pwc-archive/papers-with-abstracts huggingface dataset:
SEF-PNet: Speaker Encoder-Free Personalized Speech Enhancement with Local and Global Contexts Aggregation
Authors: Ziling Huang, Haixin Guan, Haoran Wei, Yanhua Long
Date: 2025-01-20 00:00:00
Tasks: Speaker Verification, Speech Enhancement

Personalized speech enhancement (PSE) methods typically rely on pre-trained speaker verification models or self-designed speaker encoders to extract target speaker clues, guiding the PSE model in isolating the desired speech. However, these approaches suffer from significant model complexity and often underutilize enrollment speaker information, limiting the potential performance of the PSE model. To address these limitations, we propose a novel Speaker Encoder-Free PSE network, termed SEF-PNet, which fully exploits the information present in both the enrollment speech and noisy mixtures. SEF-PNet incorporates two key innovations: Interactive Speaker Adaptation (ISA) and Local-Global Context Aggregation (LCA). ISA dynamically modulates the interactions between enrollment and noisy signals to enhance the speaker adaptation, while LCA employs advanced channel attention within the PSE encoder to effectively integrate local and global contextual information, thus improving feature learning. Experiments on the Libri2Mix dataset demonstrate that SEF-PNet significantly outperforms baseline models, achieving state-of-the-art PSE performance.
FlashSR: One-step Versatile Audio Super-resolution via Diffusion Distillation
Authors: Jaekwon Im, Juhan Nam
Date: 2025-01-18 00:00:00
Tasks: Audio Super-Resolution, Super-Resolution

Versatile audio super-resolution (SR) is the challenging task of restoring high-frequency components from low-resolution audio with sampling rates between 4kHz and 32kHz in various domains such as music, speech, and sound effects. Previous diffusion-based SR methods suffer from slow inference due to the need for a large number of sampling steps. In this paper, we introduce FlashSR, a single-step diffusion model for versatile audio super-resolution aimed at producing 48kHz audio. FlashSR achieves fast inference by utilizing diffusion distillation with three objectives: distillation loss, adversarial loss, and distribution-matching distillation loss. We further enhance performance by proposing the SR Vocoder, which is specifically designed for SR models operating on mel-spectrograms. FlashSR demonstrates competitive performance with the current state-of-the-art model in both objective and subjective evaluations while being approximately 22 times faster.
Speech Emotion Detection Based on MFCC and CNN-LSTM Architecture
Authors: Qianhe Ouyang
Date: 2025-01-18 00:00:00

Emotion detection techniques have been applied to multiple cases mainly from facial image features and vocal audio features, of which the latter aspect is disputed yet not only due to the complexity of speech audio processing but also the difficulties of extracting appropriate features. Part of the SAVEE and RAVDESS datasets are selected and combined as the dataset, containing seven sorts of common emotions (i.e. happy, neutral, sad, anger, disgust, fear, and surprise) and thousands of samples. Based on the Librosa package, this paper processes the initial audio input into waveplot and spectrum for analysis and concentrates on multiple features including MFCC as targets for feature extraction. The hybrid CNN-LSTM architecture is adopted by virtue of its strong capability to deal with sequential data and time series, which mainly consists of four convolutional layers and three long short-term memory layers. As a result, the architecture achieved an accuracy of 61.07% comprehensively for the test set, among which the detection of anger and neutral reaches a performance of 75.31% and 71.70% respectively. It can also be concluded that the classification accuracy is dependent on the properties of emotion to some extent, with frequently-used and distinct-featured emotions having less probability to be misclassified into other categories. Emotions like surprise whose meaning depends on the specific context are more likely to confuse with positive or negative emotions, and negative emotions also have a possibility to get mixed with each other.
BOOST: Microgrid Sizing using Ordinal Optimization
Authors: Mohamad Fares El Hajj Chehade, Sami Karaki
Date: 2025-01-18 00:00:00
Tasks: Computational Efficiency

The transition to sustainable energy systems has highlighted the critical need for efficient sizing of renewable energy resources in microgrids. In particular, designing photovoltaic (PV) and battery systems to meet residential loads is challenging due to trade-offs between cost, reliability, and environmental impact. While previous studies have employed dynamic programming and heuristic techniques for microgrid sizing, these approaches often fail to balance computational efficiency and accuracy. In this work, we propose BOOST, or Battery-solar Ordinal Optimization Sizing Technique, a novel framework for optimizing the sizing of PV and battery components in microgrids. Ordinal optimization enables computationally efficient evaluations of potential designs while preserving accuracy through robust ranking of solutions. To determine the optimal operation of the system at any given time, we introduce a mixed-integer linear programming (MILP) approach, which achieves lower costs than the commonly used dynamic programming methods. Our numerical experiments demonstrate that the proposed framework identifies optimal designs that achieve a levelized cost of energy (LCOE) as low as 8.84 cents/kWh, underscoring its potential for cost-effective microgrid design. The implications of our work are significant: BOOST provides a scalable and accurate methodology for integrating renewable energy into residential microgrids, addressing economic and environmental goals simultaneously.
Instantaneous Core Loss -- Cycle-by-cycle Modeling of Power Magnetics in PWM DC-AC Converters
Authors: Binyu Cui, Jun Wang
Date: 2025-01-23 00:00:00

