pwc-archive/papers-with-abstracts huggingface dataset:
Conditions for Convergence of Dynamic Regressor Extension and Mixing Parameter Estimator Using LTI Filters
Authors: Bowen Yi, Romeo Ortega
Date: 2020-07-30 00:00:00

In this note we study the conditions for convergence of recently introduced dynamic regressor extension and mixing (DREM) parameter estimator when the extended regressor is generated using LTI filters. In particular, we are interested in relating these conditions with the ones required for convergence of the classical gradient (or least squares), namely the well-known persistent excitation (PE) requirement on the original regressor vector, $\phi(t) \in \mathbb{R}^q$, with $q \in \mathbb{N}$ the number of unknown parameters. Moreover, we study the case when only interval excitation (IE) is available, under which DREM, concurrent and composite learning schemes ensure global convergence, being the convergence for DREM in finite time. Regarding PE we prove that, under some mild technical assumptions, if $\phi(t)$ is PE then the scalar regressor of DREM, $\Delta(t) \in \mathbb{R}$, is also PE, ensuring exponential convergence. Concerning IE we prove that if $\phi(t)$ is IE then $\Delta(t)$ is also IE. All these results are established in the almost sure sense, namely proving that the set of filter parameters for which the claims do not hold is of zero measure. The main technical tool used in our proof is inspired by a study of Luenberger observers for nonautonomous nonlinear systems recently reported in the literature.
An Efficient Asynchronous Batch Bayesian Optimization Approach for Analog Circuit Synthesis
Authors: Shuhan Zhang, Fan Yang, Dian Zhou, Xuan Zeng
Date: 2021-06-28 00:00:00
Tasks: Bayesian Optimization, Diversity

In this paper, we propose EasyBO, an Efficient ASYnchronous Batch Bayesian Optimization approach for analog circuit synthesis. In this proposed approach, instead of waiting for the slowest simulations in the batch to finish, we accelerate the optimization procedure by asynchronously issuing the next query points whenever there is an idle worker. We introduce a new acquisition function that can better explore the design space for asynchronous batch Bayesian optimization. A new strategy is proposed to better balance the exploration and exploitation and guarantee the diversity of the query points. And a penalization scheme is proposed to further avoid redundant queries during the asynchronous batch optimization. The efficiency of optimization can thus be further improved. Compared with the state-of-the-art batch Bayesian optimization algorithm, EasyBO achieves up to 7.35 times speed-up without sacrificing the optimization results.
Unifying Revealed Preference and Revealed Rational Inattention
Authors: Kunal Pattanayak, Vikram Krishnamurthy
Date: 2021-06-28 00:00:00

This paper unifies two key results from economic theory, namely, revealed rational inattention and classical revealed preference. Revealed rational inattention tests for rationality of information acquisition for Bayesian decision makers. On the other hand, classical revealed preference tests for utility maximization under known budget constraints. Our first result is an equivalence result - we unify revealed rational inattention and revealed preference through an equivalence map over decision parameters and partial order for payoff monotonicity over the decision space in both setups. Second, we exploit the unification result computationally to extend robustness measures for goodness-of-fit of revealed preference tests in the literature to revealed rational inattention. This extension facilitates quantifying how well a Bayesian decision maker's actions satisfy rational inattention. Finally, we illustrate the significance of the unification result on a real-world YouTube dataset comprising thumbnail, title and user engagement metadata from approximately 140,000 videos. We compute the Bayesian analog of robustness measures from revealed preference literature on YouTube metadata features extracted from a deep auto-encoder, i.e., a deep neural network that learns low-dimensional features of the metadata. The computed robustness values show that YouTube user engagement fits the rational inattention model remarkably well. All our numerical experiments are completely reproducible.
Data-driven Model Predictive and Reinforcement Learning Based Control for Building Energy Management: a Survey
Authors: Huiliang Zhang, Sayani Seal, Di wu, Benoit Boulet, Francois Bouffard, Geza Joos
Date: 2021-06-28 00:00:00
Tasks: energy management, Management, Model Predictive Control, reinforcement-learning, Reinforcement Learning (RL)

