costs nor terminal
constraints is investigated. We show that the results from the undisturbed case
can be extended to systems with bounded disturbances by using similar turnpike
arguments and a properly modified stage cost. We prove robust guarantees on the
closed-loop performance, convergence, and stability under suitable
dissipativity and controllability conditions and discuss them in a numerical
example.
Power System Decarbonization: Impacts of Energy Storage Duration and Interannual Renewables Variability
Date: 2019-11-27 00:00:00
Tasks: Decision Making

Decarbonization of the electricity sector is one of the major measures in
slowing down the pace of climate change. In this paper, we analyze the impacts
of energy storage systems (ESS) and interannual uncertainty of variable
renewable energy (VRE) on power system decarbonization in 2050. We perform
capacity expansion optimization based on technology cost projections and CO2
emission restrictions using 11 years of VRE and load data in Italy's power
system, with a particular focus on the role of ESS and its duration. We also
explore capacity expansion optimization based on multiple-year vs. single-year
data to quantify the impact of VRE interannual variability. Our results
indicate high renewables penetration even in the absence of decarbonization
policies. In the transition to zero carbon system, CCS plays a minor role due
to its carbon capture efficiency. ESS investments contribute to lower system
costs by replacing more expensive flexibility resources. However, longer ESS
durations have lower marginal value per added kWh ESS. Interannual variability
of VRE substantially changes the system's configuration and energy cost.
Decision-making based on single-year data substantially increases the systems'
operational costs in other years. In contrast, optimizing over multiple years
provides a more robust and cost effective generation expansion strategy.
Two-Stage Learning for Uplink Channel Estimation in One-Bit Massive MIMO
Date: 2019-11-27 00:00:00
Tasks: Deep Learning, Denoising, Quantization

We develop a two-stage deep learning pipeline architecture to estimate the
uplink massive MIMO channel with one-bit ADCs. This deep learning pipeline is
composed of two separate generative deep learning models. The first one is a
supervised learning model and designed to compensate for the quantization loss.
The second one is an unsupervised learning model and optimized for denoising.
Our results show that the proposed deep learning-based channel estimator can
significantly outperform other state-of-the-art channel estimators for one-bit
quantized massive MIMO systems. In particular, our design provides 5-10 dB gain
in channel estimation error. Furthermore, it requires a reasonable amount of
pilots, on the order of 20 per coherence time interval.
Spectrum Cartography via Coupled Block-Term Tensor Decomposition
Authors: Guoyong Zhang, Xiao Fu, Jun Wang, Xi-Le Zhao, Mingyi Hong
Date: 2019-11-28 00:00:00
Tasks: Spectrum Cartography, Tensor Decomposition

Spectrum cartography aims at estimating power propagation patterns over a geographical region across multiple frequency bands (i.e., a radio map)---from limited samples taken sparsely over the region. Classic cartography methods are mostly concerned with recovering the aggregate radio frequency (RF) information while ignoring the constituents of the radio map---but fine-grained emitter-level RF information is of great interest. In addition, many existing cartography methods work explicitly or implicitly assume random spatial sampling schemes that may be difficult to implement, due to legal/privacy/security issues. The theoretical aspects (e.g., identifiability of the radio map) of many existing methods are also unclear. In this work, we propose a joint radio map recovery and disaggregation method that is based on coupled block-term tensor decomposition. Our method guarantees identifiability of the individual radio map of \textit{each emitter} (thereby that of the aggregate radio map as well), under realistic conditions. The identifiability result holds under a large variety of geographical sampling patterns, including a number of pragmatic systematic sampling strategies. We also propose effective optimization algorithms to carry out the formulated radio map disaggregation problems. Extensive simulations are employed to showcase the effectiveness of the proposed approach.
System Integration and Control Design of a Maglev Platform for Space Vibration Isolation
Date: 2020-03-25 00:00:00

Micro-vibration has been a dominant factor impairing the performance of
scientific experiments which are expected to be deployed in a micro-gravity
environment such as Spacelab. The micro-vibration has a serious impact on
scientific experiments requiring a quasi-static environment. Therefore, we
proposed a maglev vibration isolation platform (MVIP) operating in six degrees
of freedom (DOF) to fulfill the environmental requirements. In view of
non-contact and large stroke requirements for micro-vibration isolation, an
optimization method was utilized to design the actuator. Mathematical models of
the actuator's remarkable nonlinearity were established so that its output can
be compensated according to floater's varying position and the system's
performance may be satisfied. Furthermore, aiming to adapt to an energy-limited
environment such as Spacelab, an optimum allocation scheme was put forward.
Considering the actuator's nonlinearity, accuracy and minimum
energy-consumption can be obtained simultaneously. In view of operating in six
DOF, methods for nonlinear compensation and system decoupling were discussed,
the necessary controller was also presented. Simulation and experiments
validate the system's performance. With a movement range of 10x10x8 mm and
rotations of 200 mrad, the decay ratio of -40 dB/Dec between 1-10 Hz was
obtained under closed-loop control.
Abnormal source identification for parabolic distributed parameter systems
Date: 2019-11-28 00:00:00

