pwc-archive/papers-with-abstracts huggingface dataset:
Robust Positive Consensus for Heterogeneous Multi-agent Systems
Authors: Ruonan Li, Yutao Tang, Shurong Li
Date: 2022-05-01 00:00:00

This paper investigates a robust positive consensus problem for a class of heterogeneous high-order multi-agent systems subject to external inputs. Compared with existing multi-agent consensus results, the most distinct feature of the formulated problem is that the state variables of all heterogeneous agents are confined in the positive orthant. To solve this problem, we present a two-step design procedure. By constructing an auxiliary multi-agent system as positive local reference generators, we incorporate the reference generator into some applicable decentralized robust tracking controller for each agent. The proposed distributed algorithm is proven to ensure a robust consensus fulfilling certain prescribed pattern for the multi-agent system under switching topology in the sense of finite-gain stability with respect to the external inputs. A simulation example is finally given to illustrate the effectiveness of our design.
Stable Reinforcement Learning for Optimal Frequency Control: A Distributed Averaging-Based Integral Approach
Authors: Yan Jiang, Wenqi Cui, Baosen Zhang, Jorge Cortés
Date: 2022-05-01 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning (RL)

Frequency control plays a pivotal role in reliable power system operations. It is conventionally performed in a hierarchical way that first rapidly stabilizes the frequency deviations and then slowly recovers the nominal frequency. However, as the generation mix shifts from synchronous generators to renewable resources, power systems experience larger and faster frequency fluctuations due to the loss of inertia, which adversely impacts the frequency stability. This has motivated active research in algorithms that jointly address frequency degradation and economic efficiency in a fast timescale, among which the distributed averaging-based integral (DAI) control is a notable one that sets controllable power injections directly proportional to the integrals of frequency deviation and economic inefficiency signals. Nevertheless, DAI do not typically consider the transient performance of the system following power disturbances and has been restricted to quadratic operational cost functions. This manuscript aims to leverage nonlinear optimal controllers to simultaneously achieve optimal transient frequency control and find the most economic power dispatch for frequency restoration. To this end, we integrate reinforcement learning (RL) to the classic DAI, which results in RL-DAI. Specifically, we use RL to learn a neural network-based control policy mapping from the integral variables of DAI to the controllable power injections which provides optimal transient frequency control, while DAI inherently ensures the frequency restoration and optimal economic dispatch. Compared to existing methods, we provide provable guarantees on the stability of the learned controllers and extend allowable cost functions to a much larger class. Simulations on the 39-bus New England system illustrate our results.
Ensuring Resilience Against Stealthy Attacks on Cyber-Physical Systems
Authors: Paul Griffioen, Bruce H. Krogh, Bruno Sinopoli
Date: 2022-05-01 00:00:00

This article provides a tool for analyzing mechanisms that aim to achieve resilience against stealthy, or undetectable, attacks on cyber-physical systems (CPSs). We consider attackers who are able to corrupt all of the inputs and outputs of the system. To counter such attackers, a response scheme must be implemented that keeps the attacker from corrupting the inputs and outputs of the system for certain periods of time. To aid in the design of such a response scheme, our tool provides sufficient lengths for these periods of time in order to ensure safety with a particular probability. We provide a conservative upper bound on how long the system can remain under stealthy attack before the safety constraints are violated. Furthermore, we show how a detector limits the set of biases an attacker can exert on the system while still remaining stealthy, aiding a system operator in the design of the detector. Our contributions are demonstrated with an illustrative example.
Direct closed-loop identification of continuous-time systems using fixed-pole observer model
Authors: Ichiro Maruta, Toshiharu Sugie
Date: 2022-05-01 00:00:00

This paper provides a method for obtaining a continuous-time model of a target system in closed-loop from input-output data alone, in the case where no knowledge of the controllers nor excitation signals is available and I/O data may suffer from unknown offsets. The proposed method is based on a fixed-pole observer model, which is a reasonable continuous-time version corresponding to the innovation model in discrete-time and allows the identification of unstable target systems. Furthermore, it is shown that the proposed method can be attributed to a convex optimization problem by fixing the observer poles. The method is within the framework of the stabilized output error method and shares usability advantages such as robustness to noise with complex dynamics and applicability to a wide class of models. The effectiveness of the method is illustrated through numerical examples.
Synchronous Multistep Predictive Spectral Control of the Switching Distortion in DC--DC Converters
Authors: Christian Korte, Till Luetje, Stefan M. Goetz
Date: 2022-04-30 00:00:00
Tasks: Model Predictive Control