Building energy management is one of the core problems in modern power grids to reduce energy consumption while ensuring occupants' comfort. However, the building energy management system (BEMS) is now facing more challenges and uncertainties with the increasing penetration of renewables and complicated interactions between humans and buildings. Classical model predictive control (MPC) has shown its capacity to reduce building energy consumption, but it suffers from labor-intensive modelling and complex on-line control optimization. Recently, with the growing accessibility to the building control and automation data, data-driven solutions have attracted more research interest. This paper presents a compact review of the recent advances in data-driven MPC and reinforcement learning based control methods for BEMS. The main challenges in these approaches and insights on the selection of a control method are discussed.
Armoured Fighting Vehicle Team Performance Prediction against Missile Attacks with Directed Energy Weapons
Authors: Graham V. Weinberg, Mitchell Kracman
Date: 2021-06-28 00:00:00

A recent study has introduced a procedure to quantify the survivability of a team of armoured fighting vehicles when it is subjected to a single missile attack. In particular this study investigated the concept of collaborative active protection systems, focusing on the case where vehicle defence is provided by high power radio frequency directed energy weapons. The purpose of the current paper is to demonstrate how this analysis can be extended to account for more than one missile threat. This is achieved by introducing a jump stochastic process whose states represent the number of missiles defeated at a given time instant. Analysis proceeds through consideration of the sojourn times of this stochastic process, and it is shown how consideration of these jump times can be related to transition probabilities of the auxiliary stochastic process. The latter probabilities are then related to the probabilities of detection and disruption of missile threats. The sum of these sojourn times can then be used to quantify the survivability of the team at any given time instant. Due to the fact that there is much interest in the application of high energy lasers in the context of this paper, the numerical examples will thus focus on such directed energy weapons for armoured fighting vehicle team defence.
Intelligent Omni-Surface: Ubiquitous Wireless Transmission by Reflective-Transmissive Metasurface
Authors: Shuhang Zhang, Hongliang Zhang, Boya Di, Yunhua Tan, Marco Di Renzo, Zhu Han, H. Vincent Poor, Lingyang Song
Date: 2020-11-02 00:00:00

Intelligent reflecting surface (IRS), which is capable to adjust propagation conditions by controlling phase shifts of the reflected waves that impinge on the surface, has been widely analyzed for enhancing the performance of wireless systems. However, the reflective properties of widely studied IRSs restrict the service coverage to only one side of the surface. In this paper, to extend the wireless coverage of communication systems, we introduce the concept of intelligent omni-surface (IOS)-assisted communication. More precisely, IOS is an important instance of reconfigurable intelligent surface (RIS) that is capable to provide service coverage to the mobile users (MUs) in a reflective and a transmissive manner. We consider a downlink IOS-assisted communication system, where a multi-antenna small base station (SBS) and an IOS perform beamforming jointly, to improve the received power of multiple MUs on both sides of the IOS, through different reflective/transmissive channels. To maximize the sum-rate, we formulate a joint IOS phase shift design and SBS beamforming optimization problem, and propose an iterative algorithm to solve the resulting non-convex program efficiently. Both theoretical analysis and simulation results show that an IOS significantly extends the service coverage of the SBS when compared to an IRS.
On the Design of an Insurance Mechanism for Reliability Differentiation in Electricity Markets
Authors: Farhad Billimoria, Filiberto Fele, Iacopo Savelli, Thomas Morstyn, Malcolm McCulloch
Date: 2021-06-28 00:00:00
Tasks: Management

