pwc-archive/papers-with-abstracts huggingface dataset:
A Scenario-oriented Approach to Energy-Reserve Joint Procurement and Pricing
Authors: Jiantao Shi, Ye Guo, Lang Tong, Wenchuan Wu, Hongbin Sun
Date: 2021-07-16 00:00:00

We consider some crucial problems related to the secure and reliable operation of power systems with high renewable penetrations: how much reserve should we procure, how should reserve resources distribute among different locations, and how should we price reserve and charge uncertainty sources. These issues have so far been largely addressed empirically. In this paper, we first develop a scenario-oriented energy-reserve co-optimization model, which directly connects reserve procurement with possible outages and load/renewable power fluctuations without the need for empirical reserve requirements. Accordingly, reserve can be optimally procured system-wide to handle all possible future uncertainties with the minimum expected system total cost. Based on the proposed model, marginal pricing approaches are developed for energy and reserve, respectively. Locational uniform pricing is established for energy, and the similar property is also established for the combination of reserve and re-dispatch. In addition, properties of cost recovery for generators and revenue adequacy for the system operator are also proven.
$H_{\infty}$ Inverse Optimal Attitude Tracking on the Special Orthogonal Group $SO(3)$
Authors: Farooq Aslam, M. Farooq Haydar
Date: 2021-07-16 00:00:00

The problem of attitude tracking using rotation matrices is addressed using an approach which combines inverse optimality and $\mathcal{L}_{2}$ disturbance attenuation. Conditions are provided which solve the inverse optimal nonlinear $H_{\infty}$ control problem by minimizing a meaningful cost function. The approach guarantees that the energy gain from an exogenous disturbance to a specified error signal respects a given upper bound. For numerical simulations, a simple problem setup from literature is considered and results demonstrate competitive performance.
Performance Assessment of Linear Models of Hydropower Plants
Authors: Stefano Cassano, Christian Landry, Christophe Nicolet, Fabrizio Sossan
Date: 2021-07-16 00:00:00
Tasks: Model Predictive Control

This paper discusses linearized models of hydropower plants (HPPs). First, it reviews state-of-the-art models and discusses their non-linearities, then it proposes suitable linearization strategies for the plant head, discharge, and turbine torque. It is shown that neglecting the dependency of the hydroacoustic resistance on the discharge leads to a linear formulation of the hydraulic circuits model. For the turbine, a numerical linearization based on a first-order Taylor expansion is proposed. Model performance is evaluated for a medium- and a low-head HPP with a Francis and Kaplan turbine, respectively. Perspective applications of these linear models are in the context of efficient model predictive control of HPPs based on convex optimization.
Physics-augmented models to simulate commercial adaptive cruise control (ACC) systems
Authors: Yinglong He, Marcello Montanino, Konstantinos Mattas, Vincenzo Punzo, Biagio Ciuffo
Date: 2021-07-16 00:00:00

This paper investigates the accuracy and robustness of car-following (CF) and adaptive cruise control (ACC) models used to simulate measured driving behaviour of commercial ACCs. To this aim, a general modelling framework is proposed, in which ACC and CF models have been incrementally augmented with physics extensions; namely, perception delay, linear or nonlinear vehicle dynamics, and acceleration constraints. The framework has been applied to the Intelligent Driver Model (IDM), the Gipps model, and to three basic ACCs. These are a linear controller coupled with a constant time-headway spacing policy and with two other policies derived from the traffic flow theory, which are the IDM desired-distance function and the Gipps equilibrium distance-speed function. The ninety models resulting from the combination of the five base models and the aforementioned physics extensions, have been assessed and compared through a vast calibration and validation experiment against measured trajectory data of low-level automated vehicles. When a single extension has been applied, perception delay and linear dynamics have been the extensions to mostly increase modelling accuracy, whatsoever the base model considered. Concerning models, Gipps-based ones have outperformed all other CF and ACC models in calibration. Even among ACCs, the linear controllers coupled with a Gipps spacing policy have been the best performing. On the other hand, IDM-based models have been by far the most robust in validation, showing almost no crash when calibrated parameters have been used to simulate different trajectories. Overall, the paper shows the importance of cross-fertilization between traffic flow and vehicle studies.
Distributed Value of Information in Feedback Control over Multi-hop Networks
Authors: Precious Ugo Abara, Sandra Hirche
Date: 2021-07-16 00:00:00
Tasks: Scheduling

