pwc-archive/papers-with-abstracts huggingface dataset:
Analytical solution to swing equations in power grids
Date: 2019-11-25 00:00:00
Tasks: Form

Objective: To derive a closed-form analytical solution to the swing equation
describing the power system dynamics, which is a nonlinear second order
differential equation. Existing challenges: No analytical solution to the swing
equation has been identified, due to the complex nature of power systems. Two
major approaches are pursued for stability assessments on systems: (1)
computationally simple models based on physically unacceptable assumptions, and
(2) digital simulations with high computational costs. Motivation: The motion
of the rotor angle that the swing equation describes is a vector function.
Often, a simple form of the physical laws is revealed by coordinate
transformation. Methods: The study included the formulation of the swing
equation in the Cartesian coordinate system, which is different from
conventional approaches that describe the equation in the polar coordinate
system. Based on the properties and operational conditions of electric power
grids referred to in the literature, we identified the swing equation in the
Cartesian coordinate system and derived an analytical solution within a
validity region. Results: The estimated results from the analytical solution
derived in this study agree with the results using conventional methods, which
indicates the derived analytical solution is correct. Conclusion: An analytical
solution to the swing equation is derived without unphysical assumptions, and
the closed-form solution correctly estimates the dynamics after a fault occurs.
Orienting Ordered Scaffolds: Complexity and Algorithms
Date: 2019-11-25 00:00:00

Despite the recent progress in genome sequencing and assembly, many of the
currently available assembled genomes come in a draft form. Such draft genomes
consist of a large number of genomic fragments (scaffolds), whose order and/or
orientation (i.e., strand) in the genome are unknown. There exist various
scaffold assembly methods, which attempt to determine the order and orientation
of scaffolds along the genome chromosomes. Some of these methods (e.g., based
on FISH physical mapping, chromatin conformation capture, etc.) can infer the
order of scaffolds, but not necessarily their orientation. This leads to a
special case of the scaffold orientation problem (i.e., deducing the
orientation of each scaffold) with a known order of the scaffolds.
  We address the problem of orientating ordered scaffolds as an optimization
problem based on given weighted orientations of scaffolds and their pairs
(e.g., coming from pair-end sequencing reads, long reads, or homologous
relations). We formalize this problem using notion of a scaffold graph (i.e., a
graph, where vertices correspond to the assembled contigs or scaffolds and
edges represent connections between them). We prove that this problem is
NP-hard, and present a polynomial-time algorithm for solving its special case,
where orientation of each scaffold is imposed relatively to at most two other
scaffolds. We further develop an FPT algorithm for the general case of the OOS
problem.
Income growth with high inequality implies loss of well-being: A mathematical model
Date: 2019-11-25 00:00:00

A mathematical model of measurement of the perception of well-being for
groups with increasing incomes, but proportionally unequal is proposed.
Assuming that welfare grows with own income and decreases with relative
inequality (income of the other concerning one's own), possible scenarios for
long-term behavior in welfare functions are concluded. Also, it is proved that
a high relative inequality (parametric definition) always implies the loss of
the self-perception of the well-being of the most disadvantaged group.
A Binary Particle Swarm Optimization Approach for Gene Expression Biclustering Problem
Date: 2019-11-25 00:00:00

Microarray techniques are widely used in Gene expression analysis. These
techniques are based on discovering submatrices of genes that share similar
expression patterns across a set of experimental conditions with coherence
constraint. Actually, these submatrices are called biclusters and the
extraction process is called biclustering. In this paper we present a novel
binary particle swarm optimization model for the gene expression biclustering
problem. Hence, we apply the binary particle swarm optimization algorithm with
a proposed measure, called Discretized Column-based Measure (DCM) as a novel
cost function for evaluating biclusters where biological relevance, MSR and the
size of the bicluster are considered as evaluation metrics for our results.
Results are compared to the existing algorithms and they show the validity of
our proposed approach.
Development of On Board Computer for a Nanosatellite
Date: 2020-03-17 00:00:00
Tasks: Fault Detection, Image Compression