In automotive power electronics, distortion and electromagnetic interference (EMI) generated by the switching action of power semiconductors can be a significant challenge for the design of a compact, lightweight vehicle. As semiconductor switching frequencies increase, e.g., through the introduction of new materials, such as gallium nitride and silicon carbide, this problem becomes more severe. We present a control scheme for an automotive dc-to-dc converter that reduces the EMI generated by shaping switching distortion predictively at the run time. The multistep model-predictive control scheme chooses the subsequent switching state that optimizes the output spectrum according to predefined criteria. To achieve real-time operation, it evaluates the possible switching state candidates for the next modulation step without explicitly solving a single Fourier transform. In addition, the switching rate and voltage ripple are controlled in a single unified control law. We present and experimentally validate that the control scheme can indeed run at real time already with currently available mid-range hardware. The results demonstrate that the largest spectral peak of the switching distortion can be decreased by 48 dB compared to conventional pulse-width modulation. Furthermore, spectral gaps can be implemented in the output distortion and altered in real-time, allowing certain frequency bands -- e.g., bands used by other sensitive electronics such as sensors, communication busses, or tuners -- to be kept free of EMI from the converter's switching actions.
A new safety-guided design methodology to complement model-based safety analysis for safety assurance
Authors: Minghui Sun, Cody H. Fleming
Date: 2022-04-30 00:00:00

With the rapid advancement of Formal Methods, Model-based Safety Analysis (MBSA) has been gaining tremendous attention for its ability to rigorously verify whether the safety-critical scenarios are adequately addressed by the design solution of a cyber-physical human system. However, there is a gap. If specific safety-critical scenarios are not included in the given design solution (i.e., the model) in the first place, the results of MBSA cannot be trusted for safety assurance. To tackle this problem, we propose a new safety-guided design methodology (called STPA+) to complement MBSA. Inspired by STPA, STPA+ treats a system as a control structure, which is particularly fit for systems with complex interactions between human, machine, and automation. Three methods are developed in STPA+ to tackle the possible omissions of safety-critical scenarios caused by incorrectly defined safety constraints, improperly constrained process model, and inadequately designed controller. In this way, STPA+ directly derives an adequately defined design solution as the input to an MBSA verification program and bridges the gap between current MBSA approaches and safety assurance.
An Approach for Fast Cascading Failure Simulation in Dynamic Models of Power Systems
Authors: Sina Gharebaghi, Nilanjan Ray Chaudhuri, Ting He, Thomas La Porta
Date: 2022-04-29 00:00:00

The ground truth for cascading failure in power system can only be obtained through a detailed dynamic model involving nonlinear differential and algebraic equations whose solution process is computationally expensive. This has prohibited adoption of such models for cascading failure simulation. To solve this, we propose a fast cascading failure simulation approach based on implicit Backward Euler method (BEM) with stiff decay property. Unfortunately, BEM suffers from hyperstability issue in case of oscillatory instability and converges to the unstable equilibrium. We propose a predictor-corrector approach to fully address the hyperstability issue in BEM. The predictor identifies oscillatory instability based on eigendecomposition of the system matrix at the post-disturbance unstable equilibrium obtained as a byproduct of BEM. The corrector uses right eigenvectors to identify the group of machines participating in the unstable mode. This helps in applying appropriate protection schemes as in ground truth. We use Trapezoidal method (TM)-based simulation as the benchmark to validate the results of the proposed approach on the IEEE 118-bus network, 2,383-bus Polish grid, and IEEE 68-bus system. The proposed approach is able to track the cascade path and replicate the end results of TM-based simulation with very high accuracy while reducing the average simulation time by approximately 10-35 fold. The proposed approach was also compared with the partitioned method, which led to similar conclusions.
Sizing Battery Energy Storage and PV System in an Extreme Fast Charging Station Considering Uncertainties and Battery Degradation
Authors: Waqas ur Rehman, Rui Bo, Hossein Mehdipourpicha, Jonathan Kimball
Date: 2022-04-29 00:00:00
Tasks: energy management, Management

This paper presents mixed integer linear programming (MILP) formulations to obtain optimal sizing for a battery energy storage system (BESS) and solar generation system in an extreme fast charging station (XFCS) to reduce the annualized total cost. The proposed model characterizes a typical year with eight representative scenarios and obtains the optimal energy management for the station and BESS operation to exploit the energy arbitrage for each scenario. Contrasting extant literature, this paper proposes a constant power constant voltage (CPCV) based improved probabilistic approach to model the XFCS charging demand for weekdays and weekends. This paper also accounts for the monthly and annual demand charges based on realistic utility tariffs. Furthermore, BESS life degradation is considered in the model to ensure no replacement is needed during the considered planning horizon. Different from the literature, this paper offers pragmatic MILP formulations to tally BESS charge/discharge cycles using the cumulative charge/discharge energy concept. McCormick relaxations and the Big-M method are utilized to relax the bi-linear terms in the BESS operational constraints. Finally, a robust optimization-based MILP model is proposed and leveraged to account for uncertainties in electricity price, solar generation, and XFCS demand. Case studies were performed to signify the efficacy of the proposed formulations.
The Cost of Lunar Landing Pads with a Trade Study of Construction Methods
Authors: Philip T. Metzger, Greg W. Autry
Date: 2022-05-01 00:00:00

