pwc-archive/papers-with-abstracts huggingface dataset:
Interpreting End-to-End Deep Learning Models for Speech Source Localization Using Layer-wise Relevance Propagation
Authors: Luca Comanducci, Fabio Antonacci, Augusto Sarti
Date: 2024-04-04 00:00:00
Tasks: Explainable artificial intelligence, Explainable Artificial Intelligence (XAI)

Deep learning models are widely applied in the signal processing community, yet their inner working procedure is often treated as a black box. In this paper, we investigate the use of eXplainable Artificial Intelligence (XAI) techniques to learning-based end-to-end speech source localization models. We consider the Layer-wise Relevance Propagation (LRP) technique, which aims to determine which parts of the input are more important for the output prediction. Using LRP we analyze two state-of-the-art models, of differing architectural complexity that map audio signals acquired by the microphones to the cartesian coordinates of the source. Specifically, we inspect the relevance associated with the input features of the two models and discover that both networks denoise and de-reverberate the microphone signals to compute more accurate statistical correlations between them and consequently localize the sources. To further demonstrate this fact, we estimate the Time-Difference of Arrivals (TDoAs) via the Generalized Cross Correlation with Phase Transform (GCC-PHAT) using both microphone signals and relevance signals extracted from the two networks and show that through the latter we obtain more accurate time-delay estimation results.
CLaM-TTS: Improving Neural Codec Language Model for Zero-Shot Text-to-Speech
Authors: Jaehyeon Kim, Keon Lee, Seungjun Chung, Jaewoong Cho
Date: 2024-04-03 00:00:00
Tasks: Language Modeling, Language Modelling, Quantization, text-to-speech, Text to Speech

With the emergence of neural audio codecs, which encode multiple streams of discrete tokens from audio, large language models have recently gained attention as a promising approach for zero-shot Text-to-Speech (TTS) synthesis. Despite the ongoing rush towards scaling paradigms, audio tokenization ironically amplifies the scalability challenge, stemming from its long sequence length and the complexity of modelling the multiple sequences. To mitigate these issues, we present CLaM-TTS that employs a probabilistic residual vector quantization to (1) achieve superior compression in the token length, and (2) allow a language model to generate multiple tokens at once, thereby eliminating the need for cascaded modeling to handle the number of token streams. Our experimental results demonstrate that CLaM-TTS is better than or comparable to state-of-the-art neural codec-based TTS models regarding naturalness, intelligibility, speaker similarity, and inference speed. In addition, we examine the impact of the pretraining extent of the language models and their text tokenization strategies on performances.
Setpoint control of bilinear systems from noisy data
Authors: Andrea Bisoffi, Dominiek M. Steeman, Claudio De Persis
Date: 2024-04-04 00:00:00

We consider the problem of designing a controller for an unknown bilinear system using only noisy input-states data points generated by it. The controller should achieve regulation to a given state setpoint and provide a guaranteed basin of attraction. Determining the equilibrium input to achieve that setpoint is not trivial in a data-based setting and we propose the design of a controller in two scenarios. The design takes the form of linear matrix inequalities and is validated numerically for a Cuk converter.
AMC-backed Twin Arrow Antenna for Wearable Electronic Travel Aid System at 24 GHz
Authors: Alicia Flórez Berdasco, María Elena de Cos Gómez, Jaime Laviada, Fernando Las-Heras
Date: 2024-04-04 00:00:00

An ultra-compact wearable antenna, for electronic travel aid (ETA) applications, is presented. An AMC-backed twin arrow antenna, operative in the 24.05-24.25 GHz frequency band, has been designed for imaging systems supporting ETA. Artificial Magnetic Conductor (AMC) is combined with the antenna with the aim of reducing the backward radiation to the wearing person while improving its radiation properties and bandwidth, all this without increasing the initial area of the antenna. Prototypes of the AMC-antenna have been fabricated and measured. In order to test its performance for the application, imaging have been conducted by means of synthetic aperture radar (SAR) techniques by placing the antenna in the arm of a user to take advantage of natural body movement. Electromagnetic images have been obtained and the target has been identified, demonstrating the suitability of the AMC-antenna for the ETA system.
Analytical Characterization of Epileptic Dynamics in a Bistable System
Authors: Yuzhen Qin, Ahmed El-Gazzar, Danielle S. Bassett, Fabio Pasqualetti, Marcel van Gerven
Date: 2024-04-04 00:00:00