Nowadays, PWM excitation is one of the most common waveforms seen by magnetic components in power electronic converters. Core loss modeling approaches such as the improved Generalized Steinmetz Equation (iGSE) or the loss map based on the composite waveform hypothesis (CWH) generally process the PWM excitation piecewise, which has been proven effective for DC-DC converters. However, an additional challenge arises in DC-AC converters, i.e. the fundamental-frequency sinewave component induces the major loop loss on top of the piecewise high-frequency segments. This major loop loss cannot be modeled on a switching cycle basis by any existing methods. To address this gap, this paper proposes a novel fundamental concept, instantaneous core loss, which is observed empirically for the first time. Enabled by the reactive voltage cancellation method, the instantaneous core loss, which only contains the real power loss, can be measured as a function of time. Building on this concept, a modeling method is proposed to break down the major loop core loss, typically represented as an average value in the literature, into the time domain, enabling cycle-by-cycle modeling as a practical workflow for predicting core losses in PWM converters. Through experiments, the existence of the major loop loss is verified, and generic instantaneous core loss models are extracted for several magnetic components. An example workflow is proposed to extract the cycle-by-cycle core loss at the design stage of a PWM DC-AC converter. This work enhances the fundamental understanding of the core loss process in real time, contributing to the advancement of scientific knowledge.
Benchmark Study of Transient Stability during Power-Hardware-in-the-Loop and Fault-Ride-Through capabilities of PV inverters
Authors: Carina Lehmal, Ziqian Zhang, Robert Schürhuber
Date: 2025-01-23 00:00:00

The deployment of PV inverters is rapidly expanding across Europe, where these devices must increasingly comply with stringent grid requirements.This study presents a benchmark analysis of four PV inverter manufacturers, focusing on their Fault Ride Through capabilities under varying grid strengths, voltage dips, and fault durations, parameters critical for grid operators during fault conditions.The findings highlight the influence of different inverter controls on key metrics such as total harmonic distortion of current and voltage signals, as well as system stability following grid faults.Additionally, the study evaluates transient stability using two distinct testing approaches.The first approach employs the current standard method, which is testing with an ideal voltage source. The second utilizes a Power Hardware in the Loop methodology with a benchmark CIGRE grid model.The results reveal that while testing with an ideal voltage source is cost-effective and convenient in the short term, it lacks the ability to capture the dynamic interactions and feedback loops of physical grid components.This limitation can obscure critical real world factors, potentially leading to unexpected inverter behavior and operational challenges in grids with high PV penetration.This study underscores the importance of re-evaluating conventional testing methods and incorporating Power Hardware in the Loop structures to achieve test results that more closely align with real-world conditions.
Leveraging Digital Twin and Machine Learning Techniques for Anomaly Detection in Power Electronics Dominated Grid
Authors: Ildar N. Idrisov, Divine Okeke, Abdullatif Albaseer, Mohamed Abdallah, Federico M. Ibanez
Date: 2025-01-23 00:00:00
Tasks: Anomaly Detection

Modern power grids are transitioning towards power electronics-dominated grids (PEDG) due to the increasing integration of renewable energy sources and energy storage systems. This shift introduces complexities in grid operation and increases vulnerability to cyberattacks. This research explores the application of digital twin (DT) technology and machine learning (ML) techniques for anomaly detection in PEDGs. A DT can accurately track and simulate the behavior of the physical grid in real-time, providing a platform for monitoring and analyzing grid operations, with extended amount of data about dynamic power flow along the whole power system. By integrating ML algorithms, the DT can learn normal grid behavior and effectively identify anomalies that deviate from established patterns, enabling early detection of potential cyberattacks or system faults. This approach offers a comprehensive and proactive strategy for enhancing cybersecurity and ensuring the stability and reliability of PEDGs.
Risk and Vulnerability Assessment of Energy-Transportation Infrastructure Systems to Extreme Weather
Authors: Jiawei Wang, Qinglai Guo, Hongbin Sun
Date: 2025-01-23 00:00:00

