pwc-archive/papers-with-abstracts huggingface dataset:
Secondary frequency control stabilizing voltage dynamics
Authors: Eder Batista Tchawou Tchuisseu, Eric-Donald Ndogmo, Pavel Procházka, Paul Woafo, Pere Colet, Benjamin Schäfer
Date: 2021-09-06 00:00:00

The ongoing energy transition challenges the stability of the electrical power system. Stable operation of the electrical power grid requires both the voltage (amplitude) and the frequency to stay within operational bounds. While much research has focused on frequency dynamics and stability, the voltage dynamics has been neglected. Here, we study frequency and voltage stability in the case of simple networks via linear stability and bulk analysis. In particular, our linear stability analysis of the network shows that the frequency secondary control guarantees the stability of a particular electric network. Even more interesting, while we only consider secondary frequency control, we observe a stabilizing effect on the voltage dynamics, especially in our numerical bulk analysis.
Multirotors from Takeoff to Real-Time Full Identification Using the Modified Relay Feedback Test and Deep Neural Networks
Authors: Abdulla Ayyad, Mohamad Chehadeh, Pedro Silva, Mohamad Wahbah, Oussama Abdul Hay, Igor Boiko, Yahya Zweiri
Date: 2020-10-06 00:00:00

Low cost real-time identification of multirotor unmanned aerial vehicle (UAV) dynamics is an active area of research supported by the surge in demand and emerging application domains. Such real-time identification capabilities shorten development time and cost, making UAVs' technology more accessible, and enable a wide variety of advanced applications. In this paper, we present a novel comprehensive approach, called DNN-MRFT, for real-time identification and tuning of multirotor UAVs using the Modified Relay Feedback Test (MRFT) and Deep Neural Networks (DNN). The main contribution is the development of a generalized framework for the application of DNN-MRFT to higher-order systems. One of the notable advantages of DNN-MRFT is the exact estimation of identified process gain, which mitigates the inaccuracies introduced due to the use of the describing function method in approximating the response of Lure's systems. A secondary contribution is a generalized controller based on DNN-MRFT that takes-off a UAV with unknown dynamics and identifies the inner loops dynamics in-flight. Using the developed framework, DNN-MRFT is sequentially applied to the outer translational loops of the UAV utilizing in-flight results obtained for the inner attitude loops. DNN-MRFT takes on average 15 seconds to get the full knowledge of multirotor UAV dynamics and without any further tuning or calibration the UAV would be able to pass through a vertical window, and accurately follow trajectories achieving state-of-the-art performance. Such demonstrated accuracy, speed, and robustness of identification pushes the limits of state-of-the-art in real-time identification of UAVs.
Privacy-Preserved Average Consensus Algorithms with Edge-based Additive Perturbations
Authors: Yi Xiong, Zhongkui Li
Date: 2021-09-06 00:00:00

In this paper, we consider the privacy preservation problem in both discrete- and continuous-time average consensus algorithms with strongly connected and balanced graphs, against either internal honest-but-curious agents or external eavesdroppers. A novel algorithm is proposed, which adds edge-based perturbation signals to the process of consensus computation. Our algorithm can be divided into two phases: a coordinated scrambling phase, which is for privacy preservation, and a convergence phase. In the scrambling phase, each agent is required to generate some perturbation signals and add them to the edges leading out of it. In the convergence phase, the agents update their states following a normal updating rule. It is shown that an internal honest-but-curious agent can obtain the privacy of a target agent if and only if no other agents can communicate with the target agent.
Mathematical Modeling of Guidance Trajectory with a Moving Destination Using Conditionally Markov Modeling
Authors: Reza Rezaie, X. Rong Li
Date: 2021-09-05 00:00:00
Tasks: Object, Trajectory Prediction

A trajectory of a destination-directed moving object (e.g. an aircraft from an origin airport to a destination airport) has three main components: an origin, a destination, and motion in between. We call such a trajectory that end up at the destination \textit{destination-directed trajectory (DDT)}. A class of conditionally Markov (CM) sequences (called CM$_\text{L}$) has the following main components: a joint density of two endpoints and a Markov-like evolution law. A CM$_\text{L}$ dynamic model can describe the evolution of a DDT but not of a guided object chasing a moving guide. The trajectory of a guided object is called a \textit{guided trajectory (GT)}. Inspired by a CM$_\text{L}$ model, this paper proposes a model for a GT with a moving guide. The proposed model reduces to a CM$_\text{L}$ model if the guide is not moving. We also study filtering and trajectory prediction based on the proposed model. Simulation results are presented.
Guaranteed State Estimation via Indirect Polytopic Set Computation for Nonlinear Discrete-Time Systems
Authors: Mohammad Khajenejad, Fatima Shoaib, Sze Zheng Yong
Date: 2021-09-05 00:00:00
Tasks: State Estimation

