pwc-archive/papers-with-abstracts huggingface dataset:
Neural-inspired Measurement Observability
Authors: Burak Boyacıoğlu, Alice C. Schwarze, Bingni W. Brunton, Kristi A. Morgansen
Date: 2022-06-06 00:00:00

The neural encoding by biological sensors of flying insects, which prefilters stimulus data before sending it to the central nervous system in the form of voltage spikes, enables sensing capabilities that are computationally low-cost while also being highly robust to noise. This process, which can be modeled as the composition of a linear moving average filter and a nonlinear decision function, inspired the work reported here to improve engineered sensing performance by maximizing the observability of particular neural-inspired composite measurement functions. We first present a tool to determine the observability of a linear system with measurement delay (the first element of the composition), then use a Lie algebraic observability approach to study nonlinear autonomous systems with output delay (the second element of the composition). The Lie algebraic tools are then extended to address overall observability of systems with composite outputs as in the neural encoder model we adopt. The analytical outcomes are supported using the empirical observability Gramian, and optimal sensor placement on a bioinspired wing model is performed using metrics based on the empirical Gramian.
Integral Line-of-Sight Curved Path Following of Helical Microswimmers Actuated by Rotating Magnetic Dipoles
Authors: Alireza Mohammadi, Mark W. Spong
Date: 2022-06-05 00:00:00

This short paper investigates the problem of curved path following for helical microswimmers actuated by rotating magnetic dipoles. The proposed solution, which relies on an integral line-of-sight (ILOS) guidance law, can be utilized in both below and beyond step-out frequency regimes.
Conditions for Oscillator Small-Signal Amplitude-Phase Orthogonality
Authors: Torsten Djurhuus, Viktor Krozer
Date: 2022-06-05 00:00:00

The paper explores a previously unknown connection relating the symmetry properties of an oscillator steady-state to the orthogonal representation of amplitude and phase variables in the small-signal regime. It is shown that only circuits producing perfectly symmetric steady-states can produce an orthogonal Floquet decomposition. Considering room temperature operation this scenario implies zero AM-PM noise conversion. This surprising and novel result follows directly from the predictions of a rigorous model framework first described herein. The work presented in this text extend the current state-of-the-art w.r.t. oscillator small-signal/noise characterization.
Compositional Construction of Control Barrier Functions for Continuous-Time Stochastic Hybrid Systems
Authors: Ameneh Nejati, Sadegh Soudjani, Majid Zamani
Date: 2020-12-14 00:00:00

In this work, we propose a compositional framework for the construction of control barrier functions for networks of continuous-time stochastic hybrid systems enforcing complex logic specifications expressed by finite-state automata. The proposed scheme is based on a notion of so-called pseudo-barrier functions computed for subsystems, by employing which one can synthesize hybrid controllers for interconnected systems enforcing complex specifications over a finite-time horizon. Particularly, we first leverage sufficient small-gain type conditions to compositionally construct control barrier functions for interconnected systems based on the corresponding pseudo-barrier functions computed for subsystems. Then, using the constructed control barrier functions, we provide probabilistic guarantees on the satisfaction of given complex specifications in a bounded time horizon. In this respect, we decompose the given complex specification to simpler reachability tasks based on automata representing the complements of original finite-state automata. We then provide systematic approaches to solve those simpler reachability tasks by computing corresponding pseudo-barrier functions. Two different systematic techniques are provided based on (i) the sum-of-squares (SOS) optimization program and (ii) counter-example guided inductive synthesis (CEGIS) to search for pseudo-barrier functions of subsystems while synthesizing local controllers. We demonstrate the effectiveness of our proposed results by applying them to a fully-interconnected Kuramoto network of 100 nonlinear oscillators with Markovian switching signals.
LASSO-Based Multiple-Line Outage Identification In Partially Observable Power Systems
Authors: Xiaozhou Yang, Nan Chen
Date: 2022-06-05 00:00:00

Phasor measurement units (PMUs) create ample real-time monitoring opportunities for modern power systems. Among them, line outage detection and identification remains a crucial but challenging task. Current works on outage identification succeed in full PMU deployment and single-line outages. Performance however degrades for multiple-line outage with partial system observability. We propose a novel framework of multiple-line outage identification using partial nodal voltage measurements. Using alternating current (AC) power flow model, phase angle signatures of outages are extracted and used to group lines into minimal diagnosable clusters. Identification is then formulated into an underdetermined sparse regression problem solved by lasso. Tested on IEEE 39-bus system with 25% and 50% PMU coverage, the proposed identification method is 93% and 80% accurate for single- and double-line outages. Our study suggests that the AC power flow is better at capturing outage patterns and sacrificing some precision could yield substantial improvement in identification accuracy. These findings could contribute to the development of future control schemes that help power systems resist and recover from outage disruptions in real time.
On the exponential convergence of input-output signals of nonlinear feedback systems
Authors: Lanlan Su, Di Zhao, Sei Zhen Khong
Date: 2022-06-04 00:00:00

