pwc-archive/papers-with-abstracts huggingface dataset:
Enhancing Feasibility and Safety of Nonlinear Model Predictive Control with Discrete-Time Control Barrier Functions
Authors: Jun Zeng, Zhongyu Li, Koushil Sreenath
Date: 2021-05-21 00:00:00
Tasks: Model Predictive Control

Safety is one of the fundamental problems in robotics. Recently, one-step or multi-step optimal control problems for discrete-time nonlinear dynamical system were formulated to offer tracking stability using control Lyapunov functions (CLFs) while subject to input constraints as well as safety-critical constraints using control barrier functions (CBFs). The limitations of these existing approaches are mainly about feasibility and safety. In the existing approaches, the feasibility of the optimization and the system safety cannot be enhanced at the same time theoretically. In this paper, we propose two formulations that unifies CLFs and CBFs under the framework of nonlinear model predictive control (NMPC). In the proposed formulations, safety criteria is commonly formulated as CBF constraints and stability performance is ensured with either a terminal cost function or CLF constraints. Slack variables with relaxing technique are introduced on the CBF constraints to resolve the tradeoff between feasibility and safety so that they can be enhanced at the same. The advantages about feasibility and safety of proposed formulations compared with existing methods are analyzed theoretically and validated with numerical results.
Analysis of Contractions in System Graphs: Application to State Estimation
Authors: Mohammadreza Doostmohammadian, Themistoklis Charalambous, Miadreza Shafie-khah, Hamid R. Rabiee, Usman A. Khan
Date: 2021-05-22 00:00:00
Tasks: Clustering, State Estimation

Observability and estimation are closely tied to the system structure, which can be visualized as a system graph--a graph that captures the inter-dependencies within the state variables. For example, in social system graphs such inter-dependencies represent the social interactions of different individuals. It was recently shown that contractions, a key concept from graph theory, in the system graph are critical to system observability, as (at least) one state measurement in every contraction is necessary for observability. Thus, the size and number of contractions are critical in recovering for loss of observability. In this paper, the correlation between the average-size/number of contractions and the global clustering coefficient (GCC) of the system graph is studied. Our empirical results show that estimating systems with high GCC requires fewer measurements, and in case of measurement failure, there are fewer possible options to find substitute measurement that recovers the system's observability. This is significant as by tuning the GCC, we can improve the observability properties of large-scale engineered networks, such as social networks and smart grid.
Remunerating Space-Time, Load-Shifting Flexibility from Data Centers in Electricity Markets
Authors: Weiqi Zhang, Victor M. Zavala
Date: 2021-05-24 00:00:00

We study an electricity market clearing formulation that seeks to remunerate spatio-temporal, load-shifting flexibility provided by data centers (DaCes). Load-shifting flexibility is a key asset for power grid operators as they aim to integrate larger amounts of intermittent renewable power and to decarbonize the grid. Central to our study is the concept of virtual links, which provide non-physical pathways that can be used by DaCes to shift power loads (by shifting computing loads) across space and time. We use virtual links to show that the clearing formulation treats DaCes as prosumers that simultaneously request load and provide a load-shifting flexibility service. Our analysis also reveals that DaCes are remunerated for the provision of load-shifting flexibility based on nodal price differences (across space and time). We also show that DaCe flexibility helps relieve space-time price volatility and show that the clearing formulation satisfies fundamental economic properties that are expected from coordinated markets (e.g., provides a competitive equilibrium and achieves revenue adequacy and cost recovery). The concepts presented are applicable to other key market players that can offer space-time shifting flexibility such as distributed manufacturing facilities and storage systems. Case studies are presented to demonstrate these properties.
Verification of Dissipativity and Evaluation of Storage Function in Economic Nonlinear MPC using Q-Learning
Authors: Arash Bahari Kordabad, Sebastien Gros
Date: 2021-05-24 00:00:00
Tasks: Q-Learning, Reinforcement Learning (RL)