Epilepsy is one of the most common neurological disorders globally, affecting millions of individuals. Despite significant advancements, the precise mechanisms underlying this condition remain largely unknown, making accurately predicting and preventing epileptic seizures challenging. In this paper, we employ a bistable model, where a stable equilibrium and a stable limit cycle coexist, to describe epileptic dynamics. The equilibrium captures normal steady-state neural activity, while the stable limit cycle signifies seizure-like oscillations. The noise-driven switch from the equilibrium to the limit cycle characterizes the onset of seizures. The differences in the regions of attraction of these two stable states distinguish epileptic brain dynamics from healthy ones. We analytically construct the regions of attraction for both states. Further, using the notion of input-to-state stability, we theoretically show how the regions of attraction influence the stability of the system subject to external perturbations. Generalizing the bistable system into coupled networks, we also find the role of network parameters in shaping the regions of attraction. Our findings shed light on the intricate interplay between brain networks and epileptic activity, offering mechanistic insights into potential avenues for more predictable treatments.
Implementation of complex-valued sliding mode controllers in three-phase power converters
Authors: Arnau Dòria-Cerezo, Pau Boira, Víctor Repecho, Domingo Biel
Date: 2024-04-04 00:00:00

This paper presents two methods for implementing complex-valued sliding mode controllers in three-phase power converters. The paper includes the description of the algorithms and a detailed analysis of the proposed implementations. The methods, that are easy to code and have a low computational burden, retain the sliding mode properties of robustness and fast response and do not require any additional processing often used to decouple the dynamics of the three-phase system. The performance of the methods is compared in numerical simulations, and the algorithms are experimentally tested in a microcontroller using a Hardware-in-the-Loop platform.
Formal Verification of Linear Temporal Logic Specifications Using Hybrid Zonotope-Based Reachability Analysis
Authors: Loizos Hadjiloizou, Frank J. Jiang, Amr Alanwar, Karl H. Johansson
Date: 2024-04-04 00:00:00

In this paper, we introduce a hybrid zonotope-based approach for formally verifying the behavior of autonomous systems operating under Linear Temporal Logic (LTL) specifications. In particular, we formally verify the LTL formula by constructing temporal logic trees (TLT)s via backward reachability analysis (BRA). In previous works, TLTs are predominantly constructed with either highly general and computationally intensive level set-based BRA or simplistic and computationally efficient polytope-based BRA. In this work, we instead propose the construction of TLTs using hybrid zonotope-based BRA. By using hybrid zonotopes, we show that we are able to formally verify LTL specifications in a computationally efficient manner while still being able to represent complex geometries that are often present when deploying autonomous systems, such as non-convex, disjoint sets. Moreover, we evaluate our approach on a parking example, providing preliminary indications of how hybrid zonotopes facilitate computationally efficient formal verification of LTL specifications in environments that naturally lead to non-convex, disjoint geometries.
Formulating the Restoration of Distribution Networks as a Multiple Traveling Salesman Problem
Authors: Ran Wei, Arindam K. Das, Payman Arabshahi, Daniel S. Kirschen
Date: 2024-04-04 00:00:00
Tasks: Scheduling, Traveling Salesman Problem