Securing an adequate supply of dispatchable resources is critical for keeping a power system reliable under high penetrations of variable generation. Traditional resource adequacy mechanisms are poorly suited to exploiting the growing flexibility and heterogeneity of load enabled by advancements in distributed resource and control technology. To address these challenges this paper develops a resource adequacy mechanism for the electricity sector utilising insurance risk management frameworks that is adapted to a future with variable generation and flexible demand. The proposed design introduces a central insurance scheme with prudential requirements that align diverse consumer reliability preferences with the financial objectives of an insurer-of-last-resort. We illustrate the benefits of the scheme in (i) differentiating load by usage to enable better management of the system during times of extreme scarcity, (ii) incentivising incremental investment in generation infrastructure that is aligned with consumer reliability preferences and (iii) improving overall reliability outcomes for consumers.
A Cooperative Game Theory-based Approach to Under-frequency Load Shedding Control
Authors: Mukesh Gautam, Narayan Bhusal, Mohammed Ben-Idris
Date: 2021-06-27 00:00:00

This paper proposes a cooperative game theory-based under-frequency load shedding (UFLS) approach for frequency stability and control in power systems. UFLS is a crucial factor for frequency stability and control especially in power grids with high penetration of renewable energy sources and restructured power systems. Conventional UFLS methods, most of which are off-line, usually shed fixed amounts of predetermined loads based on a predetermined schedule which can lead to over or under curtailment of load. This paper presents a co-operative game theory-based two-stage strategy to effectively and precisely determine locations and amounts of loads to be shed for UFLS control. In the first stage, the total amount of loads to be shed, also referred to as deficit in generation or the disturbance power, is computed using the initial rate of change of frequency (ROCOF) referred to the equivalent inertial center. In the second stage, the Shapley value, one of the solution concepts of cooperative game theory, is used to determine load shedding amounts and locations. The proposed method is implemented on the reduced 9-bus 3-machine Western Electricity Coordinating Council (WECC) system and simulated on Real-time Digital Simulators (RTDS). The results show that the proposed UFLS approach can effectively return the system to normal state after disturbances.
Disconnectivity-Aware Energy-Efficient Cargo-UAV Trajectory Planning with Minimum Handoffs
Authors: Nesrine Cherif, Wael Jaafar, Halim Yanikomeroglu, Abbas Yongacoglu
Date: 2021-06-27 00:00:00
Tasks: Trajectory Planning

On-board battery consumption, cellular disconnectivity, and frequent handoff are key challenges for unmanned aerial vehicle (UAV) based delivery missions, a.k.a., cargo-UAV. Indeed, with the introduction of UAV technology into cargo shipping and logistics, designing energy-efficient paths becomes a serious issue for the next retail industry transformation. Typically, the latter has to guarantee uninterrupted or slightly interrupted cellular connectivity for the UAV's command and control through a small number of handoffs. In this paper, we formulate the trajectory planning as a multi-objective problem aiming to minimize both the UAV's energy consumption and the handoff rate, constrained by the UAV battery size and disconnectivity rate. Due to the problem's complexity, we propose a dynamic programming based solution. Through simulations, we demonstrate the efficiency of our approach in providing optimized UAV trajectories. Also, the impact of several parameters, such as the cargo-UAV altitude, disconnectivity rate, and type of environment, are investigated. The obtained results allow to draw recommendations and guidelines for cargo-UAV operations.
Joint Mobile Charging and Coverage-Time Extension for Unmanned Aerial Vehicles
Authors: Soohyun Park, Won-Yong Shin, Minseok Choi, Joongheon Kim
Date: 2021-06-27 00:00:00
Tasks: Scheduling

In modern networks, the use of drones as mobile base stations (MBSs) has been discussed for coverage flexibility. However, the realization of drone-based networks raises several issues. One of the critical issues is drones are extremely power-hungry. To overcome this, we need to characterize a new type of drones, so-called charging drones, which can deliver energy to MBS drones. Motivated by the fact that the charging drones also need to be charged, we deploy ground-mounted charging towers for delivering energy to the charging drones. We introduce a new energy-efficiency maximization problem, which is partitioned into two independently separable tasks. More specifically, as our first optimization task, two-stage charging matching is proposed due to the inherent nature of our network model, where the first matching aims to schedule between charging towers and charging drones while the second matching solves the scheduling between charging drones and MBS drones. We analyze how to convert the formulation containing non-convex terms to another one only with convex terms. As our second optimization task, each MBS drone conducts energy-aware time-average transmit power allocation minimization subject to stability via Lyapunov optimization. Our solutions enable the MBS drones to extend their lifetimes; in turn, network coverage-time can be extended.
Sparse Control Synthesis for Uncertain Responsive Loads with Stochastic Stability Guarantees
Authors: Sai Pushpak Nandanoori, Soumya Kundu, Jianming Lian, Umesh Vaidya, Draguna Vrabie, Karanjit Kalsi
Date: 2021-06-27 00:00:00

