pwc-archive/papers-with-abstracts huggingface dataset:
Self-Distillation Prototypes Network: Learning Robust Speaker Representations without Supervision
Authors: Yafeng Chen, Siqi Zheng, Hui Wang, Luyao Cheng, Qian Chen, Chong Deng, Shiliang Zhang, Wen Wang
Date: 2023-08-05 00:00:00
Tasks: Diversity, Representation Learning, Speaker Verification, Transfer Learning

Training speaker-discriminative and robust speaker verification systems without explicit speaker labels remains a persistent challenge. In this paper, we propose a novel self-supervised speaker verification approach, Self-Distillation Prototypes Network (SDPN), which effectively facilitates self-supervised speaker representation learning. SDPN assigns the representation of the augmented views of an utterance to the same prototypes as the representation of the original view, thereby enabling effective knowledge transfer between the augmented and original views. Due to lack of negative pairs in the SDPN training process, the network tends to align positive pairs quite closely in the embedding space, a phenomenon known as model collapse. To mitigate this problem, we introduce a diversity regularization term to embeddings in SDPN. Comprehensive experiments on the VoxCeleb datasets demonstrate the superiority of SDPN among self-supervised speaker verification approaches. SDPN sets a new state-of-the-art on the VoxCeleb1 speaker verification evaluation benchmark, achieving Equal Error Rate 1.80%, 1.99%, and 3.62% for trial VoxCeleb1-O, VoxCeleb1-E and VoxCeleb1-H, without using any speaker labels in training. Ablation studies show that both proposed learnable prototypes in self-distillation network and diversity regularization contribute to the verification performance.
MyVoice: Arabic Speech Resource Collaboration Platform
Authors: Yousseif Elshahawy, Yassine El Kheir, Shammur Absar Chowdhury, Ahmed Ali
Date: 2023-07-23 00:00:00

We introduce MyVoice, a crowdsourcing platform designed to collect Arabic speech to enhance dialectal speech technologies. This platform offers an opportunity to design large dialectal speech datasets; and makes them publicly available. MyVoice allows contributors to select city/country-level fine-grained dialect and record the displayed utterances. Users can switch roles between contributors and annotators. The platform incorporates a quality assurance system that filters out low-quality and spurious recordings before sending them for validation. During the validation phase, contributors can assess the quality of recordings, annotate them, and provide feedback which is then reviewed by administrators. Furthermore, the platform offers flexibility to admin roles to add new data or tasks beyond dialectal speech and word collection, which are displayed to contributors. Thus, enabling collaborative efforts in gathering diverse and large Arabic speech data.
Adaptive robust tracking control with active learning for linear systems with ellipsoidal bounded uncertainties
Authors: Xuehui Ma, Shiliang Zhang, Yushuai Li, Fucai Qian, TingWen Huang
Date: 2023-08-07 00:00:00
Tasks: Active Learning, State Estimation

This paper is concerned with the robust tracking control of linear uncertain systems, whose unknown system parameters and disturbances are bounded within ellipsoidal sets. We propose an adaptive robust control that can actively learn the ellipsoid sets. Particularly, the proposed approach utilizes the recursive set-membership state estimation in learning the ellipsoidal sets, aiming at mitigating uncertainties in the system control. Upon the learned sets representing the recognized uncertainties, we construct a robust control with one-step prediction for system output tracking. In deriving an optimized control law, we reformulate the optimization objective into a second-order cone programming problem that can be solved in a computationally friendly way. To further stimulate the active learning of uncertainties over the control procedures, we enrich the information used for the learning by maximizing the volume of the ellipsoid set, supposed to lead to increased learning accuracy and accelerated uncertainty reduction. To verify our approach, we conduct numerical simulations to compare the fixed-ellipsoidal-set robust control with ours, and investigate the positive effect of the designed active learning in the uncertain system control process.
Learning-based Parameter Optimization for a Class of Orbital Tracking Control Laws
Authors: Gianni Bianchini, Andrea Garulli, Antonio Giannitrapani, Mirko Leomanni, Renato Quartullo
Date: 2023-08-07 00:00:00

This paper presents a machine learning approach for tuning the parameters of a family of stabilizing controllers for orbital tracking. An augmented random search algorithm is deployed, which aims at minimizing a cost function combining convergence time and fuel consumption. The main feature of the proposed learning strategy is that closed-loop stability is always guaranteed during the exploration of the parameter space, {a property that allows one to streamline the training process by restricting the search domain to well-behaved control policies.} The proposed approach is tested on two case studies: an orbital transfer and a rendezvous and docking mission. It is shown that in both cases the learned control parameters lead to a significant improvement of the considered performance measure.
A general method for estimating zonal transmission interface limits from nodal network data
Authors: Patrick R. Brown, Clayton P. Barrows, Jarrad G. Wright, Gregory L. Brinkman, Sourabh Dalvi, Jiazi Zhang, Trieu Mai
Date: 2023-08-07 00:00:00