This paper describes the process of conceptualization, design, and testing of
the Onboard Computer (OBC) Software for a 3U nanosatellite. The on-board
computer of the satellite is responsible for initiating dataflow between
onboard hardware, performing image compression, and run control algorithms like
fine pointing, sun pointing, ground pointing for payload operation and idle
state detumbling. The actuation is carried out by interfacing magnetorquers and
reaction wheels with the OBC. The software of the onboard computer is
implemented on a Linux based operating system run on the ARM Cortex A9
processor which is part of the Zynq-7000 SoC. A Field Programmable Gate Array
(FPGA) is used specifically for image compression. The compressed image is
stored in a serial flash memory shared between the camera and the FPGA. The
architecture comprises of a system-wide I2C bus to which the sensors
interfaced. The collected data is used for logging followed by downlink and as
input to algorithms used for pointing and detumbling. An SPI interface is used
between the Power Subsystem microcontroller and the On-Board Computer since a
large amount of housekeeping data will have to be exchanged at high rates.
Reaction wheels and magnetorquers are actuated by current driver circuits which
get the control signals from the OBC. The satellite is modelled as a Finite
State Machine for software development. The states broadly fall under two
categories, Normal and Emergency. Each state has a predetermined set of logical
tasks to be run, which are abstracted as separate processes in the memory.
State transitions take place by polling the health metrics of the satellite.
However, hardware interrupts are implemented on selected peripherals which
ensure an asynchronous switching to the Emergency States for safety. A review
of some common fault detection, isolation and removal methods used shall
conclude the paper.
A new set of cluster driven composite development indicators
Date: 2020-03-26 00:00:00
Tasks: Clustering, Dimensionality Reduction

Composite development indicators used in policy making often subjectively
aggregate a restricted set of indicators. We show, using dimensionality
reduction techniques, including Principal Component Analysis (PCA) and for the
first time information filtering and hierarchical clustering, that these
composite indicators miss key information on the relationship between different
indicators. In particular, the grouping of indicators via topics is not
reflected in the data at a global and local level. We overcome these issues by
using the clustering of indicators to build a new set of cluster driven
composite development indicators that are objective, data driven, comparable
between countries, and retain interpretabilty. We discuss their consequences on
informing policy makers about country development, comparing them with the top
PageRank indicators as a benchmark. Finally, we demonstrate that our new set of
composite development indicators outperforms the benchmark on a dataset
reconstruction task.
Internet of things-based (IoT) inventory monitoring refrigerator using arduino sensor network
Date: 2019-11-25 00:00:00

This study presents a system that combines a conventional refrigerator,
microcontrollers and a smart phone to create an inventory monitoring that can
monitor the stocks inside the refrigerator wirelessly by accessing an Android
application. The developed refrigerator uses a sensor network system that is
installed in a respective compartment inside the refrigerator. Each sensor will
transmit data to the microcontrollers, such as Arduino Yun and Arduino Uno,
which are interconnected by the I2C communications. All data and images will be
processed to provide the user an Internet of Things application through the
cloud-based website Temboo. Temboo will have access to send data to the
Dropbox. A smartphone is connected to the Dropbox where all the data and images
are stored. The user can monitor the stocks or contents of the refrigerator
wirelessly using an Android Application.
State-Space Based Network Topology Identification
Date: 2019-11-25 00:00:00

In this work, we explore the state-space formulation of network processes to
recover the underlying structure of the network (local connections). To do so,
we employ subspace techniques borrowed from system identification literature
and extend them to the network topology inference problem. This approach
provides a unified view of the traditional network control theory and signal
processing on networks. In addition, it provides theoretical guarantees for the
recovery of the topological structure of a deterministic linear dynamical
system from input-output observations even though the input and state evolution
networks can be different.
Controller Synthesis of Wind Turbine Generator and Energy Storage System with Stochastic Wind Variations under Temporal Logic Specifications
Date: 2019-11-26 00:00:00

