pwc-archive/papers-with-abstracts huggingface dataset:
Geometrical Characterization of Sensor Placement for Cone-Invariant and Multi-Agent Systems against Undetectable Zero-Dynamics Attacks
Authors: Jianqi Chen, Jieqiang Wei, Wei Chen, Henrik Sandberg, Karl H. Johansson, Jie Chen
Date: 2021-05-10 00:00:00

Undetectable attacks are an important class of malicious attacks threatening the security of cyber-physical systems, which can modify a system's state but leave the system output measurements unaffected, and hence cannot be detected from the output. This paper studies undetectable attacks on cone-invariant systems and multi-agent systems. We first provide a general characterization of zero-dynamics attacks, which characterizes fully undetectable attacks targeting the non-minimum phase zeros of a system. This geometrical characterization makes it possible to develop a defense strategy seeking to place a minimal number of sensors to detect and counter the zero-dynamics attacks on the system's actuators. The detect and defense scheme amounts to computing a set containing potentially vulnerable actuator locations and nodes, and a defense union for feasible placement of sensors based on the geometrical properties of the cones under consideration.
Passivity-based control of mechanical systems with linear damping identification
Authors: Carmen Chan-Zheng, Pablo Borja, Jacquelien Scherpen
Date: 2021-05-10 00:00:00

We propose a control approach for a class of nonlinear mechanical systems to stabilize the system under study while ensuring that the oscillations of the transient response are reduced. The approach is twofold: (i) we apply our technique for linear viscous damping identification of the system to improve the accuracy of the selected control technique, and (ii) we implement a passivity-based controller to stabilize and reduce the oscillations by selecting the control parameters properly in accordance with the identified damping. Moreover, we provide an analysis for a particular passivity-based control approach that has been shown successfully for reducing such oscillations. Also, we validate the methodology by implementing it experimentally in a planar manipulator.
Communication coordination in network controllability
Authors: Milan van den Heuvel, Jannes Nys
Date: 2021-05-10 00:00:00

Better understanding our ability to control an interconnected system of entities has been one of the central challenges in network science. The theories of node and edge controllability have been the main methodologies suggested to find the minimal set of nodes enabling control over the whole system's dynamics. While the focus is traditionally mostly on physical systems, there has been an increasing interest in control questions involving socioeconomic systems. However, surprisingly little attention has been given to the methods' underlying assumptions on control propagation, or communication assumptions, a crucial aspect in social contexts. In this paper, we show that node controllability contains a single message assumption, allowing no heterogeneity in communication to neighbouring nodes in a network. Edge controllability is shown to relax this communication assumption but aims to control the dynamics of the edge states and not the node states, thus answering a fundamentally different question. This makes comparisons of the results from the two methods nonsensical. To increase the applicability of controllability methodology to socioeconomic contexts, we provide guiding principles to choose the appropriate methodology and suggest new avenues for future theoretical work to encode more realistic communication assumptions.
Lyapunov-Krasovskii functionals for some classes of nonlinear time delay systems
Authors: Gerson Portilla, Irina V. Alexandrova, Sabine Mondié
Date: 2021-05-09 00:00:00

In this contribution, we study an homogeneous class of nonlinear time delay systems with time-varying perturbations. Using the Lyapunov-Krasovskii approach, we introduce a functional that leads to perturbation conditions matching those obtained previously in the Razumikhin framework. The functionals are applied to the estimation of the domain of attraction and of the system solutions. An illustrative example is given.
Perturbation-based Frequency Domain Linear and Nonlinear Noise Estimation
Authors: F. J. Vaquero-Caballero, D. J. Ives, S. J. Savory
Date: 2021-05-09 00:00:00
Tasks: Noise Estimation