Capacity expansion models for the electric power system often employ zonal (rather than nodal) resolution, necessitating estimates of aggregate power transfer limits across the interfaces between model zones. Interface limits between planning areas are sometimes published, but they are not generalizable to arbitrary zone shapes. There is thus a need for a reproducible method for estimating interface transfer limits (ITLs) between user-defined zones directly from nodal transmission system data. Here, we present a simple method for estimating ITLs using a DC power flow approximation via the power transfer distribution factor (PTDF) matrix. Linear optimization is performed to identify the distribution of power flows that maximizes the total flow on interface-crossing lines, subject to individual line ratings, limits on bus injection/withdrawal, and the relationships among flows, injections, and withdrawals imposed by the PTDF matrix. We demonstrate the application of the method on a 134-zone ~65000-bus system, and we explore the influence of flow direction, contingency level, and zone size on the estimated ITLs. There is significant heterogeneity in the ratio of the ITL to the sum of interface-crossing line ratings, which highlights the importance of accounting for the physical constraints on power flows imposed by Kirchhoff's laws when estimating zonal ITLs.
On Data-Driven Modeling and Control in Modern Power Grids Stability: Survey and Perspective
Authors: Xun Gong, Xiaozhe Wang, Bo Cao
Date: 2023-08-07 00:00:00

Modern power grids are fast evolving with the increasing volatile renewable generation, distributed energy resources (DERs) and time-varying operating conditions. The DERs include rooftop photovoltaic (PV), small wind turbines, energy storages, flexible loads, electric vehicles (EVs), etc. The grid control is confronted with low inertia, uncertainty and nonlinearity that challenge the operation security, efficacy and efficiency. The ongoing digitization of power grids provides opportunities to address the challenges with data-driven and control. This paper provides a comprehensive review of emerging data-driven dynamical modeling and control methods and their various applications in power grid. Future trends are also discussed based on advances in data-driven control.
Data-driven robust MPC of tiltwing VTOL aircraft
Authors: Martin Doff-Sotta, Mark Cannon, Marko Bacic
Date: 2023-08-07 00:00:00
Tasks: Model Predictive Control

This paper investigates robust tube-based Model Predictive Control (MPC) of a tiltwing Vertical Take-Off and Landing (VTOL) aircraft subject to wind disturbances and model uncertainty. Our approach is based on a Difference of Convex (DC) function decomposition of the dynamics to develop a computationally tractable optimisation with robust tubes for the system trajectories. We consider a case study of a VTOL aircraft subject to wind gusts and whose aerodynamics is defined from data.
A Safe DRL Method for Fast Solution of Real-Time Optimal Power Flow
Authors: Pengfei Wu, Chen Chen, Dexiang Lai, Jian Zhong
Date: 2023-08-07 00:00:00
Tasks: Deep Reinforcement Learning

High-level penetration of intermittent renewable energy sources (RESs) has introduced significant uncertainties into modern power systems. In order to rapidly and economically respond to the fluctuations of power system operating state, this paper proposes a safe deep reinforcement learning (SDRL) based method for fast solution of real-time optimal power flow (RT-OPF) problems. The proposed method considers the volatility of RESs and temporal constraints, and formulates the RT-OPF as a Constrained Markov Decision Process (CMDP). In the training process, the proposed method hybridizes the proximal policy optimization (PPO) and the primal-dual method. Instead of integrating the constraint violation penalty with the reward function, its actor gradients are estimated by a Lagrange advantage function which is derived from two critic systems based on economic reward and violation cost. The decoupling of reward and cost alleviates reward sparsity while improving critic approximation accuracy. Moreover, the introduction of Lagrange multipliers enables the agent to comprehend the trade-off between optimality and feasibility. Numerical tests are carried out and compared with penalty-based DRL methods on the IEEE 9-bus, 30-bus, and 118-bus test systems. The results show that the well-trained SDRL agent can significantly improve the computation efficiency while satisfying the security constraints and optimality requirements.
Towards Carbon-Free Electricity: A Flow-Based Framework for Power Grid Carbon Accounting and Decarbonization
Authors: Xin Chen, Hungpo Chao, Wenbo Shi, Na Li
Date: 2023-08-07 00:00:00
Tasks: Decision Making, Fairness