This paper proposes novel set-theoretic approaches for state estimation in bounded-error discrete-time nonlinear systems, subject to nonlinear observations/constraints. By transforming the polytopic sets that are characterized as zonotope bundles (ZB) and/or constrained zonotopes (CZ), from the state space to the space of the generators of ZB/CZ, we leverage a recent result on the remainder-form mixed-monotone decomposition functions to compute the propagated set, i.e., a ZB/CZ that is guaranteed to enclose the set of the state trajectories of the considered system. Further, by applying the remainder-form decomposition functions to the nonlinear observation function, we derive the updated set, i.e., an enclosing ZB/CZ of the intersection of the propagated set and the set of states that are compatible/consistent with the observations/constraints. Finally, we show that the mean value extension result in [1] for computing propagated sets can also be extended to compute the updated set when the observation function is nonlinear.
Risk of Cascading Failures in Time-Delayed Vehicle Platooning
Authors: Guangyi Liu, Christoforos Somarakis, Nader Motee
Date: 2021-09-05 00:00:00

We develop a systemic risk framework to explore cascading systemic failures in networked control systems. A time-delayed version of the vehicle platooning problem is used as a benchmark to study the interplay among network connectivity, system dynamics, physical limitations, and uncertainty onto the possibility of cascading failure phenomena. The measure of value-at-risk is employed to investigate the domino effect of failures among pairs of vehicles within the platoon. The systemic risk framework is suitably extended to quantify the robustness of cascading failures via a novel manipulation of bi-variate distribution. We establish closed-form risk formulas that explain the effect of network parameters (e.g., Laplacian eigen-spectrum, time delay), noise statistics, and systemic event sets onto the cascading failures. Our findings can be applied to the design of robust platoons to lower the cascading risk. We support our theoretical results with extensive simulations.
An Improved Level Set Method for Reachability Problems in Differential Games
Authors: Wei Liao, Taotao Liang, Pengwen Xiong, Chen Wang, Aiguo Song, Peter X. Liu
Date: 2021-01-24 00:00:00

This study focuses on reachability problems in differential games. An improved level set method for computing reachable tubes is proposed in this paper. The reachable tube is described as a sublevel set of a value function, which is the viscosity solution of a Hamilton-Jacobi equation with running cost. We generalize the concept of reachable tubes and propose a new class of reachable tubes, which are referred to as cost-limited one. In particular, a performance index can be specified for the system, and a cost-limited reachable tube is a set of initial states of the system's trajectories that can reach the target set before the performance index increases to a given admissible cost. Such a reachable tube can be obtained by specifying the corresponding running cost function for the Hamilton-Jacobi equation. Different non-zero sublevel sets of the viscosity solution of the Hamilton-Jacobi equation at a certain time point can be used to characterize the cost-limited reachable tubes with different admissible costs (or the reachable tubes with different time horizons), thus reducing the storage space consumption. Several examples are provided to illustrate the validity and accuracy of the proposed method.
Asymptotically Stable Observer-based Controller for Attitude Tracking with Systematic Convergence
Authors: Hashim A. Hashim
Date: 2021-09-04 00:00:00

This paper proposes a novel unit-quaternion observer-based controller for attitude tracking (attitude and angular velocity) with guaranteed transient and steady-state performance. The proposed approach is computationally cheap and can operate based on measurements provided, for instance by a typical low-cost inertial measurement unit (IMU) or magnetic, angular rate, and gravity (MARG) sensor without the knowledge of angular velocity. First, an observer evolved on $\mathbb{S}^{3}\times\mathbb{R}^{3}$ is developed guaranteeing asymptotic stability of the closed loop error signals starting from any initial condition. Afterwards, the observer is combined with the proposed controller such that the observer-based controller ensures asymptotic stability of the closed loop error signals starting from any initial condition. Simulation performed in discrete form at low sampling rate reveals the robustness and effectiveness of the proposed approach. Keywords: Observer-based controller, attitude, estimation, control, MARG, IMU, asymptotic stability.
Measurement-based Condition Monitoring of Railway Signaling Cables
Authors: Rathinamala Vijay, Gautham Prasad, Yinjia Huo, SM Sachin, TV Prabhakar
Date: 2021-09-04 00:00:00
Tasks: Fault Detection