In this paper, we present a controller synthesis approach for wind turbine
generators (WTG) and energy storage systems with metric temporal logic (MTL)
specifications, with provable probabilistic guarantees in the stochastic
environment of wind power generation. The MTL specifications are requirements
for the grid frequency deviations, WTG rotor speed variations and the power
flow constraints at different lines. We present the stochastic control
bisimulation function, which bounds the divergence of the trajectories of a
switched stochastic control system and the switched nominal control system in a
probabilistic fashion.We first design a feedforward controller by solving an
optimization problem for the nominal trajectory of the deterministic control
system with robustness against initial state variations and stochastic
uncertainties. Then we generate a feedback control law from the data of the
simulated trajectories. We implement our control method on both a four-bus
system and a nine-bus system, and test the effectiveness of the method with a
generation loss disturbance. We also test the advantage of the feedback
controller over the feedforward controller when unexpected disturbance occurs.
Neural network for pricing and universal static hedging of contingent claims
Date: 2019-11-26 00:00:00

We present here a regress later based Monte Carlo approach that uses neural
networks for pricing high-dimensional contingent claims. The choice of specific
architecture of the neural networks used in the proposed algorithm provides for
interpretability of the model, a feature that is often desirable in the
financial context. Specifically, the interpretation leads us to demonstrate
that any contingent claim -- possibly high dimensional and path-dependent --
under the Markovian and the no-arbitrage assumptions, can be semi-statically
hedged using a portfolio of short maturity options. We show how the method can
be used to obtain an upper and lower bound to the true price, where the lower
bound is obtained by following a sub-optimal policy, while the upper bound by
exploiting the dual formulation. Unlike other duality based upper bounds where
one typically has to resort to nested simulation for constructing
super-martingales, the martingales in the current approach come at no extra
cost, without the need for any sub-simulations. We demonstrate through
numerical examples the simplicity and efficiency of the method for both pricing
and semi-static hedging of path-dependent options
Minimal Driver Nodes for Structural Controllability of Large-Scale Dynamical Systems: Node Classification
Date: 2019-11-26 00:00:00
Tasks: Node Classification

This paper considers the problem of minimal control inputs to affect the
system states such that the resulting system is structurally controllable. This
problem and the dual problem of minimal observability are claimed to have no
polynomial-order exact solution and, therefore, are NP-hard. Here, adopting a
graph-theoretic approach, this problem is solved for general nonlinear (and
also structure-invariant) systems and a P-order solution is proposed. In this
direction, the dynamical system is modeled as a directed graph, called
\textit{system digraph}, and two types of graph components are introduced which
are tightly related with structural controllability. Two types of nodes which
are required to be affected (or driven) by an input, called \textit{driver
nodes}, are defined, and minimal number of these driver nodes are obtained.
Polynomial-order complexity of the given algorithms to solve the problem
ensures applicability of the solution for analysis of large-scale dynamical
systems. {The structural results in this paper are significant as compared to
the existing literature which offer approximate and computationally
less-efficient, e.g. Gramian-based, solutions for the problem, while this paper
provides exact solution with lower computational complexity and applicable for
controllability analysis of nonlinear systems.
Distributed Optimal Frequency Control for Integrated Energy Systems with Electricity and Heat
Date: 2019-11-26 00:00:00

With more and more distributed energy resources (DERs) deployed in Integrated
Energy Systems (IESs), frequency stability challenges the pursuit of
reliability and efficiency. This paper proposes a fully-distributed frequency
control method for load-side DERs, in which the optimality can be guaranteed in
an IES where electricity and heat are coupled. Moreover, the global asymptotic
stability of the closed-loop system is proved and the robustness with respect
to inaccurate coefficients is shown. Case studies demonstrate the effectiveness
of proposed method.
COOK: Chirp-OOK Communication with Self-reliant Bitrate Adaptation in Backscatter Networks
Date: 2019-11-26 00:00:00
Tasks: valid

