pwc-archive/papers-with-abstracts huggingface dataset:
Evince the artifacts of Spoof Speech by blending Vocal Tract and Voice Source Features
Authors: Tadipatri Uday Kiran Reddy, Sahukari Chaitanya Varun, Kota Pranav Kumar Sankala Sreekanth, Kodukula Sri Rama Murty
Date: 2022-12-05 00:00:00
Tasks: Edge Classification

With the rapid advancement in synthetic speech generation technologies, great interest in differentiating spoof speech from the natural speech is emerging in the research community. The identification of these synthetic signals is a difficult task not only for the cutting-edge classification models but also for humans themselves. To prevent potential adverse effects, it becomes crucial to detect spoof signals. From a forensics perspective, it is also important to predict the algorithm which generated them to identify the forger. This needs an understanding of the underlying attributes of spoof signals which serve as a signature for the synthesizer. This study emphasizes the segments of speech signals critical in identifying their authenticity by utilizing the Vocal Tract System(\textit{VTS}) and Voice Source(\textit{VS}) features. In this paper, we propose a system that detects spoof signals as well as identifies the corresponding speech-generating algorithm. We achieve 99.58\% in algorithm classification accuracy. From experiments, we found that a VS feature-based system gives more attention to the transition of phonemes, while, a VTS feature-based system gives more attention to stationary segments of speech signals. We perform model fusion techniques on the VS-based and VTS-based systems to exploit the complementary information to develop a robust classifier. Upon analyzing the confusion plots we found that WaveRNN is poorly classified depicting more naturalness. On the other hand, we identified that synthesizer like Waveform Concatenation, and Neural Source Filter is classified with the highest accuracy. Practical implications of this work can aid researchers from both forensics (leverage artifacts) and the speech communities (mitigate artifacts).
A subjective study of the perceptual acceptability of audio-video desynchronization in sports videos
Authors: Joshua Peter Ebenezer
Date: 2022-12-03 00:00:00
Tasks: Video Synchronization

This paper presents the results of a study conducted on the perceptual acceptability of audio-video desynchronization for sports videos. The study was conducted with 45 videos generated by applying 8 audio-video offsets on 5 source contents. 20 subjects participated in the study. The results show that humans are more sensitive to audio-video offset errors for speech stimuli, and the complex events that occur in sports broadcasts have higher thresholds of acceptability. This suggests the tuning of audio-video synchronization requirements in broadcasting to the content of the broadcast.
Can we still use PEAQ? A Performance Analysis of the ITU Standard for the Objective Assessment of Perceived Audio Quality
Authors: Pablo M. Delgado, Jürgen Herre
Date: 2022-12-02 00:00:00
Tasks: Bandwidth Extension

The Perceptual Evaluation of Audio Quality (PEAQ) method as described in the International Telecommunication Union (ITU) recommendation ITU-R BS.1387 has been widely used for computationally estimating the quality of perceptually coded audio signals without the need for extensive subjective listening tests. However, many reports have highlighted clear limitations of the scheme after the end of its standardization, particularly involving signals coded with newer technologies such as bandwidth extension or parametric multi-channel coding. Until now, no other method for measuring the quality of both speech and audio signals has been standardized by the ITU. Therefore, a further investigation of the causes for these limitations would be beneficial to a possible update of said scheme. Our experimental results indicate that the performance of PEAQ's model of disturbance loudness is still as good as (and sometimes superior to) other state-of-the-art objective measures, albeit with varying performance depending on the type of degraded signal content (i.e. speech or music). This finding evidences the need for an improved cognitive model. In addition, results indicate that an updated mapping of Model Output Values (MOVs) to PEAQ's Distortion Index (DI) based on newer training data can greatly improve performance. Finally, some suggestions for the improvement of PEAQ are provided based on the reported results and comparison to other systems.
Investigations on the Influence of Combined Inter-Aural Cue Distortions on Overall Audio Quality
Authors: Pablo M. Delgado, Jürgen Herre
Date: 2022-12-02 00:00:00