The interaction between extreme weather events and interdependent critical infrastructure systems involves complex spatiotemporal dynamics. Multi-type emergency decisions within energy-transportation infrastructures significantly influence system performance throughout the extreme weather process. A comprehensive assessment of these factors faces challenges in model complexity and heterogeneity between energy and transportation systems. This paper proposes an assessment framework that accommodates multiple types of emergency decisions. It integrates the heterogeneous energy and transportation infrastructures in the form of a network flow model to simulate and quantify the impact of extreme weather events on the energy-transportation infrastructure system. Based on this framework, a targeted method for identifying system vulnerabilities is further introduced, utilizing a neural network surrogate that achieves privacy protection and evaluation acceleration while maintaining consideration of system interdependencies. Numerical experiments demonstrate that the proposed framework and method can reveal the risk levels faced by urban infrastructure systems, identify weak points that should be prioritized for reinforcement, and strike a balance between accuracy and evaluation speed.
Comparative Withholding Behavior Analysis of Historical Energy Storage Bids in California
Authors: Neal Ma, Ningkun Zheng, Ning Qi, Bolun Xu
Date: 2025-01-23 00:00:00

The rapid growth of battery energy storage in wholesale electricity markets calls for a deeper understanding of storage operators' bidding strategies and their market impacts. This study examines energy storage bidding data from the California Independent System Operator (CAISO) between July 1, 2023, and October 1, 2024, with a primary focus on economic withholding strategies. Our analysis reveals that storage bids are closely aligned with day-ahead and real-time market clearing prices, with notable bid inflation during price spikes. Statistical tests demonstrate a strong correlation between price spikes and capacity withholding, indicating that operators can anticipate price surges and use market volatility to increase profitability. Comparisons with optimal hindsight bids further reveal a clear daily periodic bidding pattern, highlighting extensive economic withholding. These results underscore potential market inefficiencies and highlight the need for refined regulatory measures to address economic withholding as storage capacity in the market continues to grow.
Time-Constrained Model Predictive Control for Autonomous Satellite Rendezvous, Proximity Operations, and Docking
Authors: Gabriel Behrendt, Matthew Hale, Alexander Soderlund, Sean Phillips, Evan Kain
Date: 2025-01-22 00:00:00
Tasks: Model Predictive Control

This paper presents a time-constrained model predictive control strategy for the six degree-of-freedom autonomous rendezvous, proximity, operations and docking problem between a controllable "deputy" satellite and an uncontrolled "chief" satellite. The objective is to achieve a docking configuration defined by both the translational and attitudinal states of the deputy relative to the chief, whose dynamics are respectively governed by both the Clohessy-Wiltshire equations and Euler's second law of motion. The proposed control strategy explicitly addresses computational time constraints that are common to state-of-the-art space vehicles. Thus, a time-constrained model predictive control strategy is implemented on a space-grade processor. Although suboptimal with regards to energy consumption when compared to conventional optimal RPO trajectories, it is empirically demonstrated via numerical simulations that the deputy spacecraft still achieves a successful docking configuration while subject to computational time constraints.
Symbolic Control for Autonomous Docking of Marine Surface Vessels
Authors: Elizabeth Dietrich, Emir Cem Gezer, Bingzhuo Zhong, Murat Arcak, Majid Zamani, Roger Skjetne, Asgeir Johan Sørensen
Date: 2025-01-22 00:00:00

Docking marine surface vessels remains a largely manual task due to its safety-critical nature. In this paper, we develop a hierarchical symbolic control architecture for autonomous docking maneuvers of a dynamic positioning vessel, to provide formal safety guarantees. At the upper-level, we treat the vessel's desired surge, sway, and yaw velocities as control inputs and synthesize a symbolic controller in real-time. The desired velocities are then transmitted to and executed by the vessel's low-level velocity feedback control loop. Given a synthesized symbolic controller, we investigate methods to optimize the performance of the proposed control scheme for the docking task. The efficacy of this methodology is evaluated on a low-fidelity simulation model of a marine surface vessel in the presence of static and dynamic obstacles and, for the first time, through physical experiments on a scaled model vessel.
Learning to Optimize Joint Chance-constrained Power Dispatch Problems
Authors: Meiyi Li, Javad Mohammadi
Date: 2025-01-22 00:00:00
Tasks: energy management

The ever-increasing integration of stochastic renewable energy sources into power systems operation is making the supply-demand balance more challenging. While joint chance-constrained methods are equipped to model these complexities and uncertainties, solving these models using the traditional iterative solvers is time-consuming and can hinder real-time implementation. To overcome the shortcomings of today's solvers, we propose a fast, scalable, and explainable machine learning-based optimization proxy. Our solution, called Learning to Optimize the Optimization of Joint Chance-Constrained Problems (LOOP-JCCP), is iteration-free and solves the underlying problem in a single-shot. Our model uses a polyhedral reformulation of the original problem to manage constraint violations and ensure solution feasibility across various scenarios through customizable probability settings. To this end, we build on our recent deterministic solution (LOOP-LC 2.0) by incorporating a set aggregator module to handle uncertain sample sets of varying sizes and complexities. Our results verify the feasibility of our near-optimal solutions for joint chance-constrained power dispatch scenarios. Additionally, our feasibility guarantees increase the transparency and interpretability of our method, which is essential for operators to trust the outcomes. We showcase the effectiveness of our model in solving the stochastic energy management problem of Virtual Power Plants (VPPs). Our numerical findings complement our theoretical justifications and demonstrate great flexibility in parameter tuning, adaptability to diverse datasets, and increased computational speed.
On the reproducibility of discrete-event simulation studies in health research: an empirical study using open models
Authors: Amy Heather, Thomas Monks, Alison Harper, Navonil Mustafee, Andrew Mayne
Date: 2025-01-22 00:00:00
Tasks: Articles