Recent works in the domain of networked control systems have demonstrated that the joint design of medium access control strategies and control strategies for the closed-loop system is beneficial. However, several metrics introduced so far fail in either appropriately representing the network requirements or in capturing how valuable the data is. In this paper we propose a distributed value of information (dVoI) metric for the joint design of control and schedulers for medium access in a multi-loop system and multi-hop network. We start by providing conditions under certainty equivalent controller is optimal. Then we reformulate the joint control and communication problem as a Bellman-like equation. The corresponding dynamic programming problem is solved in a distributed fashion by the proposed VoI-based scheduling policies for the multi-loop multi-hop networked control system, which outperforms the well-known time-triggered periodic sampling policies. Additionally we show that the dVoI-based scheduling policies are independent of each other, both loop-wise and hop-wise. At last, we illustrate the results with a numerical example.
Coordinating Flexible Demand Response and Renewable Uncertainties for Scheduling of Community Integrated Energy Systems with an Electric Vehicle Charging Station: A Bi-level Approach
Authors: Yang Li, Meng Han, Zhen Yang, Guoqing Li
Date: 2021-07-16 00:00:00
Tasks: Scheduling

A community integrated energy system (CIES) with an electric vehicle charging station (EVCS) provides a new way for tackling growing concerns of energy efficiency and environmental pollution, it is a critical task to coordinate flexible demand response and multiple renewable uncertainties. To this end, a novel bi-level optimal dispatching model for the CIES with an EVCS in multi-stakeholder scenarios is established in this paper. In this model, an integrated demand response program is designed to promote a balance between energy supply and demand while maintaining a user comprehensive satisfaction within an acceptable range. To further tap the potential of demand response through flexibly guiding users' energy consumption and electric vehicles' behaviors (charging, discharging and providing spinning reserves), a dynamic pricing mechanism combining time-of-use and real-time pricing is put forward. In the solution phase, by using sequence operation theory (SOT), the original chance-constrained programming (CCP) model is converted into a readily solvable mixed-integer linear programming (MILP) formulation and finally solved by CPLEX solver. The simulation results on a practical CIES located in North China demonstrate that the presented method manages to balance the interests between CIES and EVCS via the coordination of flexible demand response and uncertain renewables.
A DoE-based approach for the implementation of structural surrogate models in the early stage design of box-wing aircraft
Authors: Vittorio Cipolla, Vincenzo Binante, Karim Abu Salem, Giuseppe Palaia, Davide Zanetti
Date: 2021-07-15 00:00:00

One of the possible ways to face the challenge of reducing the environmental impact of aviation, without limiting the growth of air transport, is the introduction of more efficient, radically different aircraft architectures. Among these, the box-wing one represents a promising solution, at least in the case of its application to short-to-medium haul aircraft, which, according to the achievement of the H2020 project "PARSIFAL", would bring to a 20% reduction in terms of emitted CO2 per passenger-kilometre. The present paper faces the problem of estimating the structural mass of such a disruptive configuration in the early stages of the design, underlining the limitations in this capability of the approaches available by literature and proposing a DoE-based approach to define surrogate models suitable for such purpose. A test case from the project "PARSIFAL" is used for the first conception of the approach, starting from the Finite Element Model parametrization, then followed by the construction of a database of FEM results, hence introducing the regression models and implementing them in an optimization framework. Results achieved are investigated in order to validate both the wing sizing and the optimization procedure. Finally, an additional test case resulting from the application of the box-wing layout to the regional aircraft category within the Italian research project "PROSIB", is briefly presented to further assess the capabilities of the proposed approach.
Metasurface-Enabled On-Chip Multiplexed Diffractive Neural Networks in the Visible
Authors: Xuhao Luo, Yueqiang Hu, Xin Li, Xiangnian Ou, Jiajie Lai, Na Liu, Huigao Duan
Date: 2021-07-13 00:00:00
Tasks: Autonomous Driving