There is a considerable interest in developing algorithms that can predict audio quality of perceptually coded signals to avoid the cost of extensive listening tests during development time. While many established algorithms for predicting the perceived quality of signals with monaural (timbral) distortions are available (PEAQ, POLQA), predicting the quality degradation of stereo and multi-channel spatial signals is still considered a challenge. Audio quality degradation arising from spatial distortions is usually measured in terms of well known inter-aural cue distortion measures such as Inter-aural Level Difference Distortions (ILDD), Inter-aural Time Difference Distortions (ITDD) and Inter-aural Cross Correlation Distortions (IACCD). However, the extent to which their interaction influences the overall audio quality degradation in complex signals as expressed - for example - in a multiple stimuli test is not yet thoroughly studied. We propose a systematic approach that introduces controlled combinations of spatial distortions on a representative set of signals and evaluates their influence on overall perceived quality degradation by analyzing listening test scores over said signals. From this study we derive guidelines for designing meaningful distortion measures that consider inter-aural cue distortion interactions.
Evaluation of Energy Resilience and Cost Benefit in Microgrid with Peer-to-Peer Energy Trading
Authors: Divyanshi Dwivedi, K. Victor Sam Moses Babu, Pradeep Kumar Yemula, Pratyush Chakraborty, Mayukha Pal
Date: 2022-12-05 00:00:00
Tasks: energy trading, Time Series, Time Series Analysis

Integration of distributed energy resources (DERs) at a residential level has given way to the development of a self-sufficient energy-resilient system and peer-to-peer (P2P) energy trading at a community level. Several researchers have developed models to implement energy trading in the P2P network with DERs that enables prosumers to avail cost benefits. However, the impact of DERs and P2P energy network on the system's resilience is not evaluated with a quantifiable measure. In this work, we use the percolation threshold as a measure to quantify energy resilience, which can be computed using complex network. We consider a standard IEEE-123 node test feeder integrated with renewable energy sources and partitioned into microgrids. The microgrid with the highest percolation threshold value is considered for P2P energy trading analysis. We use cooperative game theory to model the P2P energy trading so that all prosumers take part rationally. The simulation is carried out using time series data of houses with varying load profiles, solar generation, and energy storage capacities. The results show 5.81\% of total cost benefit in a year and 10.67\% improvement in the resilience of the system due to P2P energy trading.
Research on Early Warning and NB-IoT Real-time Monitoring System for Radiation Source Shedding of Gamma Flaw Detection Machine
Authors: Zheng-yang Zhang, Zhi-hui Liu, Rui Zhang, Rong-hua He, Zhe Wang
Date: 2022-12-05 00:00:00

The system takes the embedded system single chip as the core, and organizes the gamma ray induction module, keying switch, radiation source braid locking mechanism, on-site alarm equipment, NB-IoT communication module, GPS positioning system and other related equipment to realize real-time operators warning and remote alarming to the monitoring platform. Thus, timely management of the fallen radiation source can be realized. What's more, position of radiation source braid is monitored by radiation source braid locking mechanism, gamma-ray induction module and keying switch. This idea solves the bottleneck problem of difficulty in transmitting real-time remote monitoring signal by NB-IoT technology. Single-chip microcontroller is innovatively embedded around the radiation source to monitor whether the source falls off. As a result, when part or all of the source braid are not returned to the storage location of the flaw detector, the gamma ray induction module, keying switch, and radiation source braid locking mechanism remind the operator of the dangerous situation. At the same time, NB-IoT transmission system alarms companies and regulators of safety risks. In conclusion, radiation source can be found timely when it falls off, to avoid radiation accident. Two bottlenecks related to installing GPS positioning system can be solved, that the GPS positioning system is installed on the flaw detector, and the radioactive source is still unable to be controlled, and that the GPS positioning system requires power to transmit the signal back to the regulatory platform or to the platform of the flaw detection enterprise. This article's ideas can solve this problem through the advantages of NB-IoT.
Performance Quantification of a Nonlinear Model Predictive Controller by Parallel Monte Carlo Simulations of a Closed-loop System
Authors: Morten Wahlgreen Kaysfeld, Mario Zanon, John Bagterp Jørgensen
Date: 2022-12-05 00:00:00
Tasks: CPU, Model Predictive Control