In the Economic Nonlinear Model Predictive (ENMPC) context, closed-loop stability relates to the existence of a storage function satisfying a dissipation inequality. Finding the storage function is in general -- for nonlinear dynamics and cost -- challenging, and has attracted attentions recently. Q-Learning is a well-known Reinforcement Learning (RL) techniques that attempts to capture action-value functions based on the state-input transitions and stage cost of the system. In this paper, we present the use of the Q-Learning approach to obtain the storage function and verify the dissipativity for discrete-time systems subject to state-input constraints. We show that undiscounted Q-learning is able to capture the storage function for dissipative problems when the parameterization is rich enough. The efficiency of the proposed method will be illustrated in the different case studies.
Large-Signal Grid-Synchronization Stability Analysis of PLL-based VSCs Using Lyapunov's Direct Method
Authors: Yu Zhang, Chen Zhang, Xu Cai
Date: 2021-05-23 00:00:00

Grid-synchronization stability (GSS) is an emerging stability issue of grid-tied voltage source converters (VSCs), which can be provoked by severe grid voltage sags. Although a qualitative understanding of the mechanism behind the loss of synchronization has been acquired in recent studies, an analytical method for quantitative assessment of GSS of grid-tied VSCs is still missing. To bridge this gap, a dedicated Lyapunov function is analytically proposed, and its corresponding stability criterion for GSS analysis of grid-tied VSCs is rigorously constructed. Both theoretical analysis and simulation result demonstrate that the proposed method can provide a credible GSS evaluation compared to the previous EAC/EF-based method. Therefore, it can be applied for fast GSS evaluation of the grid-tied VSCs as is exemplified in this paper, as well as an analytical tool for some GSS-related issues, e.g., GSS-oriented parameter design and stabilization control.
Principled information fusion for multi-view multi-agent surveillance systems
Authors: Bailu Wang, Suqi Li, Giorgio Battistelli, Luigi Chisci, Wei Yi
Date: 2021-05-23 00:00:00
Tasks: Multi-Object Tracking, Object, Object Tracking

A key objective of multi-agent surveillance systems is to monitor a much larger region than the limited field-of-view (FoV) of any individual agent by successfully exploiting cooperation among multi-view agents. Whenever either a centralized or a distributed approach is pursued, this goal cannot be achieved unless an appropriately designed fusion strategy is adopted. This paper presents a novel principled information fusion approach for dealing with multi-view multi-agent case, on the basis of Generalized Covariance Intersection (GCI). The proposed method can be used to perform multi-object tracking on both a centralized and a distributed peer-to-peer sensor network. Simulation experiments on realistic multi-object tracking scenarios demonstrate effectiveness of the proposed solution.
A frequency domain approach for local module identification in dynamic networks
Authors: Karthik R. Ramaswamy, Péter Zoltán Csurcsia, Johan Schoukens, Paul M. J. Van den Hof
Date: 2021-05-23 00:00:00
Tasks: Network Identification

In classical approaches of dynamic network identification, in order to identify a system (module) embedded in a dynamic network, one has to formulate a Multi-input-Single-output (MISO) identification problem that requires identification of a parametric model for all the modules constituting the MISO setup including (possibly) the noise model, and determine their model order. This requirement leads to model order selection steps for modules that are of no interest to the experimenter which increases the computational complexity for large-sized networks. Also, identification using a parametric noise model (like BJ method) can suffer from local minima, however neglecting the noise model has its impact on the variance of the estimates. In this paper, we provide a two-step identification approach to avoid these problems. The first step involves performing a non-parametric indirect approach for a MISO identification problem to get the non-parametric frequency response function (FRF) estimates and its variance as a function of frequency. In the second step, the estimated non-parametric FRF of the target module is smoothed using a parametric frequency domain estimator with the estimated variance from the previous step as the non-parametric noise model. The developed approach is practical with weak assumptions on noise, uses the available toolbox, requires a parametric model only for the target module of interest, and uses a non-parametric noise model to reduce the variance of the estimates. Numerical simulations illustrate the potentials of the introduced method in comparison with the classical identification methods.
Simulation-Based Impact of Connected Vehicles in Platooning Mode on Travel Time, Emissions and Fuel Consumption
Authors: Aso Validi, Cristina Olaverri-Monreal
Date: 2021-05-23 00:00:00