Identification of abnormal source hidden in distributed parameter systems
(DPSs) belongs to the category of inverse source problems. It is important in
industrial applications but seldom studied. In this paper, we make the first
attempt to investigate the abnormal spatio-temporal (S-T) source identification
for a class of DPSs. An inverse S-T model for abnormal source identification is
developed for the first time. It consists of an adaptive state observer for
source identification and an adaptive source estimation algorithm. One major
advantage of the proposed inverse S-T model is that only the system output is
utilized, without any state measurement. Theoretic analysis is conducted to
guarantee the convergence of the estimation error. Finally, the performance of
the proposed method is evaluated on a heat transfer rod with an abnormal S-T
source.
Reaction Asymmetries to Social Responsibility Index Recomposition: A Matching Portfolio Approach
Date: 2019-11-28 00:00:00

Listing on the Dow Jones Sustainability Index is seen as a gold-standard,
verifying to the market that a firm is fully engaged with a corporate social
responsibility agenda. Robustly quantifying the impact of listing, and
de-listing, against any industry level shocks, as well as evolution in the
competitive relationship between firms within the industry, provides a strength
absent in existing works. It is shown that cumulative abnormal returns on
stocks added to the index are significantly positive in the three trading weeks
prior to the official announcement. The post-listing correction result posited
to date is also demonstrated to hold; the proportion of periods with
significant negative returns is low, however. Announcement, rather than
effective dates are critical to returns. Differentials between these stages in
the chronology is an important contribution of this paper. Most effects end
before the membership changes become effective. Whilst there are considerable
gains to be made, they come pre-announcement date and require foresight to
exploit. Investors must research likely new members to gain maximum return.
Data Transmission based on Exact Inverse Periodic Nonlinear Fourier Transform, Part I: Theory
Authors: Jan-Willem Goossens, Hartmut Hafermann, Yves Jaouën
Date: 2019-11-28 00:00:00

The nonlinear Fourier transform (NFT) decomposes waveforms propagating through optical fiber into nonlinear degrees of freedom, which are preserved during transmission. By encoding information on the nonlinear spectrum, a transmission scheme inherently compatible with the nonlinear fiber is obtained. Despite potential advantages, the periodic NFT (PNFT) has been studied less compared to its counterpart based on vanishing boundary conditions, due to the mathematical complexity of the inverse transform. In this paper we extract the theory of the algebrogeometric integration method underlying the inverse PNFT from the literature, and tailor it to the communication problem. We provide a complete algorithm to compute the inverse PNFT. As an application, we employ the algorithm to design a novel modulation scheme called nonlinear frequency amplitude modulation, where four different nonlinear frequencies are modulated independently. Finally we provide two further modulation schemes that may be considered in future research. The algorithm is further applied in Part II of this paper to the design of a PNFT-based communication experiment.
Data Transmission based on Exact Inverse Periodic Nonlinear Fourier Transform, Part II: Waveform Design and Experiment
Authors: Jan-Willem Goossens, Hartmut Hafermann, Yves Jaouën
Date: 2019-11-28 00:00:00

The nonlinear Fourier transform has the potential to overcome limits on performance and achievable data rates which arise in modern optical fiber communication systems when nonlinear interference is treated as noise. The periodic nonlinear Fourier transform (PNFT) has been much less investigated compared to its counterpart based on vanishing boundary conditions. In this paper, we design a first experiment based on the PNFT in which information is encoded in the invariant nonlinear main spectrum. To this end, we propose a method to construct a set of periodic waveforms each having the same fixed period, by employing the exact inverse PNFT algorithm developed in Part I. We demonstrate feasibility of the transmission scheme in experiment in good agreement with simulations and obtain a bit-error ratio of $10^{-3}$ over a distance of 2000 km. It is shown that the transmission reach is significantly longer than expected from a naive estimate based on group velocity dispersion and cyclic prefix length, which is explained through a dominating solitonic component in the transmitted waveform. Our constellation design can be generalized to an arbitrary number of nonlinear degrees of freedom.
Learning switched systems from simulation models
Date: 2020-02-12 00:00:00
Tasks: Active Learning

The design of decision and control strategies for switched systems typically
requires complete knowledge of (i) mathematical models of the subsystems and
(ii) restrictions on admissible switches between the subsystems. We propose an
active learning algorithm that infers (i) and (ii) for discrete-time switched
systems whose subsystems dynamics are governed by sets of scalar polynomials
and switching signals are constrained by automata. We collect data from
gray-box simulation models of the switched systems for this purpose. Our
technique for learning (i) involves linear algebraic tools, while for learning
(ii) we employ a modified version of the well-known $L^*$-algorithm from
machine learning literature. A numerical example is presented to demonstrate
our learning algorithm.