For large-scale Internet of Things (IoT), backscatter communication is a
promising technology to reduce power consumption and simplify deployment.
However, backscatter communication lacks stability, along with limited
communication range within a few meters. Due to the limited computation ability
of backscatter tags, it is burdensome to effectively adapt the bitrate for the
time-varying channel. Thus, backscatter tags are failed to fully utilize the
optimal transmission rate. In this paper, we design a system named COOK with
self-reliant bitrate adaptation in backscatter communication. Channel symmetry
allows backscatter tags to adjust bitrate depending on the received signal
strength of the excitation source (ES) without feedback. In addition, the chirp
spreading signal is exploited as the ES signal to enable backscatter tags to
work under noise floor. Our modulation approach is denoted as Chirp-OOK since
the tags reflect the chirp signal by employing the on-off keying modulation. It
allows that receiver can decode under the noise floor and the bitrate varies
flexibly as the communication range changes. We have implemented the prototype
system based on the universal software radio peripheral (USRP) platform.
Extensive experiment results demonstrate the effectiveness of the proposed
system. Our system provides valid communication distance up to $27m$, which is
7 times as compared with normal backscatter system. The system significantly
increases the backscatter communication stability, by supporting bitrate
adaptation ranges from 0.33kbps to 1.2Mbps, and guaranteeing the bit error rate
(BER) is below 1%.
Adaptive Frequency-limited H2-Model Order Reduction
Authors: Umair Zulfiqar, Victor Sreeram, Xin Du
Date: 2019-11-26 00:00:00
Tasks: model

In this paper, we present an adaptive framework for constructing a pseudo-optimal reduced model for the frequency-limited H2-optimal model order reduction problem. We show that the frequency-limited pseudo-optimal reduced-order model has an inherent property of monotonic decay in error if the interpolation points and tangential directions are selected appropriately. We also show that this property can be used to make an automatic selection of the order of the reduced model for an allowable tolerance in error. The proposed algorithm adaptively increases the order of the reduced model such that the frequency-limited H2-norm error decays monotonically irrespective of the choice of interpolation points and tangential directions. The stability of the reduced-order model is also guaranteed. Additionally, it also generates the approximations of the frequency-limited system Gramians that monotonically approach the original solution. Further, we show that the low-rank alternating direction implicit iteration method for solving large-scale frequency-limited Lyapunov equations implicitly performs frequency-limited pseudo-optimal model order reduction. We consider two numerical examples to validate the theory presented in the paper.
Closed Quantum Black-Scholes: Quantum Drift and the Heisenberg Equation of Motion
Date: 2020-01-24 00:00:00

In this article we model a financial derivative price as an observable on the
market state function. We apply geometric techniques to integrating the
Heisenberg Equation of Motion. We illustrate how the non-commutative nature of
the model introduces quantum interference effects that can act as either a drag
or a boost on the resulting return. The ultimate objective is to investigate
the nature of quantum drift in the Accardi-Boukas quantum Black-Scholes
framework which involves modelling the financial market as a quantum
observable, and introduces randomness through the Hudson-Parthasarathy quantum
stochastic calculus. In particular we aim to differentiate randomness that is
introduced through external noise (quantum stochastic calculus) and randomness
that is fundamental to a quantum system (Heisenberg Equation of Motion).
Capacity Approaching Low Density Spreading in Uplink NOMA via Asymptotic Analysis
Authors: Hossein Asgharimoghaddam, Jarkko Kaleva, Antti Tölli
Date: 2019-11-26 00:00:00