Replacing electrons with photons is a compelling route towards light-speed, highly parallel, and low-power artificial intelligence computing. Recently, all-optical diffractive neural deep neural networks have been demonstrated. However, the existing architectures often comprise bulky components and, most critically, they cannot mimic the human brain for multitasking. Here, we demonstrate a multi-skilled diffractive neural network based on a metasurface device, which can perform on-chip multi-channel sensing and multitasking at the speed of light in the visible. The metasurface is integrated with a complementary metal oxide semiconductor imaging sensor. Polarization multiplexing scheme of the subwavelength nanostructures are applied to construct a multi-channel classifier framework for simultaneous recognition of digital and fashionable items. The areal density of the artificial neurons can reach up to 6.25x106/mm2 multiplied by the number of channels. Our platform provides an integrated solution with all-optical on-chip sensing and computing for applications in machine vision, autonomous driving, and precision medicine.
Eco-evolutionary dynamics of cooperation in the presence of policing
Authors: Sayantan Nag Chowdhury, Srilena Kundu, Jeet Banerjee, Matjaž Perc, Dibakar Ghosh
Date: 2021-07-15 00:00:00

Ecology and evolution are inherently linked, and studying a mathematical model that considers both holds promise of insightful discoveries related to the dynamics of cooperation. In the present article, we use the prisoner's dilemma (PD) game as a basis for long-term apprehension of the essential social dilemma related to cooperation among unrelated individuals. We upgrade the contemporary PD game with an inclusion of evolution-induced act of punishment as a third competing strategy in addition to the traditional cooperators and defectors. In a population structure, the abundance of ecologically-viable free space often regulates the reproductive opportunities of the constituents. Hence, additionally, we consider the availability of free space as an ecological footprint, thus arriving at a simple eco-evolutionary model, which displays fascinating complex dynamics. As possible outcomes, we report the individual dominance of cooperators and defectors as well as a plethora of mixed states, where different strategies coexist followed by maintaining the diversity in a socio-ecological framework. These states can either be steady or oscillating, whereby oscillations are sustained by cyclic dominance among different combinations of cooperators, defectors, and punishers. We also observe a novel route to cyclic dominance where cooperators, punishers, and defectors enter a coexistence via an inverse Hopf bifurcation that is followed by an inverse period doubling route.
Turbulence-immune computational ghost imaging based on a multi-scale generative adversarial network
Authors: Hao Zhang, Deyang Duan
Date: 2021-07-14 00:00:00
Tasks: Generative Adversarial Network

There is a consensus that turbulence-free images cannot be obtained by conventional computational ghost imaging (CGI) because the CGI is only a classic simulation, which does not satisfy the conditions of turbulence-free imaging. In this article, we first report a turbulence-immune CGI method based on a multi-scale generative adversarial network (MsGAN). Here, the conventional CGI framework is not changed, but the conventional CGI coincidence measurement algorithm is optimized by an MsGAN. Thus, the satisfactory turbulence-free ghost image can be reconstructed by training the network, and the visual effect can be significantly improved.
Flexible Covariate Adjustments in Regression Discontinuity Designs
Authors: Claudia Noack, Tomasz Olma, Christoph Rothe
Date: 2021-07-16 00:00:00
Tasks: regression

Empirical regression discontinuity (RD) studies often include covariates in their specifications to increase the precision of their estimates. In this paper, we propose a novel class of estimators that use such covariate information more efficiently than existing methods and can accommodate many covariates. Our estimators are simple to implement and involve running a standard RD analysis after subtracting a function of the covariates from the original outcome variable. We characterize the function of the covariates that minimizes the asymptotic variance of these estimators. We also show that the conventional RD framework gives rise to a special robustness property which implies that the optimal adjustment function can be estimated flexibly via modern machine learning techniques without affecting the first-order properties of the final RD estimator. We demonstrate our methods' scope for efficiency improvements by reanalyzing data from a large number of recently published empirical studies.
Bias optimal vol-of-vol estimation: the role of window overlapping
Authors: Giacomo Toscano, Maria Cristina Recchioni
Date: 2020-04-08 00:00:00