Severe weather events can cause extensive damage to electrical distribution networks, requiring a multi-day restoration effort. Optimizing the dispatch of repair crews minimizes the severe socio-economic consequences of such events. Considering both repair times and travel times, we use graphical manipulations to transform this multiple crew scheduling problem into a type of traveling salesman problem(TSP). Specifically, we demonstrate that the restoration problem bears major resemblance to an instance of a cost constrained reward maximizing mTSP (multiple TSP) on node and edge weighted (doubly weighted) graphs (a variant we dub the CCRM-mTSP-DW), where the objective is to maximize the aggregate reward earned during the upcoming restoration window, provided no crew violates its time budget and electrical continuity constraints are met. Despite the rich history of research on the TSP and its variants, this CCRM-mTSP-DW variant has not been studied before, although its closest cousin happens to be the "Selective TSP" (S-TSP). This reinterpretation of the restoration problem not only opens up the possibility of drawing on existing solution methods developed for the TSP and its variants, it also adds a new chapter in the annals of research on "TSP-like'' problems. In this paper, we propose a "TSP-like'' mixed integer linear programming (MILP) model for solving the restoration problem and validate it on the IEEE PES 123-node test feeder network.
PowerSimulations.jl -- A Power Systems operations simulation Library
Authors: Jose Daniel Lara, Clayton Barrows, Daniel Thom, Sourabh Dalvi, Duncan S. Callaway, Dheepak Krishnamurthy
Date: 2024-04-03 00:00:00
Tasks: Time Series

PowerSimulations.jl is a Julia-based BSD-licensed power system operations simulation tool developed as a flexible and open source software for quasi-static power systems simulations including Production Cost Models. PowerSimulations.jl tackles the issues of developing a simulation model in a modular way providing tools for the formulation of decision models and emulation models that can be solved independently or in an interconnected fashion. This paper discusses the software implementation of PowerSimulations.jl as a template for the development and implementation of operation simulators, providing solutions to commonly encountered issues like time series read/write and results sharing between models. The paper includes a publicly-available validation of classical operations simulations as well as examples of the advanced features of the software.
Impact and Integration of Mini Photovoltaic Systems on Electric Power Distribution Grids
Authors: Gökhan Demirel, Simon Grafenhorst, Kevin Förderer, Veit Hagenmeyer
Date: 2024-04-03 00:00:00

This work analyzes the impact of varying concentrations mini-photovoltaic (MPV) systems, often referred to as balcony power plants, on the stability and control of the low-voltage (LV) grid. By local energy use and potentially reversing meter operation, we focus on how these MPV systems transform grid dynamics and elucidate consumer participation in the energy transition. We scrutinize the effects of these systems on power quality, power loss, transformer loading, and the functioning of other inverter-based voltage-regulating distributed energy resources (DER). Owing to the rise in renewable output from MPVs, the emerging bidirectional energy flow poses challenges for distribution grids abundant with DERs. Our case studies, featuring sensitivity analysis and comparison of distributed and decentralized DER control strategies, highlight that autonomous inverters are essential for providing ancillary services. With the growing use of battery energy storage (BES) systems in LV grids for these services, the need for adaptable DER control strategies becomes increasingly evident.
Extending direct data-driven predictive control towards systems with finite control sets
Authors: Manuel Klädtke, Moritz Schulze Darup, Daniel E. Quevedo
Date: 2024-04-03 00:00:00
Tasks: Model Predictive Control

Although classical model predictive control with finite control sets (FCS-MPC) is quite a popular control method, particularly in the realm of power electronics systems, its direct data-driven predictive control (FCS-DPC) counterpart has received relatively limited attention. In this paper, we introduce a novel reformulation of a commonly used DPC scheme that allows for the application of a modified sphere decoding algorithm, known for its efficiency and prominence in FCS-MPC applications. We test the reformulation on a popular electrical drive example and compare the computation times of sphere decoding FCS-DPC with an enumeration-based and a MIQP method.
Towards a unifying framework for data-driven predictive control with quadratic regularization
Authors: Manuel Klädtke, Moritz Schulze Darup
Date: 2024-04-03 00:00:00
Tasks: Model Predictive Control