This note studies the exponential convergence of input-output signals of discrete-time nonlinear systems composed of a feedback interconnection of a linear time-invariant system and a nonlinear uncertainty. Both the open-loop subsystems are allowed to be unbounded. Integral-quadratic-constraint-based conditions are proposed for these uncertain feedback systems, including the Lurye type, to exhibit the property that the endogenous input-output signals enjoy an exponential convergence rate for all initial conditions of the linear time-invariant subsystem. The conditions are established via a combination of tools, including integral quadratic constraints, directed gap, and exponential weightings.
DOBC-Based Frequency & Voltage Regulation Strategy for PV-Diesel Hybrid Microgrid During Islanding Conditions
Authors: Himanshu Grover, Ashu Verma, T. S. Bhatti
Date: 2022-06-04 00:00:00

This paper proposes a disturbance observer-based control (DOBC) method for frequency and voltage regulation of a solar photovoltaic (PV)-diesel generator(DG) based hybrid microgrid during islanding conditions. The DOBC is integrated as a feed-forward control to the synchronous generator based DG, which handles real-time power mismatches and regulates the microgrid frequency and voltage under islanding. To substantiate the operational robustness of the developed controller under real-time uncertainties arising due to variability in PV output and load, the controller has been tested under worstcase uncertainty conditions. The proposed controller has been developed as a MATLAB/Simulink model and the results are validated on the real-time simulator OPAL-RT. The effectiveness of the proposed control scheme has further been validated in the presence of communication delays and noisy load conditions. Results verify the dynamic performance of the controller in regulating the system frequency and voltage for low-inertia microgrids. Finally, the proposed control strategy has been implemented on laboratory scale microgrid setup in which synchronous generator based diesel generator regulates system frequency fast and efficiently under worst case uncertainty scenario.
Markovian Decentralized Ensemble Control for Demand Response
Authors: Guanze Peng, Robert Mieth, Deepjyoti Deka, Yury Dvorkin
Date: 2022-06-04 00:00:00

With the advancement in smart grid and smart energy devices, demand response becomes one of the most economic and feasible solutions to ease the load stress of the power grids during peak hours. In this work, we propose a fully decentralized ensemble control framework with consensus for demand response (DR) events and compatible control methods based on random policies. We show that under the consensus that is tailored to DR, our proposed decentralized control method yields the same optimality as the centralized control method in both myopic and multistage settings.
Robust and Kernelized Data-Enabled Predictive Control for Nonlinear Systems
Authors: Linbin Huang, John Lygeros, Florian Dörfler
Date: 2022-06-04 00:00:00

This paper presents a robust and kernelized data-enabled predictive control (RoKDeePC) algorithm to perform model-free optimal control for nonlinear systems using only input and output data. The algorithm combines robust predictive control and a non-parametric representation of nonlinear systems enabled by regularized kernel methods. The latter is based on implicitly learning the nonlinear behavior of the system via the representer theorem. Instead of seeking a model and then performing control design, our method goes directly from data to control. This allows us to robustify the control inputs against the uncertainties in data by considering a min-max optimization problem to calculate the optimal control sequence. We show that by incorporating a proper uncertainty set, this min-max problem can be reformulated as a nonconvex but structured minimization problem. By exploiting its structure, we present a projected gradient descent algorithm to effectively solve this problem. Finally, we test the RoKDeePC on two nonlinear example systems - one academic case study and a grid-forming converter feeding a nonlinear load - and compare it with some existing nonlinear data-driven predictive control methods.
Complete Performance Analysis of Underwater VLC Diffusion Adaptive Networks
Authors: Hossein Abdavinejad, Hadi Baghali, Javad Ostadieh, Ehsan Mostafapour, Changiz Ghobadi, Javad Nourinia
Date: 2022-06-01 00:00:00

In this paper, we simulated a diffusion adaptive network in the underwater environment. The communication method between the nodes of this network is assumed to be the visible light communication technology (VLC) which in the underwater condition is known as the UVLC. The links between the nodes in this case are contaminated with the optical noise and turbulence. These contaminations are modeled with the proper statistical distributions depending on the underwater conditions. The optical turbulence modeling link coefficients are shown to be following the Log-normal distribution which its mean and variance are directly dependent on the temperature and the salinity of the simulated water and the assumed distance between the diffusion network nodes. The performance of the diffusion network in using UVLC technology is then analyzed both with simulations and theoretical calculations and the results are presented using the steady-state error metrics. Our analysis showed that the diffusion network can be implemented underwater with the VLC technology providing that the distance between the network nodes is less than 10 meters. Also, in order to guarantee the convergence of the adaptive network, the water salinity level and temperature must not exceed the values that are presented in our simulations.
Operative and Procedural Cooperative Training in Marine Ports
Authors: Francesco Longo, Alessandro Chiurco, Roberto Musmanno, Letizia Nicoletti
Date: 2022-05-30 00:00:00