Several approaches have been presented during the last decades to reduce carbon pollution from transportation. One example is the use of platooning mode. This paper considers data obtained from daily trips to investigate the impact of platooning on travel time, emissions of CO2, CO, NO2 and HC and fuel consumption on a road network in Upper Austria. For this purpose, we studied fuel combustion-based engines relying on the extension of the 3DCoAutoSim simulation platform. The obtained results showed that the platooning mode not only increased driving efficiency but also decreased the total emissions by reducing fuel consumption.
Dynamic Evaluation of Power Distribution Lines for Determining the Number of Repair Teams and Prioritizing the Resolution of Faults Caused by High-Impact Low-Probability Events
Authors: Hamidreza Sharifi Moghaddam, Reza Dashti, Abolfazl Ahmadi
Date: 2021-05-23 00:00:00

In order to increase the resilience of distribution systems against high-impact low-probability (HILP) events, it is important to prioritize the damaged assets so that the lost loads, especially critical and important loads, can be restored faster. In addition, correctly predicting the number of repair teams during critical times contributes to restoring the network to the initial resilience level. For this reason, this paper discusses the prioritization of electricity supply lines for evaluating the number of required repair teams. To this end, the economic value of distribution system lines has been considered as a criterion representing the sensitivity of the network to hurricanes. The modeling is based on value, in which the load value, failure probability of the poles, fragility curve, duration of line repair by the maintenance team, and the topology factor have been considered. This is so that the significance of the demand side, the failure extent and accessibility of the lines, the importance of time, and the network configuration are considered. The results provide a list of line priority for fault resolution, in which the topology factor has a larger effect. The number of repair teams required to restore critical and important loads is determined from this model. This modeling has been tested on an IEEE 33-bus network. Keywords: Resilience, distribution systems, asset evaluation, restoration prioritization, repair team
High Dimensional Robust Consensus over Networks with Limited Capacity
Authors: Yasin Yazicioglu, Alberto Speranzon
Date: 2021-05-22 00:00:00
Tasks: Vocal Bursts Intensity Prediction

We investigate robust linear consensus over networks under capacity-constrained communication. The capacity of each edge is encoded as an upper bound on the number of state variables that can be communicated instantaneously. When the edge capacities are small compared to the dimensionality of the state vectors, it is not possible to instantaneously communicate full state information over every edge. We investigate how robust consensus (small steady state variance of the states) can be achieved within a linear time-invariant setting by optimally assigning edges to state-dimensions. We show that if a finite steady state variance of the states can be achieved, then both the minimum cut capacity and the total capacity of the network should be sufficiently large. Optimal and approximate solutions are provided for some special classes of graphs. We also consider the related problem of optimally allocating additional capacity on a feasible initial solution. We show that this problem corresponds to the maximization of a submodular function subject to a matroid constraint, which can be approximated via a greedy algorithm.
Reconfigurable Surface Wave Platform Using Fluidic Conductive Structures
Authors: Z. Chu, K. K. Wong, K. F. Tong
Date: 2021-05-22 00:00:00