In this paper, a new method for the separation of noise categories based on Four-Wave Mixing is presented. The theoretical analysis is grounded in the Gaussian Noise model and verified by split step simulations. The noise categories react differently to the introduced perturbations, by performing a set of perturbations the behaviour of the different categories can be separated by means of a least-square fitting. Given ASE is independent of the induced perturbations, it is possible to separate noise contributions. The analysis includes constant and variable power perturbations. The estimation of the noise categories is discussed from two points of view: NSR evolution post-DSP processing, and over the power spectral density in a notched region. The NSR estimation can only be performed at reception, whereas the power spectral density approach can be performed along the optical link if a high resolution Optical Spectrum Analyzer is available. Additionally, we perform a simple experimental verification considering of two WaveLogic 3 transceivers for the NSR, successfully estimating the noise contributions.
Non-iterative Optimization Algorithm for Active Distribution Grids Considering Uncertainty of Feeder Parameters
Authors: J. Wu, M. Liu, W. Lu, K. Xie, M. Xie
Date: 2021-05-09 00:00:00

To cope with fast-fluctuating distributed energy resources (DERs) and uncontrolled loads, this paper formulates a time-varying optimization problem for distribution grids with DERs and develops a novel non-iterative algorithm to track the optimal solutions. Different from existing methods, the proposed approach does not require iterations during the sampling interval. It only needs to perform a single one-step calculation at each interval to obtain the evolution of the optimal trajectory, which demonstrates fast calculation and online-tracking capability with an asymptotically vanishing error. Specifically, the designed approach contains two terms: a prediction term tracking the change in the optimal solution based on the time-varying nature of system power, and a correction term pushing the solution toward the optimum based on Newton's method. Moreover, the proposed algorithm can be applied in the absence of an accurate network model by leveraging voltage measurements to identify the true voltage sensitivity parameters. Simulations for an illustrative distribution network are provided to validate the approach.
Impacts of Privately Owned Electric Vehicles on Distribution System Resilience: A Multi-agent Optimization Approach
Authors: Sina Baghali, Zhaomiao Guo
Date: 2021-05-09 00:00:00
Tasks: Decision Making

We investigate the effects of private electric vehicles (EVs) on the resilience of distribution systems after disruptions. We propose a framework of network-based multi-agent optimization problems with equilibrium constraints (N-MOPEC) to consider the decentralized decision making of stakeholders in transportation and energy systems. To solve the high-dimensional non-convex problem, we develop an efficient computational algorithm based on exact convex reformulation. Numerical studies are conducted to illustrate the effectiveness of our modeling and computational approach and to draw policy insights. The proposed modeling and computational strategies could provide a solid foundation for the future study of power system resilience with private EVs in coupled transportation and power networks.
Synchronization with prescribed transient behavior: Heterogeneous multi-agent systems under funnel coupling Extended arXiv version
Authors: Jin Gyu Lee, Stephan Trenn, Hyungbo Shim
Date: 2020-12-29 00:00:00

In this paper, we introduce a nonlinear time-varying coupling law, which can be designed in a fully decentralized manner and achieves approximate synchronization with arbitrary precision, under only mild assumptions on the individual vector fields and the underlying (undirected) graph structure. The proposed coupling law is motivated by the so-called funnel control method studied in adaptive control under the observation that arbitrary precision synchronization can be achieved for heterogeneous multi-agent systems by a high-gain coupling; consequently we call our novel synchronization method `(node-wise) funnel coupling.' By adjusting the conventional proof technique in the funnel control study, we are even able to obtain asymptotic synchronization with the same funnel coupling law. Moreover, the emergent collective behavior that arises for a heterogeneous multi-agent system when enforcing arbitrary precision synchronization by the proposed funnel coupling law, is analyzed in this paper. In particular, we introduce a single scalar dynamics called `emergent dynamics' which describes the emergent synchronized behavior of the multi-agent system under funnel coupling. Characterization of the emergent dynamics is important because, for instance, one can design the emergent dynamics first such that the solution trajectory behaves as desired, and then, provide a design guideline to each agent so that the constructed vector fields yield the desired emergent dynamics. We illustrate this idea via the example of a distributed median solver based on funnel coupling.
LPVcore: MATLAB Toolbox for LPV Modelling, Identification and Control
Authors: P. den Boef, P. B. Cox, R. Tóth
Date: 2021-05-08 00:00:00
Tasks: Scheduling