This paper presents a parallel Monte Carlo simulation based performance quantification method for nonlinear model predictive control (NMPC) in closed-loop. The method provides distributions for the controller performance in stochastic systems enabling performance quantification. We perform high-performance Monte Carlo simulations in C enabled by a new thread-safe NMPC implementation in combination with an existing high-performance Monte Carlo simulation toolbox in C. We express the NMPC regulator as an optimal control problem (OCP), which we solve with the new thread-safe sequential quadratic programming software NLPSQP. Our results show almost linear scale-up for the NMPC closed-loop on a 32 core CPU. In particular, we get approximately 27 times speed-up on 32 cores. We demonstrate the performance quantification method on a simple continuous stirred tank reactor (CSTR), where we perform 30,000 closed-loop simulations with both an NMPC and a reference proportional-integral (PI) controller. Performance quantification of the stochastic closed-loop system shows that the NMPC outperforms the PI controller in both mean and variance.
Interactive Planning and Operations using Peak Load Pricing in Distribution Systems
Authors: Marija Ilic, Matthew Gough
Date: 2022-12-05 00:00:00

The emergence of Distributed Energy Resources (DERs) provides both challenges and opportunities for the planning and operations of distribution systems. These resources can be deployed in a manner that is either complementary to or in competition with traditional network operations and planning as the DERs can provide numerous important services to grid operators and utilities. This paper presents a novel method to estimate the trade off between DERs and traditional investments using a dynamic Peak-Load Pricing (PLP) methodology. PLP is a pricing strategy for a time-dependent quantity of a non-storable commodity and is based on the theory of long-run marginal costs. Importantly PLP deals with the trade-off between capacity utilization and consumer welfare. Therefore, the capacity price is set at a point where the cost of investment is exactly offset by the additional social welfare that the investment would bring. Importantly it allows for capital cost recovery with no uplift payments. This dynamic PLP methodology is an interactive planning and operations model based on the Dynamic Monitoring and Decision Systems (DyMonDS) Framework which helps to align physical, information, and economic incentives across many stakeholders within the electric energy system. The DyMonDS framework helps to solve the drawbacks of PLP, which are related to the computational complexity of the PLP models considering different technologies with different payback periods over a long investment horizon. Results show that the dynamic PLP model can accurately value the impact of different technologies in reducing congestion and increasing the number of customers served. This allows the network operator to easily identify which technologies should be chosen in each investment cycle.
Model Predictive Control Tuning by Monte Carlo Simulation and Controller Matching
Authors: Morten Ryberg Wahlgreen, John Bagterp Jørgensen, Mario Zanon
Date: 2022-12-05 00:00:00
Tasks: Model Predictive Control

This paper presents a systematic method for the selection of the Model Predictive Control (MPC) stage cost. We match the MPC feedback law to a proportional-integral (PI) controller, which we efficiently tune by high-performance Monte Carlo (MC) simulation. The PI tuning offers a wide range of tuning possibilities that is then inherited by the MPC design. The MC simulation tuning of the PI controller is based on the minimization of two different objectives; 1) the 2-norm tracking error, and 2) a bi-objective consisting of the 2-norm tracking error and a 2-norm input rate of movement penalty. We apply the method to design MPC for an exothermic chemical reaction conducted in an adiabatic continuous stirred tank reactor (CSTR). The process is of interest as the nonlinear dynamics result in a desired operating point very close to a constraint. Our MPC design includes stage costs automatically designed to match the tuned PI controllers, hard input constraints, and a soft output constraint. Stochastic simulation results show that both the PI controller and the MPC can track the desired operating point. However, the MPC shows reduced output constraint violation compared to the PI controller. As such, the MPC design method successfully combines the efficient tuning of the PI controller with the constraint handling properties of MPC.
Predictive safety filter using system level synthesis
Authors: Antoine P. Leeman, Johannes Köhler, Samir Benanni, Melanie N. Zeilinger
Date: 2022-12-05 00:00:00