Surface wave inherently has less propagation loss as it adheres to the surface and minimizes unwanted dissipation in space. Recently, they find applications in network-on-chip (NoC) communications and intelligent surface aided mobile networked communications. This paper puts forward a reconfigurable surface wave platform (RSWP) that utilizes liquid metal to produce highly energy-efficient and adaptive pathways for surface wave transmission. Our simulation results illustrate that the proposed RSWP using Galinstan can obtain a $25{\rm dB}$ gain in the electric field for a propagation distance of $35\lambda$ at $30{\rm GHz}$ where $\lambda$ denotes the wavelength. Moreover, less than $1{\rm dB}$ loss is observed even at a distance of $50\lambda$, and a pathway with right-angled turns can also be created with only a $3.5{\rm dB}$ loss at the turn.
Tuning of Drone PD Controller Parameters for Medical Supplies Delivery
Authors: Azin Shamshirgaran, Hamed Javidi, Dan Simon
Date: 2021-05-22 00:00:00
Tasks: Evolutionary Algorithms

During the COVID-19 pandemic and similar outbreaks in the future, drones can be set up to reduce human interaction for medical supplies delivery, which is crucial in times of pandemic. In this short paper, we introduce the use of two evolutionary algorithms for multi-objective optimization (MOO) and tuning the parameters of the PD controller of a drone to follow the 3D desired path.
Reduction in Circulating Current with Improved Secondary Side Modulation in Isolated Current-Fed Half Bridge AC-DC Converter
Authors: Manish Kumar, Sumit Pramanick, B K Panigrahi
Date: 2021-05-22 00:00:00

Current-fed half bridge converter with bidirectional switches on ac side and a full bridge converter on dc side of a high frequency transformer is an optimal topology for single stage galvanically isolated ac-dc converter for onboard vehicle charging application. AC side switches are actively commutated to achieve zero current switching (ZCS) using single phase shift modulation (SPSM) and discontinuous current phase shift modulation (DCPSM). Furthermore, zero voltage turn-on (ZVS) is achieved for dc side switches. Compared to SPSM, DCPSM maintains a constant peak current in the converter throughout the grid cycle of ac mains voltage. However, constant peak current contributes to a high circulating current near the zero crossings of ac mains voltage and also at light load conditions. This paper proposes an improved discontinuous current phase shift modulation (IDCPSM) to increase the efficiency of the converter across different loading conditions. A dual control variable is adopted to actively reduce the circulating current while maintaining soft switching of both ac and dc side switches across the grid cycle of ac mains voltage. A 1.5 kW laboratory prototype has been developed to experimentally validate the analysis, design and improvement in performance for different loading conditions.
A Model Randomization Approach to Statistical Parameter Privacy
Authors: Ehsan Nekouei, Henrik Sandberg, Mikael Skoglund, Karl H. Johansson
Date: 2021-05-22 00:00:00
Tasks: model

In this paper, we study a privacy filter design problem for a sequence of sensor measurements whose joint probability density function (p.d.f.) depends on a private parameter. To ensure parameter privacy, we propose a filter design framework which consists of two components: a randomizer and a nonlinear transformation. The randomizer takes the private parameter as input and randomly generates a pseudo parameter. The nonlinear mapping transforms the measurements such that the joint p.d.f. of the filter's output depends on the pseudo parameter rather than the private parameter. It also ensures that the joint p.d.f. of the filter's output belongs to the same family of distributions as that of the measurements. The nonlinear transformation has a feedforward-feedback structure that allows real-time and causal generation of the disguised measurements with low complexity using a recursive structure. The design of the randomizer is formulated as an optimization problem subject to a privacy constraint, in terms of mutual information, and it is shown that the optimal randomizer is the solution of a convex optimization problem. Using information-theoretic inequalities, we show that the performance of any estimator of the private parameter, based on the output of the privacy filter, is limited by the privacy constraint. The structure of the nonlinear transformation is studied in the special cases of independent and identically distributed, Markovian, and Gauss-Markov measurements. Our results show that the privacy filter in the Gauss-Markov case can be implemented as two one-step ahead Kalman predictors and a set of minimum mean square error predictors. The Kalman predictors significantly reduce the complexity of computing the disguised measurements. A numerical example on occupancy privacy in a building automation system illustrates the approach.
An Efficient Network Solver for Dynamic Simulation of Power Systems Based on Hierarchical Inverse Computation and Modification
Authors: Lu Zhang, Bin Wang, Vivek Sarin, Weiping Shi, P. R. Kumar, Le Xie
Date: 2021-05-22 00:00:00