Reproducibility of computational research is critical for ensuring transparency, reliability and reusability. Challenges with computational reproducibility have been documented in several fields, but healthcare discrete-event simulation (DES) models have not been thoroughly examined in this context. This study assessed the computational reproducibility of eight published healthcare DES models (Python or R), selected to represent diverse contexts, complexities, and years of publication. Repositories and articles were also assessed against guidelines and reporting standards, offering insights into their relationship with reproducibility success. Reproducing results required up to 28 hours of troubleshooting per model, with 50% fully reproduced and 50% partially reproduced (12.5% to 94.1% of reported outcomes). Key barriers included the absence of open licences, discrepancies between reported and coded parameters, and missing code to produce model outputs, run scenarios, and generate tables and figures. Addressing these issues would often require relatively little effort from authors: adding an open licence and sharing all materials used to produce the article. Actionable recommendations are proposed to enhance reproducibility practices for simulation modellers and reviewers.
Urban Priority Pass: Fair Signalized Intersection Management Accounting For Passenger Needs Through Prioritization
Authors: Kevin Riehl, Anastasios Kouvelas, Michail Makridis
Date: 2025-01-22 00:00:00
Tasks: Management

Over the past few decades, efforts of road traffic management and practice have predominantly focused on maximizing system efficiency and mitigating congestion from a system perspective. This efficiency-driven approach implies the equal treatment of all vehicles, which often overlooks individual user experiences, broader social impacts, and the fact that users are heterogeneous in their urgency and experience different costs when being delayed. Existing strategies to account for the differences in needs of users in traffic management cover dedicated transit lanes, prioritization of emergency vehicles, transit signal prioritization, and economic instruments. Even though they are the major bottleneck for traffic in cities, no dedicated instrument that enables prioritization of individual drivers at intersections. The Priority Pass is a reservation-based, economic controller that expedites entitled vehicles at signalized intersections, without causing arbitrary delays for not-entitled vehicles and without affecting transportation efficiency de trop. The prioritization of vulnerable road users, emergency vehicles, commercial taxi and delivery drivers, or urgent individuals can enhance road safety, and achieve social, environmental, and economic goals. A case study in Manhattan demonstrates the feasibility of individual prioritization (up to 40\% delay decrease), and quantifies the potential of the Priority Pass to gain social welfare benefits for the people. A market for prioritization could generate up to 1 million \$ in daily revenues for Manhattan, and equitably allocate delay reductions to those in need, rather than those with a high income.
RIS-Aided Monitoring With Cooperative Jamming: Design and Performance Analysis
Authors: Shuying Lin, Yulong Zou, Zhiyang Li, Tong Wu, Eduard E. Bahingayi, Le-Nam Tran
Date: 2025-01-21 00:00:00

We investigate a reconfigurable intelligent surface (RIS) aided wireless surveillance system. In this system, a monitor not only receives signal from suspicious transmitter via a RIS-enhanced legitimate surveillance (LS) link but also simultaneously takes control of multiple jammers to degrade the quality of received suspicious signal. Under this setup, to enhance monitoring performance requires improvements of both the received signal quality at the monitor and the cooperative jamming (CJ). Considering that the surveillance system is aided by one RIS, whose phase shift optimization involves both channel state information (CSI) of the LS and CJ links, we utilize partial CSI to alleviate the CSI acquisition burden in our design. We propose two RIS-aided monitoring schemes with optimal jammer selection (OJS), and derive their closed-form expressions of surveillance success probability (SSP), respectively. Furthermore, we consider RIS-aided monitoring schemes with random jammer selection as corresponding benchmarks. Thereafter, we analyze special cases where the jammers are using power control to avoid being found, making it appears like passive monitoring. Also, the effect of RIS is highlighted by considering asymptotically large number of RIS elements. Numerical results verify that the proposed OJS strategy further enhances the RIS-aided monitoring performance compared with non-jammer-selection RISLR and RISCR schemes, where the superiority comes at the cost of CSI knowledge and becomes marginal in the region of high jamming power. In addition, the RISLO shows surveillance performance advantage overRISCOwhen the suspicious power is low or when the number of RIS elements is large.