We derive a feasible criterion for the bias-optimal selection of the tuning parameters involved in estimating the integrated volatility of the spot volatility via the simple realized estimator by Barndorff-Nielsen and Veraart (2009). Our analytic results are obtained assuming that the spot volatility is a continuous mean-reverting process and that consecutive local windows for estimating the spot volatility are allowed to overlap in a finite sample setting. Moreover, our analytic results support some optimal selections of tuning parameters prescribed in the literature, based on numerical evidence. Interestingly, it emerges that window-overlapping is crucial for optimizing the finite-sample bias of volatility-of-volatility estimates.
Subspace Shrinkage in Conjugate Bayesian Vector Autoregressions
Authors: Florian Huber, Gary Koop
Date: 2021-07-16 00:00:00

Macroeconomists using large datasets often face the choice of working with either a large Vector Autoregression (VAR) or a factor model. In this paper, we develop methods for combining the two using a subspace shrinkage prior. Subspace priors shrink towards a class of functions rather than directly forcing the parameters of a model towards some pre-specified location. We develop a conjugate VAR prior which shrinks towards the subspace which is defined by a factor model. Our approach allows for estimating the strength of the shrinkage as well as the number of factors. After establishing the theoretical properties of our proposed prior, we carry out simulations and apply it to US macroeconomic data. Using simulations we show that our framework successfully detects the number of factors. In a forecasting exercise involving a large macroeconomic data set we find that combining VARs with factor models using our prior can lead to forecast improvements.
Key features of administrative responsibility
Authors: Vladimir Zhavoronkov, Valeri Lipunov, Mattia Masolletti
Date: 2021-07-16 00:00:00

The article examines both the legal responsibility itself and its types, and in various aspects. The authors apply legal analysis, as well as the principles of consistency and integrity. The contradictions of administrative responsibility, as well as legal gaps in its interpretation, are highlighted.
Monotone Comparative Statics in the Calvert-Wittman Model
Authors: Francisco Rodríguez, Eduardo Zambrano
Date: 2021-07-16 00:00:00

In this paper, we show that when policy-motivated parties can commit to a particular platform during a uni-dimensional electoral contest where valence issues do not arise there must be a positive association between the policies preferred by candidates and the policies adopted in expectation in the lowest and the highest equilibria of the electoral contest. We also show that this need not be so if the parties cannot commit to a particular policy. The implication is that evidence of a negative relationship between enacted and preferred policies is suggestive of parties that hold positions from which they would like to move from yet are unable to do so.
Toward Streaming ASR with Non-Autoregressive Insertion-based Model
Authors: Yuya Fujita, Tianzi Wang, Shinji Watanabe, Motoi Omachi
Date: 2020-12-18 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Segmentation, speech-recognition, Speech Recognition

Neural end-to-end (E2E) models have become a promising technique to realize practical automatic speech recognition (ASR) systems. When realizing such a system, one important issue is the segmentation of audio to deal with streaming input or long recording. After audio segmentation, the ASR model with a small real-time factor (RTF) is preferable because the latency of the system can be faster. Recently, E2E ASR based on non-autoregressive models becomes a promising approach since it can decode an $N$-length token sequence with less than $N$ iterations. We propose a system to concatenate audio segmentation and non-autoregressive ASR to realize high accuracy and low RTF ASR. As a non-autoregressive ASR, the insertion-based model is used. In addition, instead of concatenating separated models for segmentation and ASR, we introduce a new architecture that realizes audio segmentation and non-autoregressive ASR by a single neural network. Experimental results on Japanese and English dataset show that the method achieved a reasonable trade-off between accuracy and RTF compared with baseline autoregressive Transformer and connectionist temporal classification.
Avoiding normalization uncertainties in deep learning architectures for end-to-end communication
Authors: Simon Bos, Evgenii Vinogradov, Sofie Pollin
Date: 2021-07-16 00:00:00