We propose a composite diagnostics solution for railway infrastructure monitoring. In particular, we address the issue of soft-fault detection in underground railway cables. We first demonstrate the feasibility of an orthogonal multitone time domain reflectometry based fault detection and location method for railway cabling infrastructure by implementing it using software defined radios. Our practical implementation, comprehensive measurement campaign, and our measurement results guide the design of our overall composite solution. With several diagnostics solutions available in the literature, our conglomerated method presents a technique to consolidate results from multiple diagnostics methods to provide an accurate assessment of underground cable health. We present a Bayesian framework based cable health index computation technique that indicates the extent of degradation that a cable is subject to at any stage during its lifespan. We present the performance results of our proposed solution using real-world measurements to demonstrate its effectiveness.
Structural polyhedral stability of a biochemical network is equivalent to finiteness of the associated generalised Petri net
Authors: Franco Blanchini, Carlos Andrés Devia, Giulia Giordano
Date: 2021-09-03 00:00:00

We consider biochemical systems associated with a generalised class of Petri nets with possibly negative token numbers. We show that the existence of a structural polyhedral Lyapunov function for the biochemical system is equivalent to the boundedness of the associated Petri net evolution or, equivalently, to the finiteness of the number of states reachable from each initial condition. For networks that do not admit a polyhedral Lyapunov function, we investigate whether it is possible to enforce polyhedral structural stability by applying a strong negative feedback on some pinned nodes: in terms of the Petri net, this is equivalent to turning pinned nodes into black holes that clear any positive or negative incoming token. If such nodes are chosen so that the transformed Petri net has bounded discrete trajectories, then there exists a stabilising pinning control: the biochemical network becomes Lyapunov stable if a sufficiently strong local negative feedback is applied to the pinned nodes. These results allow us to structurally identify the critical nodes to be locally controlled so as to ensure the stability of the whole network.
A Markov chain approximation of switched Fokker-Planck equations for a model of on-off intermittency in the postural control during quiet standing
Authors: Yasuyuki Suzuki, Keigo Togame, Akihiro Nakamura, Taishin Nomura
Date: 2021-09-03 00:00:00

The intermittent on-off switching of feedback control is considered as a major mechanism of postural stabilization during human quiet standing, which can be modeled by switched-type hybrid stochastic delay differential equations with unstable subsystems. Dynamics of the model can also be described by the corresponding switched-type Fokker-Planck (FP) equations. Here, we develop a comprehensive numerical recipe to simulate switched-type FP equations in the case that the probability current is conserved at the switching boundary, as is the case for noise-free models exhibiting C^0-continuity for solutions at the boundary, but in a way extendable to cases with discontinuous jump. Specifically, the FP equations are approximated by a finite state Markov chain model using the finite element method. Then, dynamics of the Markov chain model, including time evolution of probability density function (PDF), stationary PDF, and power spectrum of postural sway are analyzed. We further investigate how the stationary PDF alters as values of important parameters of the model change. Dynamics of the Markov chain model are compared with Monte Carlo-based dynamics of the model, by which the developed numerical recipe is validated. The obtained Markov chain model forms a basis of our future investigations of the intermittent postural control as a Markov decision process.
On Stress Driven Diffusion in Bone -- An Experimental Study
Authors: Gustav Lindberg, Per Ståhle
Date: 2021-09-03 00:00:00

Abstract The transport of nutrients or signal constituents that stimulate growth of bone tissue is supposed to be affected by a static mechanical load. It follows from basic thermodynamical principles that constituents causing volumetric change are dragged along the gradients of hydrostatic stress. The present preliminary study examines the behaviour of iodine present in the medullary cavity of a bovine long bone exposed to mechanical load. A section of the bone is x-ray scanned with the static load present, with and without the iodine. The resulting distribution in a selected 2D plane is numerically evaluated using a discrete Radon's inverse transform. The result suggests that iodine is a useful constituent with a good attenuation effect on the x-ray beam and clearly reveals the temporal distribution of its transport through the bone. It further result shows some indication that stress does affect the iodine distribution.
Mechanisms for spontaneous symmetry breaking in developing visual cortex
Authors: Francesco Fumarola, Bettina Hein, Kenneth D. Miller
Date: 2021-09-05 00:00:00

For the brain to recognize local orientations within images, neurons must spontaneously break the translation and rotation symmetry of their response functions -- an archetypal example of unsupervised learning. The dominant framework for unsupervised learning in biology is Hebb's principle, but how Hebbian learning could break such symmetries is a longstanding biophysical riddle. Theoretical studies agree that this should require inputs to visual cortex to invert the relative magnitude of their correlations at long distances. Empirical measurements have searched in vain for such an inversion, and report the opposite to be true. We formally approach the question through the hermitianization of a multi-layer model, which maps it into a problem of zero-temperature phase transitions. In the emerging phase diagram, both symmetries break spontaneously as long as (1) recurrent interactions are sufficiently long-range and (2) Hebbian competition is duly accounted for. The relevant mechanism for symmetry breaking is competition among connections sprouting from the same afferent cell. Such competition, and not the structure of the inputs, is capable of triggering the broken-symmetry phase required by image processing. We provide analytic predictions on the relative magnitudes of the relevant length-scales needed for this novel mechanism to occur. These results reconcile experimental observations to the Hebbian paradigm, shed light on a new mechanism for visual cortex development, and contribute to our growing understanding of the relationship between learning and symmetry breaking.
Significant anisotropic neuronal refractory period plasticity
Authors: Roni Vardi, Yael Tugendhaft, Shira Sardi, Ido Kanter
Date: 2021-09-05 00:00:00