Data-driven predictive control (DPC) has recently gained popularity as an alternative to model predictive control (MPC). Amidst the surge in proposed DPC frameworks, upon closer inspection, many of these frameworks are more closely related (or perhaps even equivalent) to each other than it may first appear. We argue for a more formal characterization of these relationships so that results can be freely transferred from one framework to another, rather than being uniquely attributed to a particular framework. We demonstrate this idea by examining the connection between $\gamma$-DDPC and the original DeePC formulation.
Stabilizing switched nonlinear systems under restricted but arbitrary switching signals
Authors: Atreyee Kundu
Date: 2024-04-03 00:00:00

This paper deals with input/output-to-state stability (IOSS) of switched nonlinear systems whose switching signals obey pre-specified restrictions on admissible switches between the subsystems and admissible dwell times on the subsystems. We present sufficient conditions on the subsystems, admissible switches between them and admissible dwell times on them, such that a switched system generated under all switching signals obeying the given restrictions is IOSS. Multiple Lyapunov-like functions and graph theory are the key apparatuses for our analysis. A numerical example is presented to demonstrate our results.
Learning with errors based dynamic encryption that discloses residue signal for anomaly detection
Authors: Yeongjun Jang, Joowon Lee, Junsoo Kim, Hyungbo Shim
Date: 2024-04-03 00:00:00
Tasks: Anomaly Detection

Anomaly detection is a protocol that detects integrity attacks on control systems by comparing the residue signal with a threshold. Implementing anomaly detection on encrypted control systems has been a challenge because it is hard to detect an anomaly from the encrypted residue signal without the secret key. In this paper, we propose a dynamic encryption scheme for a linear system that automatically discloses the residue signal. The initial state and the input are encrypted based on the zero-dynamics of the system, so that the effect of encryption on the residue signal remains identically zero. The proposed scheme is shown to be secure in the sense that no other information than the residue signal is disclosed. Furthermore, we demonstrate a method of utilizing the disclosed residue signal to operate an observer-based controller over encrypted data for an infinite time horizon without re-encryption.
On future power system digital twins: A vision towards a standard architecture
Authors: Wouter Zomerdijk, Peter Palensky, Tarek Alskaif, Pedro P. Vergara
Date: 2024-04-03 00:00:00

The energy sector's digital transformation brings mutually dependent communication and energy infrastructure, tightening the relationship between the physical and the digital world. Digital twins (DT) are the key concept for this. This paper initially discusses the evolution of the DT concept across various engineering applications before narrowing its focus to the power systems domain. By reviewing different definitions and applications, the authors present a new definition of DTs specifically tailored to power systems. Based on the proposed definition and extensive deliberations and consultations with distribution system operators, energy traders, and municipalities, the authors introduce a vision of a standard DT ecosystem architecture that offers services beyond real-time updates and can seamlessly integrate with existing transmission and distribution system operators' processes while reconciling with concepts such as microgrids and local energy communities based on a system-of-systems view. The authors also discuss their vision related to the integration of power system DTs into various phases of the system's life cycle, such as long-term planning, emphasising challenges that remain to be addressed, such as managing measurement and model errors, and uncertainty propagation. Finally, the authors present their vision of how artificial intelligence and machine learning can enhance several power systems DT modules established in the proposed architecture.
Network-Aware and Welfare-Maximizing Dynamic Pricing for Energy Sharing
Authors: Ahmed S. Alahmed, Guido Cavraro, Andrey Bernstein, Lang Tong
Date: 2024-04-03 00:00:00