Safety filters provide modular techniques to augment potentially unsafe control inputs (e.g. from learning-based controllers or humans) with safety guarantees in the form of constraint satisfaction. In this paper, we present an improved model predictive safety filter (MPSF) formulation, which incorporates system level synthesis techniques in the design. The resulting SL-MPSF scheme ensures safety for linear systems subject to bounded disturbances in an enlarged safe set. It requires less severe and frequent modifications of potentially unsafe control inputs compared to existing MPSF formulations to certify safety. In addition, we propose an explicit variant of the SL-MPSF formulation, which maintains scalability, and reduces the required online computational effort - the main drawback of the MPSF. The benefits of the proposed system level safety filter formulations compared to state-of-the-art MPSF formulations are demonstrated using a numerical example.
Robust Reinforcement Learning for Risk-Sensitive Linear Quadratic Gaussian Control
Authors: Leilei Cui, Tamer Başar, Zhong-Ping Jiang
Date: 2022-12-05 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper proposes a novel robust reinforcement learning framework for discrete-time linear systems with model mismatch that may arise from the sim-to-real gap. A key strategy is to invoke advanced techniques from control theory. Using the formulation of the classical risk-sensitive linear quadratic Gaussian control, a dual-loop policy optimization algorithm is proposed to generate a robust optimal controller. The dual-loop policy optimization algorithm is shown to be globally and uniformly convergent, and robust against disturbances during the learning process. This robustness property is called small-disturbance input-to-state stability and guarantees that the proposed policy optimization algorithm converges to a small neighborhood of the optimal controller as long as the disturbance at each learning step is relatively small. In addition, when the system dynamics is unknown, a novel model-free off-policy policy optimization algorithm is proposed. Finally, numerical examples are provided to illustrate the proposed algorithm.
Data-driven memory-dependent abstractions of dynamical systems
Authors: Adrien Banse, Licio Romao, Alessandro Abate, Raphaël M. Jungers
Date: 2022-12-04 00:00:00

We propose a sample-based, sequential method to abstract a (potentially black-box) dynamical system with a sequence of memory-dependent Markov chains of increasing size. We show that this approximation allows to alleviating a correlation bias that has been observed in sample-based abstractions. We further propose a methodology to detect on the fly the memory length resulting in an abstraction with sufficient accuracy. We prove that under reasonable assumptions, the method converges to a sound abstraction in some precise sense, and we showcase it on two case studies.
An energy management system model with power quality constraints for unbalanced multi-microgrids interacting in a local energy market
Authors: Johanna Castellanos, Carlos Adrian Correa-Florez, Alejandro Garcés, Gabriel Ordóñez-Plata, César A. Uribe, Diego Patino
Date: 2022-12-04 00:00:00
Tasks: energy management, Management

As multi-microgrids become readily available, some limited models have been proposed that study operational and power quality constraints with local energy markets independently. This paper proposes a convex optimization model of an energy management system with operational and power quality constraints and interactions in a Local Energy Market (LEM) for unbalanced microgrids (MGs). The LEM consists of a pre-dispatch step and an energy transactions step (ETS). The ETS combines the MGs' objectives while considering two strategies: minimize the cost of buyers or maximize the revenue of sellers. Our proposed model considers harmonic distortion and voltage limit power quality constraints in both steps. Moreover, we model operational constraints such as power flow, power balance, and distributed energy resources behaviors and capacities. We numerically evaluate the proposed model using three unbalanced MGs with residential, industrial, and commercial load profiles, where each microgrid manages its resources locally. Furthermore, we create two groups of cases to analyze the interactions in the local energy market. In the first group, the price of the DSO energy and the surplus from MGs to DSO are the same. The numerical results show that using the increasing revenue strategy promotes MGs to interact more while encouraging them to have high energy prices. When the reducing cost strategy is used, fewer energy interactions occur, and the price of MGs energy is encouraged to be lower.
A New Decomposition Strategy for Analyzing Large-Scale Systems Such as Power Systems
Authors: Minquan Chen, Deqiang Gan
Date: 2022-12-04 00:00:00