This paper describes the LPVcore software package for MATLAB developed to model, simulate, estimate and control systems via linear parameter-varying (LPV) input-output (IO), state-space (SS) and linear fractional (LFR) representations. In the LPVcore toolbox, basis affine parameter-varying matrix functions are implemented to enable users to represent LPV systems in a global setting, i.e., for time-varying scheduling trajectories. This is a key difference compared to other software suites that use a grid or only LFR-based representations. The paper contains an overview of functions in the toolbox to simulate and identify IO, SS and LFR representations. Based on various prediction-error minimization methods, a comprehensive example is given on the identification of a DC motor with an unbalanced disc, demonstrating the capabilities of the toolbox. The software and examples are available on www.lpvcore.net.
One-Shot Coordination of First and Last Mode Transportation
Authors: Subhajit Goswami, Pavankumar Tallapragada
Date: 2020-01-05 00:00:00
Tasks: Computational Efficiency, Disaster Response

In this paper, we consider coordinated control of feeder vehicles for first and last mode transportation. The model is macroscopic with volumes of demands and supplies along with flows of vehicles. We propose a one-shot problem for transportation of demand to or from a hub within a fixed time window, assuming the knowledge of the demand and supply configurations. We present a unified optimization framework that is applicable for both operator profit maximization and social welfare maximization. The latter goal is useful for applications such as disaster response. The decision variables in the optimization problem are routing and allocations of the vehicles for different services. With K.K.T. analysis we propose an offline method for reducing the problem size. Further, we also analyze the problem of maximizing profits by optimally locating the supply for a given total supply and present a closed form expression of the maximum profits that can be earned over all supply configurations for a given demand configuration. We also show an equivalence between optimal supply location in the first mode problem and the last mode problem. We present a model for pricing based on the cost and travel time of the best alternate transportation and present necessary conditions for the feeder service to be viable. We illustrate the results through simulations and also compare the proposed model with a traditional vehicle routing problem. Through simulations, we also compare with the microscopic version of the problem with the decision variables being integers. We demonstrate that the route reduction algorithm proposed for the macroscopic formulation is still useful for computing nearly optimal solutions to the microscopic problem with much improved computational efficiency.
A Phase Theory of MIMO LTI Systems
Authors: Wei Chen, Dan Wang, Sei Zhen Khong, Li Qiu
Date: 2021-05-08 00:00:00
Tasks: LEMMA

In this paper, we define the phase response for a class of multi-input multi-output (MIMO) linear time-invariant (LTI) systems whose frequency responses are (semi-)sectorial at all frequencies. The newly defined phase concept subsumes the well-known notions of positive real systems and negative imaginary systems. We formulate a small phase theorem for feedback stability, which complements the celebrated small gain theorem. The small phase theorem lays the foundation of a phase theory of MIMO systems. We also discuss time-domain interpretations of phase-bounded systems via both energy signal analysis and power signal analysis. In addition, a sectored real lemma is derived for the computation of MIMO phases, which serves as a natural counterpart of the bounded real lemma.
Optimal Cybersecurity Investments in Large Networks Using SIS Model: Algorithm Design
Authors: Van Sy Mai, Richard J. La, Abdella Battou
Date: 2020-05-14 00:00:00