The proliferation of behind-the-meter (BTM) distributed energy resources (DER) within the electrical distribution network presents significant supply and demand flexibilities, but also introduces operational challenges such as voltage spikes and reverse power flows. In response, this paper proposes a network-aware dynamic pricing framework tailored for energy-sharing coalitions that aggregate small, but ubiquitous, BTM DER downstream of a distribution system operator's (DSO) revenue meter that adopts a generic net energy metering (NEM) tariff. By formulating a Stackelberg game between the energy-sharing market leader and its prosumers, we show that the dynamic pricing policy induces the prosumers toward a network-safe operation and decentrally maximizes the energy-sharing social welfare. The dynamic pricing mechanism involves a combination of a locational ex-ante dynamic price and an ex-post allocation, both of which are functions of the energy sharing's BTM DER. The ex-post allocation is proportionate to the price differential between the DSO NEM price and the energy-sharing locational price. Simulation results using real DER data and the IEEE 13-bus test systems illustrate the dynamic nature of network-aware pricing at each bus, and its impact on voltage.
Detection and Mitigation of Cyberattacks on Volt-Var Control
Authors: Milad Beikbabaei, Ali Mehrizi-Sani
Date: 2024-04-03 00:00:00

Cyberattacks are becoming more frequent, and attackers can use different mechanisms, such as denial of service (DoS) and false data injection (FDI). Furthermore, multiple attack types can be launched simultaneously, known as hybrid attacks, to cause more damage. Volt-Var control algorithms are widely used in the distribution system to maintain the voltage within a nominal range. This work uses an artificial neural network (ANN)-based method to detect and mitigate hybrid cyberattacks on the Volt-VAr control algorithm.
Observability of Nonlinear Dynamical Systems over Finite Fields
Authors: Ramachandran Anantharaman, Virendra Sule
Date: 2024-04-02 00:00:00

This paper discusses the observability of nonlinear Dynamical Systems over Finite Fields (DSFF) through the Koopman operator framework. In this work, given a nonlinear DSFF, we construct a linear system of the smallest dimension, called the Linear Output Realization (LOR), which can generate all the output sequences of the original nonlinear system through proper choices of initial conditions (of the associated LOR). We provide necessary and sufficient conditions for the observability of a nonlinear system and establish that the maximum number of outputs sufficient for computing the initial condition is precisely equal to the dimension of the LOR. Further, when the system is not known to be observable, we provide necessary and sufficient conditions for the unique reconstruction of initial conditions for specific output sequences.
Advanced Algorithms for Autonomous Guidance of Solar-powered UAVs
Authors: Siyuan Li
Date: 2024-03-28 00:00:00

Unmanned aerial vehicle (UAV) techniques have developed rapidly within the past few decades. Using UAVs provides benefits in numerous applications such as site surveying, communication systems, parcel delivery, target tracking, etc. The high manoeuvrability of the drone and its ability to replace a certain amount of labour cost are the reasons why it can be widely chosen. There will be more applications of UAVs if they can have longer flight time, which is a very challenging hurdle because of the energy constraint of the onboard battery. One promising solution is to equip UAVs with some lightweight solar panels to maximize flight time. Therefore, more research is needed for solar-powered UAVs (SUAVs) in different environments.
RNA Dynamics from Experimental and Computational Approaches
Authors: Giovanni Bussi, Massimiliano Bonomi, Paraskevi Gkeka, Michael Sattler, Hashim M. Al-Hashimi, Pascal Auffinger, Maria Duca, Yann Foricher, Danny Incarnato, Alisha N. Jones, Serdal Kirmizialtin, Miroslav Krepl, Modesto Orozco, Giulia Palermo, Samuela Pasquali, Loïc Salmon, Harald Schwalbe, Eric Westhof, Martin Zacharias
Date: 2024-04-03 00:00:00

Ribonucleic acids (RNA) are unique in that they can store genetic information, replicate and perform catalysis. Importantly, RNA molecules are highly dynamic, and thus determining the ensemble of conformations that they populate is crucial not only to elucidate their biological functions, but also for their potential use as therapeutic targets. Computational and experimental techniques provide complementary views on RNA dynamics, and their integration is fundamental to improve the accuracy of computations and the resolution of experiments. Recent exciting developments in this field, were discussed at the CECAM workshop ``RNA dynamics from experimental and computational approaches'', in Paris, June 26-28, 2023. This report outlines key `take-home' messages that emerged during this workshop from the presentations and discussions.