Low-density spreading non-orthogonal multiple-access (LDS-NOMA) is considered where $K$ single-antenna user-equipments (UEs) communicate with a base-station (BS) over $F$ fading sub-carriers. Each UE $k$ spreads its data symbols over $d_k<F$ sub-carriers. We aim to identify the LDS-code allocations that maximize the ergodic mutual information (EMI). The BS assigns resources solely based on pathlosses. Conducting analysis in the regime where $F$, $K$, and ${d_k,\forall k}$ converge to $+\infty$ at the same rate, we present EMI as a deterministic equivalent plus a residual term. The deterministic equivalent is a function of pathlosses and spreading codes, and the small residual term scales as $\mathcal{O}(\frac{1}{\min(d_k^2)})$. We formulate an optimization problem to get the set of all spreading codes, irrespective of sparsity constraints, which maximize the deterministic EMI. This yields a simple resource allocation rule that facilitates the construction of desired LDS-codes via an efficient partitioning algorithm. The acquired LDS-codes additionally harness the small incremental gain inherent in the residual term, and thus, attain near-optimal values of EMI in the finite regime. While regular LDS-NOMA is found to be asymptotically optimal in symmetric models, an irregular spreading arises in generic asymmetric cases. The spectral efficiency enhancement relative to regular and random spreading is validated numerically.
Direct and indirect transactions and requirements
Authors: Husna Betul Coskun, Huseyin Coskun
Date: 2019-11-23 00:00:00
Tasks: Novel Concepts

The indirect transactions between sectors of an economic system has been a long-standing open problem. There have been numerous attempts to conceptually define and mathematically formulate this notion in various other scientific fields in literature as well. The existing direct and indirect effects formulations, however, can neither determine the direct and indirect transactions separately nor quantify these transactions between two individual sectors of interest in a multisectoral economic system. The novel concepts of the direct, indirect and transfer (total) transactions between any two sectors are introduced, and the corresponding requirements matrices and coefficients are systematically formulated relative to both final demands and gross outputs based on the system decomposition theory in the present manuscript. It is demonstrated theoretically and through illustrative examples that the proposed requirements matrices accurately define and correctly quantify the corresponding direct, indirect, and total interactions and relationships. The proposed requirements matrices for the US economy using aggregated input-output tables for multiple years are then presented and briefly analyzed.
On electron imaging of small molecules, Part II: tomographic reconstruction from defocus series
Date: 2019-11-28 00:00:00

A practical method utilising three-dimensional image pattern matching is
proposed which, in principle, is capable of unambiguous determination of the
types and positions of atoms in small molecules from defocus series collected
at only a few different angular orientations of the molecule. Numerical tests
of the method are presented using multislice calculations of defocus series of
small biological molecules. The proposed technique can in future gain from
Bayesian or machine-learning approaches and is likely to be useful in cryogenic
electron microscopy.
On electron imaging of small molecules, Part I: relative roles of multiple scattering and Fresnel diffraction
Date: 2019-11-28 00:00:00

The relative roles of multiple electron scattering and in-molecule free-space
propagation in transmission electron microscopy of small molecules are
discussed. It is argued that while multiple scattering tends to have only a
moderate effect in this case, the in-molecule Fresnel diffraction is likely to
be significant due to the shallow depth of focus under the relevant
experimental conditions. As a consequence, diffraction tomography based on the
first Born or first Rytov approximation represents a more suitable method for
the reconstruction of three-dimensional distribution of the electrostatic
potential in this context, compared to conventional computed tomography which
is intrinsically based on the projection approximation. A simplified method for
diffraction tomography is proposed and tested on numerically simulated
examples.
Mapping and Monitoring Pollution Levels of Carbon Monoxide (CO) using Arduino and Location-Based Service
Date: 2019-11-11 00:00:00

Air pollution is a serious issue in the world. Around 98% of cities with a
population of over 100,000 people in low and middle-income countries do not
meet air quality standards, while in high-income countries, the number has
decreased by 52%. Industries and motor vehicles are the biggest contributors to
carbon monoxide (CO).Air pollution is a severe issue in the world. The object
of research to investigate is the detection of carbon monoxide (CO) levels in
an area mapped based on the current CO levels along with the location
coordinates via GPS and LBS technology with a microcontroller and sensor-based
device.The results of testing the CO level detection devices found that high CO
levels are in the afternoon with an average CO level of 49.59656 which means
classified as dangerous if we are outdoors more than 30 minutes, can interfere
with heart function.