Recently, deep learning is considered to optimize the end-to-end performance of digital communication systems. The promise of learning a digital communication scheme from data is attractive, since this makes the scheme adaptable and precisely tunable to many scenarios and channel models. In this paper, we analyse a widely used neural network architecture and show that the training of the end-to-end architecture suffers from normalization errors introduced by an average power constraint. To solve this issue, we propose a modified architecture: shifting the batch slicing after the normalization layer. This approach meets the normalization constraints better, especially in the case of small batch sizes. Finally, we experimentally demonstrate that our modified architecture leads to significantly improved performance of trained models, even for large batch sizes where normalization constraints are more easily met.
Band & Tone Jamming Analysis and Detection on LoRa signals
Authors: Clément Demeslay, Roland Gautier, Anthony Fiche, Gilles Burel
Date: 2021-07-16 00:00:00

This paper examines the effect of Band Jamming (BJ) and Tone Jamming (TJ) on LoRa signals in a flat Additive White Gaussian Noise (AWGN) channel. In this scenario, LoRa proves to have good resiliency against these jamming attacks. Furthermore, a simple and lightweight BJ and TJ jammer detection scheme is derived. Theoretical and simulation results show good detection capability, especially with Single Tone Jamming (STJ).
Towards Multi-Functional 6G Wireless Networks: Integrating Sensing, Communication and Security
Authors: Zhongxiang Wei, Fan Liu, Christos Masouros, Nanchi Su, Athina P. Petropulu
Date: 2021-07-16 00:00:00
Tasks: Integrated sensing and communication, ISAC

Integrated sensing and communication (ISAC) has recently emerged as a candidate 6G technology, aiming to unify the two key operations of the future network in spectrum/energy/cost efficient way. ISAC involves communicating information to receivers and simultaneously sensing targets, while both operations use the same waveforms, the same transmitter and ultimately the same network infrastructure. Nevertheless, the inclusion of information signalling into the probing waveform for target sensing raises unique and difficult challenges from the perspective of information security. At the same time, the sensing capability incorporated in the ISAC transmission offers unique opportunities to design secure ISAC techniques. This overview paper discusses these unique challenges and opportunities for the next generation of ISAC networks. We first briefly discuss the fundamentals of waveform design for sensing and communication. Then, we detail the challenges and contradictory objectives involved in securing ISAC transmission, along with state-of-the-art approaches to address them. We then identify the new opportunity of using the sensing capability to obtain knowledge of the targets, as an enabling approach against known weaknesses of PHY security. Finally, we illustrate a low-cost secure ISAC architecture, followed by a series of open research topics. This family of sensing-aided secure ISAC techniques brings a new insight on providing information security, with an eye on robust and hardware-constrained designs tailored for low-cost ISAC devices.
Cooperative Multi-Modal Localization in Connected and Autonomous Vehicles
Authors: Nikos Piperigkos, Aris S. Lalos, Kostas Berberidis, Christos Anagnostopoulos
Date: 2021-07-16 00:00:00
Tasks: Autonomous Vehicles, Position

Cooperative Localization is expected to play a crucial role in various applications in the field of Connected and Autonomous vehicles (CAVs). Future 5G wireless systems are expected to enable cost-effective Vehicle-to-Everything (V2X)systems, allowing CAVs to share with the other entities of the network the data they collect and measure. Typical measurement models usually deployed for this problem, are absolute position from Global Positioning System (GPS), relative distance and azimuth angle to neighbouring vehicles, extracted from Light Detection and Ranging (LIDAR) or Radio Detection and Ranging(RADAR) sensors. In this paper, we provide a cooperative localization approach that performs multi modal-fusion between the interconnected vehicles, by representing a fleet of connected cars as an undirected graph, encoding each vehicle position relative to its neighbouring vehicles. This method is based on:i) the Laplacian Processing, a Graph Signal Processing tool that allows to capture intrinsic geometry of the undirected graph of vehicles rather than their absolute position on global coordinate system and ii) the temporal coherence due to motion patterns of the moving vehicles.