This study estimates the cost of building lunar landing pads and examines whether any construction methods are economically superior to others. Some proposed methods require large amounts of mass transported from the Earth, others require high energy consumption on the lunar surface, and others have a long construction time. Each of these factors contributes direct and indirect costs to lunar activities. The most important economic variables turn out to be the transportation cost to the lunar surface and the magnitude of the program delay cost imposed by a construction method. The cost of a landing pad depends sensitively on the optimization of the mass and speed of the construction equipment, so a minimum-cost set of equipment exists for each construction method within a specified economic scenario. Several scenarios have been analyzed across a range of transportation costs with both high and low program delay costs. It is found that microwave sintering is currently the most favorable method to build the inner, high temperature zone of a lunar landing pad, although other methods are within the range of uncertainty. The most favorable method to build the outer, low temperature zone of the landing pad is also sintering when transportation costs are high, but it switches to polymer infusion when transportation costs drop below about \$110K/kg to the lunar surface. It is estimated that the Artemis Basecamp could build a landing pad with a budgeted line-item cost of \$229M assuming that transportation costs will be reduced modestly from the current rate \$1M/kg to the lunar surface to \$300K/kg. A landing pad drops to \$130M when the transportation cost drops further to \$100K/kg, or to \$47M if transportation costs fall below \$10K/kg. Ultimately, landing pads can be built around the Moon at very low cost, due to economies of scale.
Excess Out-of-Sample Risk and Fleeting Modes
Authors: Jean-Philippe Bouchaud, Iacopo Mastromatteo, Marc Potters, Konstantin Tikhonov
Date: 2022-05-02 00:00:00

Using Random Matrix Theory, we propose a universal and versatile tool to reveal the existence of "fleeting modes", i.e. portfolios that carry statistically significant excess risk, signalling ex-post a change in the correlation structure in the underlying asset space. Our proposed test is furthermore independent of the "true" (but unknown) underlying correlation structure. We show empirically that such fleeting modes exist both in futures markets and in equity markets. We proposed a metric to quantify the alignment between known factors and fleeting modes and identify momentum as a source of excess risk in the equity space.
Designing thermal radiation metamaterials via hybrid adversarial autoencoder and Bayesian optimization
Authors: Dezhao Zhu, Jiang Guo, Gang Yu, C. Y. Zhao, Hong Wang, Shenghong Ju
Date: 2022-04-26 00:00:00
Tasks: Bayesian Optimization

Designing thermal radiation metamaterials is challenging especially for problems with high degrees of freedom and complex objective. In this letter, we have developed a hybrid materials informatics approach which combines the adversarial autoencoder and Bayesian optimization to design narrowband thermal emitters at different target wavelengths. With only several hundreds of training data sets, new structures with optimal properties can be quickly figured out in a compressed 2-dimensional latent space. This enables the optimal design by calculating far less than 0.001\% of the total candidate structures, which greatly decreases the design period and cost. The proposed design framework can be easily extended to other thermal radiation metamaterials design with higher dimensional features.
Off-resonance artifact correction for magnetic resonance imaging: a review
Authors: Melissa W. Haskell, Jon-Fredrik Nielsen, Douglas C. Noll
Date: 2022-05-02 00:00:00

In magnetic resonance imaging (MRI), inhomogeneity in the main magnetic field used for imaging, referred to as off-resonance, can lead to image artifacts ranging from mild to severe depending on the application. Off-resonance artifacts, such as signal loss, geometric distortions, and blurring, can compromise the clinical and scientific utility of MR images. In this review, we describe sources of off-resonance in MRI, how off-resonance affects images, and strategies to prevent and correct for off-resonance. Given recent advances and the great potential of low field and/or portable MRI, we also highlight the advantages and challenges of imaging at low field with respect to off-resonance.
Physics-guided Terahertz Computational Imaging
Authors: Weng-Tai Su, Yi-Chun Hung, Po-Jen Yu, Chia-Wen Lin, Shang-Hua Yang
Date: 2022-04-30 00:00:00
Tasks: Image Restoration

Visualizing information inside objects is an ever-lasting need to bridge the world from physics, chemistry, biology to computation. Among all tomographic techniques, terahertz (THz) computational imaging has demonstrated its unique sensing features to digitalize multi-dimensional object information in a non-destructive, non-ionizing, and non-invasive way. Applying modern signal processing and physics-guided modalities, THz computational imaging systems are now launched in various application fields in industrial inspection, security screening, chemical inspection and non-destructive evaluation. In this article, we overview recent advances in THz computational imaging modalities in the aspects of system configuration, wave propagation and interaction models, physics-guided algorithm for digitalizing interior information of imaged objects. Several image restoration and reconstruction issues based on multi-dimensional THz signals are further discussed, which provides a crosslink between material digitalization, functional property extraction, and multi-dimensional imager utilization from a signal processing perspective.
A short term credibility index for central banks under inflation targeting: an application to Brazil
Authors: Alain Hecq, Joao Issler, Elisa Voisin
Date: 2022-05-02 00:00:00