This article faces the problem of operative and procedural cooperative training in marine ports with particular attention to harbour pilots and port traffic controller. The design and development of an advanced system, equipped with dedicated hardware in the loop, for cooperative training of operators involved in the last mile of navigation is presented. Indeed, the article describes the software and hardware development of a distributed and interoperable system composed by two simulators (the bridge ship simulator and control tower simulator). Multiple problems are faced and solved including (i) the motion of the ship at sea that is based on a 6 Degree Of Freedom (DOF) model for surge, sway and yaw and closed form expressions for pitch, roll and heave and its validation; (ii) the development of the 3D geometric models and related virtual environments of a real marine port and vessel (to provide the trainees with the sensation to experience a real port and ship environment); (iii) the design of a bridge ship replica, the bridge hardware integration and the design of the visualization system; (iv) the design and development of the control tower simulator; (v) the integration of the bridge ship simulator and control tower simulator through the IEEE 1516 High Level Architecture standard for distributed simulation.
Scan4CFU: Low-cost, open-source bacterial colony tracking over large areas and extended incubation times
Authors: Santosh Pandey, Yunsoo Park, Ankita Ankita, Gregory J. Phillips
Date: 2022-05-25 00:00:00

A hallmark of bacterial populations cultured in vitro is their homogeneity of growth, where the majority of cells display identical growth rate, cell size and content. Recent insights, however, have revealed that even cells growing in exponential growth phase can be heterogeneous with respect to variables typically used to measure cell growth. Bacterial heterogeneity has important implications for how bacteria respond to environmental stresses, such as antibiotics. The phenomenon of antimicrobial persistence, for example, has been linked to a small subpopulation of cells that have entered into a state of dormancy where antibiotics are no longer effective. While methods have been developed for identifying individual non-growing cells in bacterial cultures, there has been less attention paid to how these cells may influence growth in colonies on a solid surface. In response, we have developed a low-cost, open-source platform to perform automated image capture and image analysis of bacterial colony growth on multiple nutrient agar plates simultaneously. The descriptions of the hardware and software are included, along with details about the temperature-controlled growth chamber, high-resolution scanner, and graphical interface to extract and plot the colony lag time and growth kinetics. Experiments were conducted using a wild type strain of Escherichia coli K12 to demonstrate the feasibility and operation of our setup. By automated tracking of bacterial growth kinetics in colonies, the system holds the potential to reveal new insights into understanding the impact of microbial heterogeneity on antibiotic resistance and persistence.
Establishing the Capabilities of the Murchison Widefield Array as a Passive Radar for the Surveillance of Space
Authors: Brendan Hennessy, Robert Young, Steven Tingay, Ashley Summers, Daniel Gustainis, Brian Crosse, Marcin Sokolowski
Date: 2022-06-06 00:00:00

This paper describes the use of the Murchison Widefield Array, a low-frequency radio telescope at a radio-quiet Western Australian site, as a radar receiver forming part of a continent-spanning multistatic radar network for the surveillance of space. This paper details the system geometry employed, the orbit-specific radar signal processing, and the orbit determination algorithms necessary to ensure resident space objects are detected, tracked, and propagated. Finally, the paper includes the results processed after a short collection campaign utilising several FM radio transmitters across the country, up to a maximum baseline distance of over 2500 km. The results demonstrate the Murchison Widefield Array is able to provide widefield and persistent coverage of objects in low Earth orbit.
Noise-induced transitions in gene circuits: a perturbative approach for slow noise
Authors: Gerardo Aquino, Andrea Rocco
Date: 2022-06-04 00:00:00

We consider a generic class of gene circuits affected by nonlinear extrinsic noise. To address this nonlinearity we introduce a general perturbative methodology based on assuming timescale separation between noise and genes dynamics, with fluctuations exhibiting a large but finite correlation time. We apply this methodology to the case of the toggle switch, and by considering biologically relevant log-normal fluctuations, we find that the system exhibits noise-induced transitions. The system becomes bimodal in regions of the parameter space where it would be deterministically monostable. We show that by including higher order corrections our methodology allows one to obtain correct predictions for the occurrence of transitions even for not so large correlation time of the fluctuations, overcoming thereby limitations of previous theoretical approaches. Interestingly we find that at intermediate noise intensities the noise-induced transition in the toggle switch affects one of the genes involved, but not the other one.
Torsional Diffusion for Molecular Conformer Generation
Authors: Bowen Jing, Gabriele Corso, Jeffrey Chang, Regina Barzilay, Tommi Jaakkola
Date: 2022-06-01 00:00:00
Tasks: BIG-bench Machine Learning, Computational chemistry