Refractory periods are an unavoidable feature of excitable elements, resulting in necessary time-lags for re-excitation. Herein, we measure neuronal absolute refractory periods (ARPs) in synaptic blocked neuronal cultures. In so doing, we show that their duration can be significantly extended by dozens of milliseconds using preceding evoked spikes generated by extracellular stimulations. The ARP increases with the frequency of preceding stimulations, and saturates at the intermittent phase of the neuronal response latency, where a short relative refractory period might appear. Nevertheless, preceding stimulations via a different extracellular route does not affect the ARP. It is also found to be independent of preceding intracellular stimulations. All these features strongly suggest that the anisotropic ARPs originate in neuronal dendrites. The results demonstrate the fast and significant plasticity of the neuronal ARP, depending on the firing activity of its connecting neurons, which is expected to affect network dynamics.
Ghost Projection
Authors: David Ceddia, David M. Paganin
Date: 2021-09-03 00:00:00

Ghost imaging is a developing imaging technique that employs random masks to image a sample. Ghost projection utilizes ghost-imaging concepts to perform the complementary procedure of projection of a desired image. The key idea underpinning ghost projection is that any desired spatial distribution of radiant exposure may be produced, up to an additive constant, by spatially-uniformly illuminating a set of random masks in succession. We explore three means of achieving ghost projection: (i) weighting each random mask, namely selecting its exposure time, according to its correlation with a desired image, (ii) selecting a subset of random masks according to their correlation with a desired image, and (iii) numerically optimizing a projection for a given set of random masks and desired image. The first two protocols are analytically tractable and conceptually transparent. The third is more efficient but less amenable to closed-form analytical expressions. A comparison with existing image-projection techniques is drawn and possible applications are discussed. These potential applications include: (i) a data projector for matter and radiation fields for which no current data projectors exist, (ii) a universal-mask approach to lithography, (iii) tomographic volumetric additive manufacturing, and (iv) a ghost-projection photocopier.
A Framework for Using Value-Added in Regressions
Authors: Antoine Deeb
Date: 2021-09-03 00:00:00

As increasingly popular metrics of worker and institutional quality, estimated value-added (VA) measures are now widely used as dependent or explanatory variables in regressions. For example, VA is used as an explanatory variable when examining the relationship between teacher VA and students' long-run outcomes. Due to the multi-step nature of VA estimation, the standard errors (SEs) researchers routinely use when including VA measures in OLS regressions are incorrect. In this paper, I show that the assumptions underpinning VA models naturally lead to a generalized method of moments (GMM) framework. Using this insight, I construct correct SEs' for regressions that use VA as an explanatory variable and for regressions where VA is the outcome. In addition, I identify the causes of incorrect SEs when using OLS, discuss the need to adjust SEs under different sets of assumptions, and propose a more efficient estimator for using VA as an explanatory variable. Finally, I illustrate my results using data from North Carolina, and show that correcting SEs results in an increase that is larger than the impact of clustering SEs.
Dynamic Games in Empirical Industrial Organization
Authors: Victor Aguirregabiria, Allan Collard-Wexler, Stephen P. Ryan
Date: 2021-09-03 00:00:00
Tasks: Econometrics

This survey is organized around three main topics: models, econometrics, and empirical applications. Section 2 presents the theoretical framework, introduces the concept of Markov Perfect Nash Equilibrium, discusses existence and multiplicity, and describes the representation of this equilibrium in terms of conditional choice probabilities. We also discuss extensions of the basic framework, including models in continuous time, the concepts of oblivious equilibrium and experience-based equilibrium, and dynamic games where firms have non-equilibrium beliefs. In section 3, we first provide an overview of the types of data used in this literature, before turning to a discussion of identification issues and results, and estimation methods. We review different methods to deal with multiple equilibria and large state spaces. We also describe recent developments for estimating games in continuous time and incorporating serially correlated unobservables, and discuss the use of machine learning methods to solving and estimating dynamic games. Section 4 discusses empirical applications of dynamic games in IO. We start describing the first empirical applications in this literature during the early 2000s. Then, we review recent applications dealing with innovation, antitrust and mergers, dynamic pricing, regulation, product repositioning, advertising, uncertainty and investment, airline network competition, dynamic matching, and natural resources. We conclude with our view of the progress made in this literature and the remaining challenges.