Recent studies have demonstrated the potential of flexible loads in providing frequency response services. However, uncertainty and variability in various weather-related and end-use behavioral factors often affect the demand-side control performance. This work addresses this problem with the design of a demand-side control to achieve frequency response under load uncertainties. Our approach involves modeling the load uncertainties via stochastic processes that appear as both multiplicative and additive to the system states in closed-loop power system dynamics. Extending the recently developed mean square exponential stability (MSES) results for stochastic systems, we formulate multi-objective linear matrix inequality (LMI)-based optimal control synthesis problems to not only guarantee stochastic stability, but also promote sparsity, enhance closed-loop transient performance, and maximize allowable uncertainties. The fundamental trade-off between the maximum allowable (\textit{critical}) uncertainty levels and the optimal stochastic stabilizing control efforts is established. Moreover, the sparse control synthesis problem is generalized to the realistic power systems scenario in which only partial-state measurements are available. Detailed numerical studies are carried out on IEEE 39-bus system to demonstrate the closed-loop stochastic stabilizing performance of the sparse controllers in enhancing frequency response under load uncertainties; as well as illustrate the fundamental trade-off between the allowable uncertainties and optimal control efforts.
Adaptive Smooth Disturbance Observer-Based Fast Finite-Time Attitude Tracking Control of a Small Unmanned Helicopter
Authors: Xidong Wang, Zhan Li, Xinghu Yu, Zhen He
Date: 2021-06-26 00:00:00

In this paper, a novel adaptive smooth disturbance observer-based fast finite-time adaptive backstepping control scheme is presented for the attitude tracking of the 3-DOF helicopter system subject to compound disturbances. First, an adaptive smooth disturbance observer (ASDO) is proposed to estimate the composite disturbance, which owns the characteristics of smooth output, fast finite-time convergence, and adaptability to the disturbance of unknown derivative boundary. Then, a finite-time backstepping control protocol is construct to drive the elevation and pitch angles to track reference trajectories. To tackle the "explosion of complexity" and "singularity" problems in the conventional backstepping design framework, a fast finite-time command filter (FFTCF) is utilized to estimate the virtual control signal and its derivative. Moreover, a fractional power-based auxiliary dynamic system is introduced to compensate the error caused by the FFTCF estimation. Furthermore, an improved fractional power-based adaptive law with the $\sigma $-modification term is designed to attenuate the observer approximation error, such that the tracking performance is further enhanced. In terms of the fast finite-time stability theory, the signals of the closed-loop system are all fast finite-time bounded while the attitude tracking errors can fast converge to a sufficiently small region of the origin in finite time. Finally, a contrastive numerical simulation is carried out to validate the effectiveness and superiority of the designed control scheme.
Partial Strong Structural Controllability
Authors: Yuan Zhang, Yuanqing Xia
Date: 2021-06-26 00:00:00

This paper introduces a new controllability notion, termed partial strong structural controllability (PSSC), on a structured system whose entries of system matrices are either fixed zero or indeterminate, which naturally extends the conventional strong structural controllability (SSC) and bridges the gap between structural controllability and SSC. Dividing the indeterminate entries into two categories, generic entries and unspecified entries, a system is PSSC, if for almost all values of the generic entries in the parameter space except for a set of measure zero, and any nonzero (complex) values of the unspecified entries, the corresponding system is controllable. We highlight that this notion generalizes the generic property embedded in the conventional structural controllability for single-input systems. We then give algebraic and (bipartite) graph-theoretic necessary and sufficient conditions for single-input systems to be PSSC. Conditions for multi-input systems are subsequently given for a special case. It is shown the established results can induce a new maximum matching based criterion for SSC over the system bipartite graph representations.
Anticipatory routing methods for an on-demand ridepooling mobility system
Authors: Andres Fielbaum, Maximilian Kronmuller, Javier Alonso-Mora
Date: 2021-06-25 00:00:00