Molecular conformer generation is a fundamental task in computational chemistry. Several machine learning approaches have been developed, but none have outperformed state-of-the-art cheminformatics methods. We propose torsional diffusion, a novel diffusion framework that operates on the space of torsion angles via a diffusion process on the hypertorus and an extrinsic-to-intrinsic score model. On a standard benchmark of drug-like molecules, torsional diffusion generates superior conformer ensembles compared to machine learning and cheminformatics methods in terms of both RMSD and chemical properties, and is orders of magnitude faster than previous diffusion-based models. Moreover, our model provides exact likelihoods, which we employ to build the first generalizable Boltzmann generator. Code is available at https://github.com/gcorso/torsional-diffusion.
Towards retrieving dispersion profiles using quantum-mimic Optical Coherence Tomography and Machine Learnin
Authors: Krzysztof A. Maliszewski, Piotr Kolenderski, Varvara Vetrova, Sylwia M. Kolenderska
Date: 2022-05-30 00:00:00
Tasks: Retrieval

Artefacts in quantum-mimic Optical Coherence Tomography are considered detrimental because they scramble the images even for the simplest objects. They are a side effect of autocorrelation which is used in the quantum entanglement mimicking algorithm behind this method. Interestingly, the autocorrelation imprints certain characteristics onto an artefact - it makes its shape and characteristics depend on the amount of dispersion exhibited by the layer that artefact corresponds to. This unique relationship between the artefact and the layer's dispersion can be used to determine Group Velocity Dispersion (GVD) values of object layers and, based on them, build a dispersion-contrasted depth profile. The retrieval of GVD profiles is achieved via Machine Learning. During training, a neural network learns the relationship between GVD and the artefacts' shape and characteristics, and consequently, it is able to provide a good qualitative representation of object's dispersion profile for never-seen-before data: computer-generated single dispersive layers and experimental pieces of glass.
Spread of SARS-CoV-2 in a SIS model with vaccination and breakthrough infection
Authors: Ariel Félix Gualtieri, Carolina de la Cal, Augusto Francisco Toma, Pedro Hecht
Date: 2022-06-03 00:00:00

Although previous infection and vaccination provide protection against SARS-CoV-2 infection, both reinfection and breakthrough infection are possible events whose occurrence would increase with time after first exposure to the antigen and with the emergence of new variants of the virus. Periodic vaccination could counteract this decline in protection. In the present work, our aim was to develop and explore a model of SARS-CoV-2 spread with vaccination, reinfection and breakthrough infection. A modified deterministic SIS (Susceptible-Infected-Susceptible) model represented by a system of differential equations was designed. As in any SIS model, the population was divided into susceptible and infected individuals. But in our design, susceptible individuals were, in turn, grouped into three consecutive categories whose susceptibility increases with time after infection or vaccination. The model was studied by means of computer simulations, which were analysed qualitatively. The results obtained show that the prevalence, after oscillating between peaks and valleys, reaches a plateau phase. Moreover, as might be expected, the magnitude of the peaks and plateaus increases as the infection rate rises, the vaccination rate decreases and the rate of decay of protection conferred by vaccination or previous infection increases. Therefore, the present study suggests that, at least under certain conditions, the spread of SARS-CoV-2, although it could experience fluctuations, would finally evolve into an endemic form, with a more or less stable prevalence that would depend on the levels of infection and vaccination, and on the kinetics of post-infection and post-vaccination protection. However, it should be kept in mind that our development is a theoretical scheme with many limitations. For this reason, its predictions should be considered with great care.
The impact of spatio-temporal travel distance on epidemics using an interpretable attention-based sequence-to-sequence model
Authors: Yukang Jiang, Ting Tian, Huajun Xie, Hailiang Guo, Xueqin Wang
Date: 2022-05-26 00:00:00

Amidst the COVID-19 pandemic, travel restrictions have emerged as crucial interventions for mitigating the spread of the virus. In this study, we enhance the predictive capabilities of our model, Sequence-to-Sequence Epidemic Attention Network (S2SEA-Net), by incorporating an attention module, allowing us to assess the impact of distinct classes of travel distances on epidemic dynamics. Furthermore, our model provides forecasts for new confirmed cases and deaths. To achieve this, we leverage daily data on population movement across various travel distance categories, coupled with county-level epidemic data in the United States. Our findings illuminate a compelling relationship between the volume of travelers at different distance ranges and the trajectories of COVID-19. Notably, a discernible spatial pattern emerges with respect to these travel distance categories on a national scale. We unveil the geographical variations in the influence of population movement at different travel distances on the dynamics of epidemic spread. This will contribute to the formulation of strategies for future epidemic prevention and public health policies.