In this work, it is demonstrated that the usual power system dynamic model exhibits a feedforward-feedback control structure. The distinct properties of the feedforward and feedback subsystems are identified and studied using respective nonlinear system tools. The stability of the closed-loop system is investigated using a small gain argument from input-to-state stability theory. Test results are provided to further complement the theoretical findings. The introduced methodology also shed light on the dynamics study on other interconnected nonlinear systems.
Optimal Acceptance of Incompatible Kidneys
Authors: Xingyu Ren, Michael C. Fu, Steven I. Marcus
Date: 2022-12-04 00:00:00

Incompatibility between patient and donor is a major barrier in kidney transplantation (KT). The increasing shortage of kidney donors has driven the development of desensitization techniques to overcome this immunological challenge. Compared with compatible KT, patients undergoing incompatible KTs are more likely to experience rejection, infection, malignancy, and graft loss. We study the optimal acceptance of possibly incompatible kidneys for individual end-stage kidney disease patients. To capture the effect of incompatibility, we propose a Markov Decision Process (MDP) model that explicitly includes compatibility as a state variable. The resulting higher-dimensional model makes it more challenging to analyze, but under suitable conditions, we derive structural properties including control limit-type optimal policies that are easy to compute and implement. Numerical examples illustrate the behavior of the optimal policy under different mismatch levels and highlight the importance of explicitly incorporating the incompatibility level into the acceptance decision when desensitization therapy is an option.
Recursive Identification of Binary-Valued Systems under Uniform Persistent Excitations
Authors: Jieming Ke, Ying Wang, Yanlong Zhao, Ji-Feng Zhang
Date: 2022-12-04 00:00:00

This paper studies the control-oriented identification problem of set-valued moving average systems with uniform persistent excitations and observation noises. A stochastic approximation-based (SA-based) algorithm without projections or truncations is proposed. The algorithm overcomes the limitations of the existing empirical measurement method and the recursive projection method, where the former requires periodic inputs, and the latter requires projections to restrict the search region in a compact set.To analyze the convergence property of the algorithm, the distribution tail of the estimation error is proved to be exponentially convergent through an auxiliary stochastic process. Based on this key technique, the SA-based algorithm appears to be the first to reach the almost sure convergence rate of $ O(\sqrt{\ln\ln k/k}) $ theoretically in the non-periodic input case. Meanwhile, the mean square convergence is proved to have a rate of $ O(1/k) $, which is the best one even under accurate observations. A numerical example is given to demonstrate the effectiveness of the proposed algorithm and theoretical results.
Distributionally Robust Day-ahead Scheduling for Power-traffic Network under a Potential Game Framework
Authors: Haoran Deng, Bo Yang, Chao Ning, Cailian Chen, Xinping Guan
Date: 2022-12-04 00:00:00
Tasks: Scheduling

Widespread utilization of electric vehicles (EVs) incurs more uncertainties and impacts on the scheduling of the power-transportation coupled network. This paper investigates optimal power scheduling for a power-transportation coupled network in the day-ahead energy market considering multiple uncertainties related to photovoltaic (PV) generation and the traffic demand of vehicles. The crux of this problem is to model the coupling relation between the two networks in the day-ahead scheduling stage and consider the intra-day spatial uncertainties of the source and load. Meanwhile, the flexible load with a certain adjustment margin is introduced to ensure the balance of supply and demand of power nodes and consume the renewable energy better. Furthermore, we show the interactions between the power system and EV users from a potential game-theoretic perspective, where the uncertainties are characterized by an ambiguity set. In order to ensure the individual optimality of the two networks in a unified framework in day-ahead power scheduling, a two-stage distributionally robust centralized optimization model is established to carry out the equilibrium of power-transportation coupled network. On this basis, a combination of the duality theory and the Benders decomposition is developed to solve the distributionally robust optimization (DRO) model. Simulations demonstrate that the proposed approach can obtain individual optimal and less conservative strategies.