In power system dynamic simulation, up to 90% of the computational time is devoted to solve the network equations, i.e., a set of linear equations. Traditional approaches are based on sparse LU factorization, which is inherently sequential. In this paper, an inverse-based network solution is proposed by a hierarchical method for computing and store the approximate inverse of the conductance matrix in electromagnetic transient (EMT) simulations. The proposed method can also efficiently update the inverse by modifying only local sub-matrices to reflect changes in the network, e.g., loss of a line. Experiments on a series of simplified 179-bus Western Interconnection demonstrate the advantages of the proposed methods.
X-ray phase-contrast imaging: a broad overview of some fundamentals
Authors: David M. Paganin, Daniele Pelliccia
Date: 2020-11-08 00:00:00
Tasks: Retrieval

We outline some basics of imaging using both fully-coherent and partially-coherent X-ray beams, with an emphasis on phase-contrast imaging. We open with some of the basic notions of X-ray imaging, including the vacuum wave equations and the physical meaning of the intensity and phase of complex scalar fields. The projection approximation is introduced, together with the concepts of attenuation contrast and phase contrast. We also outline the multi-slice approach to X-ray propagation through thick samples or optical elements, together with the Fresnel scaling theorem. Having introduced the fundamentals, we then consider several aspects of the forward problem, of modelling the formation of phase-contrast X-ray images. Several topics related to this forward problem are considered, including the transport-of-intensity equation, arbitrary linear imaging systems, shift-invariant linear imaging systems, the transfer-function formalism, blurring induced by finite source size, the space-frequency model for partially-coherent fields, and the Fokker-Planck equation for paraxial X-ray imaging. Having considered these means for modelling the formation of X-ray phase-contrast images, we then consider aspects of the associated inverse problem of phase retrieval. This concerns how one may decode phase-contrast images to gain information regarding the sample-induced attenuation and phase shift.
Vector autoregression models with skewness and heavy tails
Authors: Sune Karlsson, Stepan Mazur, Hoang Nguyen
Date: 2021-05-24 00:00:00
Tasks: Bayesian Inference, Model Selection

With uncertain changes of the economic environment, macroeconomic downturns during recessions and crises can hardly be explained by a Gaussian structural shock. There is evidence that the distribution of macroeconomic variables is skewed and heavy tailed. In this paper, we contribute to the literature by extending a vector autoregression (VAR) model to account for a more realistic assumption of the multivariate distribution of the macroeconomic variables. We propose a general class of generalized hyperbolic skew Student's t distribution with stochastic volatility for the error term in the VAR model that allows us to take into account skewness and heavy tails. Tools for Bayesian inference and model selection using a Gibbs sampler are provided. In an empirical study, we present evidence of skewness and heavy tails for monthly macroeconomic variables. The analysis also gives a clear message that skewness should be taken into account for better predictions during recessions and crises.
Inference for multi-valued heterogeneous treatment effects when the number of treated units is small
Authors: Marina Dias, Demian Pouzo
Date: 2021-05-23 00:00:00
Tasks: valid

We propose a method for conducting asymptotically valid inference for treatment effects in a multi-valued treatment framework where the number of units in the treatment arms can be small and do not grow with the sample size. We accomplish this by casting the model as a semi-/non-parametric conditional quantile model and using known finite sample results about the law of the indicator function that defines the conditional quantile. Our framework allows for structural functions that are non-additively separable, with flexible functional forms and heteroskedasticy in the residuals, and it also encompasses commonly used designs like difference in difference. We study the finite sample behavior of our test in a Monte Carlo study and we also apply our results to assessing the effect of weather events on GDP growth.