This paper uses predictive densities obtained via mixed causal-noncausal autoregressive models to evaluate the statistical sustainability of Brazilian inflation targeting system with the tolerance bounds. The probabilities give an indication of the short-term credibility of the targeting system without requiring modelling people's beliefs. We employ receiver operating characteristic curves to determine the optimal probability threshold from which the bank is predicted to be credible. We also investigate the added value of including experts predictions of key macroeconomic variables.
A Note on "A survey of preference estimation with unobserved choice set heterogeneity" by Gregory S. Crawford, Rachel Griffith, and Alessandro Iaria
Authors: C. Angelo Guevara
Date: 2022-05-02 00:00:00
Tasks: Discrete Choice Models

Crawford's et al. (2021) article on estimation of discrete choice models with unobserved or latent consideration sets, presents a unified framework to address the problem in practice by using "sufficient sets", defined as a combination of past observed choices. The proposed approach is sustained in a re-interpretation of a consistency result by McFadden (1978) for the problem of sampling of alternatives, but the usage of that result in Crawford et al. (2021) is imprecise in an important matter. It is stated that consistency would be attained if any subset of the true consideration set is used for estimation, but McFadden (1978) shows that, in general, one needs to do a sampling correction that depends on the protocol used to draw the choice set. This note derives the sampling correction that is required when the choice set for estimation is built from past choices. Then, it formalizes the conditions under which such correction would fulfill the uniform condition property and can therefore be ignored when building practical estimators, such as the ones analyzed by Crawford et al. (2021).
Higher-order Expansions and Inference for Panel Data Models
Authors: Jiti Gao, Bin Peng, Yayi Yan
Date: 2022-05-01 00:00:00

In this paper, we propose a simple inferential method for a wide class of panel data models with a focus on such cases that have both serial correlation and cross-sectional dependence. In order to establish an asymptotic theory to support the inferential method, we develop some new and useful higher-order expansions, such as Berry-Esseen bound and Edgeworth Expansion, under a set of simple and general conditions. We further demonstrate the usefulness of these theoretical results by explicitly investigating a panel data model with interactive effects which nests many traditional panel data models as special cases. Finally, we show the superiority of our approach over several natural competitors using extensive numerical studies.
Greenhouse Gas Emissions and its Main Drivers: a Panel Assessment for EU-27 Member States
Authors: I. Jianu, S. M. Jeloaica, M. D. Tudorache
Date: 2022-04-30 00:00:00

This paper assesses the effects of greenhouse gas emissions drivers in EU-27 over the period 2010-2019, using a Panel EGLS model with period fixed effects. In particular, we focused our research on studying the effects of GDP, renewable energy, households energy consumption and waste on the greenhouse gas emissions. In this regard, we found a positive relationship between three independent variables (real GDP per capita, households final consumption per capita and waste generation per capita) and greenhouse gas emissions per capita, while the effect of the share of renewable energy in gross final energy consumption on the dependent variable proved to be negative, but quite low. In addition, we demonstrate that the main challenge that affects greenhouse gas emissions is related to the structure of households energy consumption, which is generally composed by environmentally harmful fuels. This suggests the need to make greater efforts to support the shift to a green economy based on a higher energy efficiency.
A basic macroeconomic agent-based model for analyzing monetary regime shifts
Authors: Florian Peters, Doris Neuberger, Oliver Reinhardt, Adelinde Uhrmacher
Date: 2022-05-02 00:00:00

In macroeconomics, an emerging discussion of alternative monetary systems addresses the dimensions of systemic risk in advanced financial systems. Monetary regime changes with the aim of achieving a more sustainable financial system have already been discussed in several European parliaments and were the subject of a referendum in Switzerland. However, their effectiveness and efficacy concerning macro-financial stability are not well-known. This paper introduces a macroeconomic agent-based model (MABM) in a novel simulation environment to simulate the current monetary system, which may serve as a basis to implement and analyze monetary regime shifts. In this context, the monetary system affects the lending potential of banks and might impact the dynamics of financial crises. MABMs are predestined to replicate emergent financial crisis dynamics, analyze institutional changes within a financial system, and thus measure macro-financial stability. The used simulation environment makes the model more accessible and facilitates exploring the impact of different hypotheses and mechanisms in a less complex way. The model replicates a wide range of stylized economic facts, including simplifying assumptions to reduce model complexity.