One of the most relevant challenges regarding on-demand ridepooling relates to the spatial imbalances of the demand, which induce a mismatch between the position of the vehicles and the origins of the emerging requests. Most ridepooling models face this problem through rebalancing methods only, i.e., moving idle vehicles towards areas with high rejections rate, which is done independently from routing and vehicle-to-orders assignments, so that vehicles serving passengers (a large portion of the total fleet) remain unaffected. This paper introduces two types of techniques for anticipatory routing that affect how vehicles are assigned to users and how to route vehicles to serve such users, so that the whole operation of the system is modified to reach more efficient states for future requests. Both techniques do not require any assumption or exogenous knowledge about the future demand, as they depend only on current and recent requests. Firstly, we introduce rewards that reduce the cost of an assignment between a vehicle and a group of passengers if the vehicle gets routed towards a high-demand zone. Secondly, we include a small set of artificial requests, whose request times are in the near future and whose origins are sampled from a probability distribution that mimics observed generation rates. These artificial requests are to be assigned together with the real requests. We test these techniques using a set of real rides from Manhattan. Introducing rewards can diminish the rejection rate to about nine-tenths of its original value. On the other hand, including future requests can reduce users' traveling times by about one-fifth, but increasing rejections. Both methods increase the vehicles-hour-traveled by about 10%. Spatial analysis reveals that vehicles are indeed moved towards the most demanded areas, such that the reduction in rejections rate is achieved mostly there.
Self-Attentive Ensemble Transformer: Representing Ensemble Interactions in Neural Networks for Earth System Models
Authors: Tobias Sebastian Finn
Date: 2021-06-21 00:00:00

Ensemble data from Earth system models has to be calibrated and post-processed. I propose a novel member-by-member post-processing approach with neural networks. I bridge ideas from ensemble data assimilation with self-attention, resulting into the self-attentive ensemble transformer. Here, interactions between ensemble members are represented as additive and dynamic self-attentive part. As proof-of-concept, I regress global ECMWF ensemble forecasts to 2-metre-temperature fields from the ERA5 reanalysis. I demonstrate that the ensemble transformer can calibrate the ensemble spread and extract additional information from the ensemble. As it is a member-by-member approach, the ensemble transformer directly outputs multivariate and spatially-coherent ensemble members. Therefore, self-attention and the transformer technique can be a missing piece for a non-parametric post-processing of ensemble data with neural networks.
Bitcoin, Currencies, and Fragility
Authors: Nassim Nicholas Taleb
Date: 2021-06-27 00:00:00

This discussion applies quantitative finance methods and economic arguments to cryptocurrencies in general and bitcoin in particular -- as there are about $10,000$ cryptocurrencies, we focus (unless otherwise specified) on the most discussed crypto of those that claim to hew to the original protocol (Nakamoto 2009) and the one with, by far, the largest market capitalization. In its current version, in spite of the hype, bitcoin failed to satisfy the notion of "currency without government" (it proved to not even be a currency at all), can be neither a short nor long term store of value (its expected value is no higher than $0$), cannot operate as a reliable inflation hedge, and, worst of all, does not constitute, not even remotely, a safe haven for one's investments, a shield against government tyranny, or a tail protection vehicle for catastrophic episodes. Furthermore, bitcoin promoters appear to conflate the success of a payment mechanism (as a decentralized mode of exchange), which so far has failed, with the speculative variations in the price of a zero-sum maximally fragile asset with massive negative externalities. Going through monetary history, we show how a true numeraire must be one of minimum variance with respect to an arbitrary basket of goods and services, how gold and silver lost their inflation hedge status during the Hunt brothers squeeze in the late 1970s and what would be required from a true inflation hedged store of value.