This paper introduces a comprehensive framework aimed at advancing research and policy development in the realm of decarbonization within electric power systems. The framework focuses on three key aspects: carbon accounting, carbon-aware decision-making, and carbon-electricity market design. It addresses existing problems, methods, and proposes solutions. In contrast to traditional pool-based emissions models, our framework proposes a novel flow-based emissions model. This model incorporates the underlying physical power grid and power flows, allowing for accurate carbon accounting at both temporal and spatial scales. This, in turn, facilitates informed decision-making to achieve grid decarbonization goals. The framework is built on a flow-based accounting methodology and utilizes the carbon-aware optimal power flow (C-OPF) technique as a theoretical foundation for decarbonization decision-making. Additionally, the paper explores the potential design of carbon-electricity markets and pricing mechanisms to incentivize decentralized decarbonization actions. The critical issues of data availability, infrastructure development, and considerations of fairness and equity are also discussed. This paper seeks to advance scholarly understanding and foster progress toward achieving sustainable and carbon-free electric power systems.
Scaling up the Optimal Safe Control of Connected and Automated Vehicles to a Traffic Network: A Hierarchical Framework of Modular Control Zones
Authors: Kaiyuan Xu, Christos G. Cassandras
Date: 2023-08-06 00:00:00

We consider the problem of scaling up optimal and safe controllers for Connected and Automated Vehicles (CAVs) from a single Control Zone (CZ) around a traffic conflict area to an entire network. The goal is to jointly minimize travel time and energy consumption for all CAVs, while providing speed-dependent safety guarantees within a CZ and satisfying velocity and acceleration constraints. A hierarchical modular CZ framework is developed consisting of a lower level where decentralized controllers are used that combine Optimal control and Control Barrier Functions (OCBF) and a higher level where a feedback flow controller is proposed to coordinate adjacent CZs. The flow controller is parameterized by a terminal velocity constraint that serves as the interface between CZs. Simulation results show that the proposed modular control zone framework outperforms a direct extension of the OCBF framework to multiple CZs without any flow control.
Optimal battery thermal management for electric vehicles with battery degradation minimization
Authors: Yue Wu, Zhiwu Huang, Dongjun Li, Heng Li, Jun Peng, Daniel Stroe, Ziyou Song
Date: 2023-08-06 00:00:00
Tasks: Management

The control of a battery thermal management system (BTMS) is essential for the thermal safety, energy efficiency, and durability of electric vehicles (EVs) in hot weather. To address the battery cooling optimization problem, this paper utilizes dynamic programming (DP) to develop an online rule-based control strategy. Firstly, an electrical-thermal-aging model of the $\rm LiFePO_4$ battery pack is established. A control-oriented onboard BTMS model is proposed and verified under different speed profiles and temperatures. Then in the DP framework, a cost function consisting of battery aging cost and cooling-induced electricity cost is minimized to obtain the optimal compressor power. By exacting three rules "fast cooling, slow cooling, and temperature-maintaining" from the DP result, a near-optimal rule-based cooling strategy, which uses as much regenerative energy as possible to cool the battery pack, is proposed for online execution. Simulation results show that the proposed online strategy can dramatically improve the driving economy and reduce battery degradation under diverse operation conditions, achieving less than a 3% difference in battery loss compared to the offline DP. Recommendations regarding battery cooling under different real-world cases are finally provided.
PowerSimulationsDynamics.jl -- An Open Source Modeling Package for Modern Power Systems with Inverter-Based Resources
Authors: Jose Daniel Lara, Rodrigo Henriquez-Auba, Matthew Bossart, Duncan S. Callaway, Clayton Barrows
Date: 2023-08-05 00:00:00

In this paper we present the development of an open-source simulation toolbox, PowerSimulationsDynamics.jl, to study the dynamic response of power systems, focusing on the requirements to model systems with high penetrations of Inverter-Based Resources (IBRs). PowerSimulationsDynamics.jl is implemented in Julia and features a rich library of synchronous generator, inverter, and load models. In addition, it allows the study of quasi-static phasors and electromagnetic dq models that use a dynamic network representation. Case studies and validation exercises show that PowerSimulationsDynamics.jl results closely match other commercial and open-source simulation tools.
Passivity-based economic ports for optimal operation of networked DC microgrids
Authors: Pol Jané-Soneira, Albertus J. Malan, Ionela Prodan, Sören Hohmann
Date: 2023-08-05 00:00:00

In this paper, we introduce the novel concept of economic ports, allowing modular and distributed optimal operation of networked microgrids. Firstly, we design a novel price-based controller for optimal operation of a single microgrid and show asymptotic stability. Secondly, we define novel physical and economic interconnection ports for the microgrid and study the dissipativity properties of these ports. Lastly, we propose an interconnection scheme for microgrids via the economic ports. This interconnection scheme requires only an exchange of the local prices and allows a globally economic optimal operation of networked microgrids at steady state, while guaranteeing asymptotic stability of the networked microgrids via the passivity properties of economic ports. The methods are demonstrated through various academic examples.
Analytical Safety Bounds for Trajectory Following Controllers in Autonomous Vehicles
Authors: Robert Jacumet, Christian Rathgeber, Vladislav Nenchev
Date: 2023-08-05 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles

A major challenge in autonomous driving is designing control architectures that guarantee safety in all relevant driving scenarios. Given a safe desired reference trajectory for the vehicle, a trajectory following controller has to ensure that the trajectory is followed with a maximally allowed deviation, even in the presence of external disturbances, such as wind gusts or inclined roads. In this paper, a method for computing upper bounds for linear, time invariant single-input-multiple-output systems with state feedback controllers and additive bounded disturbances is proposed. The bounds for the offset between states and the reference are derived analytically based on worst case disturbance sequences. For systems with two states the bounds are strict, while for higher order systems they are conservative. The method is applied to obtain position bounds for lateral trajectory following controllers, and to analyze how different choices of feedback parameters affect safety margins.
The role of APOBEC3-induced mutations in the differential evolution of monkeypox virus
Authors: Xiangting Li, Sara Habibipour, Tom Chou, Otto O. Yang
Date: 2023-08-07 00:00:00

Recent studies show that newly sampled monkeypox virus (MPXV) genomes exhibit mutations consistent with Apolipoprotein B mRNA Editing Catalytic Polypeptide-like3 (APOBEC3)-mediated editing, compared to MPXV genomes collected earlier. It is unclear whether these single nucleotide polymorphisms (SNPs) result from APOBEC3-induced editing or are a consequence of genetic drift within one or more MPXV animal reservoirs. We develop a simple method based on a generalization of the General-Time-Reversible (GTR) model to show that the observed SNPs are likely the result of APOBEC3-induced editing. The statistical features allow us to extract lineage information and estimate evolutionary events.
An Effective Hydrodynamic Description of Marching Locusts
Authors: Dan Gorbonos, Felix Oberhauser, Luke L. Costello, Yannick Günzel, Einat Couzin-Fuchs, Benjamin Koger, Iain D. Couzin
Date: 2023-08-03 00:00:00

A fundamental question in complex systems is how to relate interactions between individual components ("microscopic description") to the global properties of the system ("macroscopic description"). Another fundamental question is whether such a macroscopic description exists at all and how well it describes the large-scale properties. Here, we address these questions using as a canonical example of a self-organizing complex system - the collective motion of desert locusts. One of the world's most devastating insect plagues begins when flightless juvenile locusts form "marching bands". Moving through semiarid habitats in the search for food, these bands display remarkable coordinated motion. We investigated how well physical models can describe the flow of locusts within a band. For this, we filmed locusts within marching bands during an outbreak in Kenya and automatically tracked all individuals passing through the camera frame. We first analysed the spatial topology of nearest neighbors and found individuals to be isotropically distributed. Despite this apparent randomness, a local order was observed in regions of high density with a clear second neighbor peak in the radial distribution function, akin to an ordered fluid. Furthermore, reconstructing individual locust trajectories revealed a highly-aligned movement, consistent with the one-dimensional version of the Toner-Tu equations, which are a generalization of the Navier-Stokes equations for fluids, used to describe the equivalent macroscopic fluid properties of active particles. Using this effective Toner-Tu equation, which relates the gradient of the pressure to the acceleration, we show that the effective "pressure" of locusts increases as a linear function of density in segments with highest polarization. Our study thus demonstrates an effective hydrodynamic description of flow dynamics in plague locust swarms.
Simultaneous self-organization of arterial and venous networks driven by the physics of global power optimization
Authors: James P. Hague
Date: 2023-08-04 00:00:00

Understanding of vascular organization is a long-standing problem in quantitative biology and biophysics and is essential for the growth of large cultured tissues. Approaches are needed that (1) make predictions of optimal arteriovenous networks in order to understand the natural vasculatures that originate from evolution (2) can design vasculature for 3D printing of cultured tissues, meats, organoids and organs. I present a method for determining the globally optimal structure of interlocking arterial and venous (arteriovenous) networks. The core physics is comprised of the minimization of total power associated with the whole vascular network, with penalties to stop arterial and venous segments from intersecting. Specifically, the power needed for Poiseuille flow through vessels and the metabolic power cost for blood maintenance are optimized. Simultaneous determination of both arterial and venous vasculatures is essential to avoid intersections between vessels that would bypass the capillary network. As proof-of-concept, I examine the optimal vascular structure for supplying square- and disk-like tissue shapes that would be suitable for bioprinting in multi-well plates. Features in the trees are driven by the bifurcation exponent and metabolic constant which affect whether arteries and veins follow the same or different routes through the tissue. They also affect the level of tortuosity in the vessels. The method could be used to understand the distribution of blood vessels within organs, to form the core of simulations, and combined with 3D printing to generate vasculatures for arbitrary volumes of cultured tissue and cultured meat.