We study the problem of minimizing the (time) average security costs in large networks/systems comprising many interdependent subsystems, where the state evolution is captured by a susceptible-infected-susceptible (SIS) model. The security costs reflect security investments, economic losses and recovery costs from infections and failures following successful attacks. We show that the resulting optimization problem is nonconvex and propose a suite of algorithms - two based on a convex relaxation, and the other two for finding a local minimizer, based on a reduced gradient method and sequential convex programming. Also, we provide a sufficient condition under which the convex relaxations are exact and, hence, their solution coincides with that of the original problem. Numerical results are provided to validate our analytical results and to demonstrate the effectiveness of the proposed algorithms.
Impact of DER Communication Delay in AGC: Cyber-Physical Dynamic Simulation
Authors: Wenbo Wang, Xin Fang, Anthony Florita
Date: 2021-05-07 00:00:00

Distributed energy resource (DER) frequency regulations are promising technologies for future grid operation. Unlike conventional generators, DERs might require open communication networks to exchange signals with control centers, possibly through DER aggregators; therefore, the impacts of the communication variations on the system stability need to be investigated. This paper develops a cyber-physical dynamic simulation model based on the Hierarchical Engine for Large-Scale Co-Simulation (HELICS) to evaluate the impact of the communication variations, such as delays in DER frequency regulations. The feasible delay range can be obtained under different parameter settings. The results show that the risk of instability generally increases with the communication delay.
An interstellar communication method: system design and observations
Authors: William J. Crilly Jr
Date: 2021-05-08 00:00:00

A system of synchronized radio telescopes is utilized to search for hypothetical wide bandwidth interstellar communication signals. Transmitted signals are hypothesized to have characteristics that enable high channel capacity and minimally low energy per information bit, while containing energy-efficient signal elements that are readily discoverable, distinct from random noise. A hypothesized transmitter signal is described. Signal reception and discovery processes are detailed. Observations using individual and multiple synchronized radio telescopes, during 2017 - 2021, are described. Conclusions and further work are suggested.
Symbol Dynamics, Information theory and Complexity of Economic time series
Authors: Geoffrey Ducournau
Date: 2021-05-10 00:00:00
Tasks: Time Series, Time Series Analysis

We propose to examine the predictability and the complexity characteristics of the Standard&Poor500 dynamics behaviors in a coarse-grained way using the symbolic dynamics method and under the prism of the Information theory through the concept of entropy and uncertainty. We believe that experimental measurement of entropy as a way of examining the complexity of a system is more relevant than more common tests of universality in the transition to chaos because it does not make any prior prejudices on the underlying causes associated with the system dynamics, whether deterministic or stochastic. We regard the studied economic time series as being complex and propose to express it in terms of the amount of information this last is producing on different time scales and according to various scaling parameters.
Superresolution photoacoustic tomography using random speckle illumination and second order moments
Authors: Osman Asif Malik, Venkatalakshmi Vyjayanthi Narumanchi, Stephen Becker, Todd W. Murray
Date: 2021-05-09 00:00:00
Tasks: Object

Idier et al. [IEEE Trans. Comput. Imaging 4(1), 2018] propose a method which achieves superresolution in the microscopy setting by leveraging random speckle illumination and knowledge about statistical second order moments for the illumination patterns and model noise. This is achieved without any assumptions on the sparsity of the imaged object. In this paper, we show that their technique can be extended to photoacoustic tomography. We propose a simple algorithm for doing the reconstruction which only requires a small number of linear algebra steps. It is therefore much faster than the iterative method used by Idier et al. We also propose a new representation of the imaged object based on Dirac delta expansion functions.
Modeling the evolution of COVID-19 via compartmental and particle-based approaches: application to the Cyprus case
Authors: Constantia Alexandrou, Vangelis Harmandaris, Anastasios Irakleous, Giannis Koutsou, Nikos Savva
Date: 2020-08-07 00:00:00

We present two different approaches for modeling the spread of the COVID-19 pandemic. Both approaches are based on the population classes susceptible, exposed, infectious, quarantined, and recovered and allow for an arbitrary number of subgroups with different infection rates and different levels of testing. The first model is derived from a set of ordinary differential equations that incorporates the rates at which population transitions take place among classes. The other is a particle model, which is a specific case of crowd simulation model, in which the disease is transmitted through particle collisions and infection rates are varied by adjusting the particle velocities. The parameters of these two models are tuned using information on COVID-19 from the literature and country-specific data, including the effect of restrictions as they were imposed and lifted. We demonstrate the applicability of both models using data from Cyprus, for which we find that both models yield very similar results, giving confidence in the predictions.
Multi-Sensor Data Fusion for Accurate Traffic Speed and Travel Time Reconstruction
Authors: Lisa Kessler, Felix Rempe, Klaus Bogenberger
Date: 2021-05-08 00:00:00

This paper studies the joint reconstruction of traffic speeds and travel times by fusing sparse sensor data. Raw speed data from inductive loop detectors and floating cars as well as travel time measurements are combined using different fusion techniques. A novel fusion approach is developed which extends existing speed reconstruction methods to integrate low-resolution travel time data. Several state-of-the-art methods and the novel approach are evaluated on their performance in reconstructing traffic speeds and travel times using various combinations of sensor data. Algorithms and sensor setups are evaluated with real loop detector, floating car and Bluetooth data collected during severe congestion on German freeway A9. Two main aspects are examined: (i) which algorithm provides the most accurate result depending on the used data and (ii) which type of sensor and which combination of sensors yields higher estimation accuracies. Results show that, overall, the novel approach applied to a combination of floating-car data and loop data provides the best speed and travel time accuracy. Furthermore, a fusion of sources improves the reconstruction quality in many, but not all cases. In particular, Bluetooth data only provide a benefit for reconstruction purposes if integrated distinctively.
Efficient Peer Effects Estimators with Group Effects
Authors: Guido M. Kuersteiner, Ingmar R. Prucha, Ying Zeng
Date: 2021-05-10 00:00:00

We study linear peer effects models where peers interact in groups, individual's outcomes are linear in the group mean outcome and characteristics, and group effects are random. Our specification is motivated by the moment conditions imposed in Graham 2008. We show that these moment conditions can be cast in terms of a linear random group effects model and lead to a class of GMM estimators that are generally identified as long as there is sufficient variation in group size. We also show that our class of GMM estimators contains a Quasi Maximum Likelihood estimator (QMLE) for the random group effects model, as well as the Wald estimator of Graham 2008 and the within estimator of Lee 2007 as special cases. Our identification results extend insights in Graham 2008 that show how assumptions about random group effects as well as variation in group size can be used to overcome the reflection problem in identifying peer effects. Our QMLE and GMM estimators accommodate additional covariates and are valid in situations with a large but finite number of different group sizes or types. Because our estimators are general moment based procedures, using instruments other than binary group indicators in estimation is straight forward. Our QMLE estimator accommodates group level covariates in the spirit of Mundlak and Chamberlain and offers an alternative to fixed effects specifications. Monte-Carlo simulations show that the bias of the QMLE estimator decreases with the number of groups and the variation in group size, and increases with group size. We also prove the consistency and asymptotic normality of the estimator under reasonable assumptions.
Difference-in-Differences Estimation with Spatial Spillovers
Authors: Kyle Butts
Date: 2021-05-08 00:00:00
Tasks: counterfactual

Empirical work often uses treatment assigned following geographic boundaries. When the effects of treatment cross over borders, classical difference-in-differences estimation produces biased estimates for the average treatment effect. In this paper, I introduce a potential outcomes framework to model spillover effects and decompose the estimate's bias in two parts: (1) the control group no longer identifies the counterfactual trend because their outcomes are affected by treatment and (2) changes in treated units' outcomes reflect the effect of their own treatment status and the effect from the treatment status of 'close' units. I propose conditions for non-parametric identification that can remove both sources of bias and semi-parametrically estimate the spillover effects themselves including in settings with staggered treatment timing. To highlight the importance of spillover effects, I revisit analyses of three place-based interventions.