pwc-archive/papers-with-abstracts huggingface dataset:
Dynamic Placement of Rapidly Deployable Mobile Sensor Robots Using Machine Learning and Expected Value of Information
Authors: Alice Agogino, Hae Young Jang, Vivek Rao, Ritik Batra, Felicity Liao, Rohan Sood, Irving Fang, R. Lily Hu, Emerson Shoichet-Bartus, John Matranga
Date: 2021-11-15 00:00:00
Tasks: Decision Making

Although the Industrial Internet of Things has increased the number of sensors permanently installed in industrial plants, there will be gaps in coverage due to broken sensors or sparse density in very large plants, such as in the petrochemical industry. Modern emergency response operations are beginning to use Small Unmanned Aerial Systems (sUAS) that have the ability to drop sensor robots to precise locations. sUAS can provide longer-term persistent monitoring that aerial drones are unable to provide. Despite the relatively low cost of these assets, the choice of which robotic sensing systems to deploy to which part of an industrial process in a complex plant environment during emergency response remains challenging. This paper describes a framework for optimizing the deployment of emergency sensors as a preliminary step towards realizing the responsiveness of robots in disaster circumstances. AI techniques (Long short-term memory, 1-dimensional convolutional neural network, logistic regression, and random forest) identify regions where sensors would be most valued without requiring humans to enter the potentially dangerous area. In the case study described, the cost function for optimization considers costs of false-positive and false-negative errors. Decisions on mitigation include implementing repairs or shutting down the plant. The Expected Value of Information (EVI) is used to identify the most valuable type and location of physical sensors to be deployed to increase the decision-analytic value of a sensor network. This method is applied to a case study using the Tennessee Eastman process data set of a chemical plant, and we discuss implications of our findings for operation, distribution, and decision-making of sensors in plant emergency and resilience scenarios.
Joint Far- and Near-End Speech Intelligibility Enhancement based on the Approximated Speech Intelligibility Index
Authors: Andreas Jonas Fuglsig, Jan Østergaard, Jesper Jensen, Lars Søndergaard Bertelsen, Peter Mariager, Zheng-Hua Tan
Date: 2021-11-15 00:00:00
Tasks: Speech Enhancement

This paper considers speech enhancement of signals picked up in one noisy environment which must be presented to a listener in another noisy environment. Recently, it has been shown that an optimal solution to this problem requires the consideration of the noise sources in both environments jointly. However, the existing optimal mutual information based method requires a complicated system model that includes natural speech variations, and relies on approximations and assumptions of the underlying signal distributions. In this paper, we propose to use a simpler signal model and optimize speech intelligibility based on the Approximated Speech Intelligibility Index (ASII). We derive a closed-form solution to the joint far- and near-end speech enhancement problem that is independent of the marginal distribution of signal coefficients, and that achieves similar performance to existing work. In addition, we do not need to model or optimize for natural speech variations.
Investigating self-supervised front ends for speech spoofing countermeasures
Authors: Xin Wang, Junichi Yamagishi
Date: 2021-11-15 00:00:00
Tasks: Face Swapping

Self-supervised speech model is a rapid progressing research topic, and many pre-trained models have been released and used in various down stream tasks. For speech anti-spoofing, most countermeasures (CMs) use signal processing algorithms to extract acoustic features for classification. In this study, we use pre-trained self-supervised speech models as the front end of spoofing CMs. We investigated different back end architectures to be combined with the self-supervised front end, the effectiveness of fine-tuning the front end, and the performance of using different pre-trained self-supervised models. Our findings showed that, when a good pre-trained front end was fine-tuned with either a shallow or a deep neural network-based back end on the ASVspoof 2019 logical access (LA) training set, the resulting CM not only achieved a low EER score on the 2019 LA test set but also significantly outperformed the baseline on the ASVspoof 2015, 2021 LA, and 2021 deepfake test sets. A sub-band analysis further demonstrated that the CM mainly used the information in a specific frequency band to discriminate the bona fide and spoofed trials across the test sets.
Monaural source separation: From anechoic to reverberant environments
Authors: Tobias Cord-Landwehr, Christoph Boeddeker, Thilo von Neumann, Catalin Zorila, Rama Doddipatla, Reinhold Haeb-Umbach
Date: 2021-11-15 00:00:00

Impressive progress in neural network-based single-channel speech source separation has been made in recent years. But those improvements have been mostly reported on anechoic data, a situation that is hardly met in practice. Taking the SepFormer as a starting point, which achieves state-of-the-art performance on anechoic mixtures, we gradually modify it to optimize its performance on reverberant mixtures. Although this leads to a word error rate improvement by 7 percentage points compared to the standard SepFormer implementation, the system ends up with only marginally better performance than a PIT-BLSTM separation system, that is optimized with rather straightforward means. This is surprising and at the same time sobering, challenging the practical usefulness of many improvements reported in recent years for monaural source separation on nonreverberant data.
Performance bounds of adaptive MPC with bounded parameter uncertainties
Authors: Francisco Moreno-Mora, Lukas Beckenbach, Stefan Streif
Date: 2021-11-15 00:00:00
Tasks: Model Predictive Control, parameter estimation

Model predictive control is a control approach that minimizes a stage cost over a predicted system trajectory based on a model of the system and is capable of handling state and input constraints. For uncertain models, robust or adaptive methods can be used. Because the system model is used to calculate the control law, the closed-loop behavior of the system and thus its performance, measured by the sum of the stage costs, are related to the model used. If it is adapted online, a performance bound is difficult to obtain and thus the impact of model adaptation is mostly unknown. This work provides a (worst-case) performance bound for a linear adaptive predictive control scheme with a specific model parameter estimation. The proposed bound is expressed in terms of quantities such as the initial system parameter error and the constraint set, among others and can be calculated a priori. The results are discussed in a numerical example.
Energy-optimal Design and Control of Electric Powertrains under Motor Thermal Constraints
Authors: Mouleeswar Konda, Theo Hofman, Mauro Salazar
Date: 2021-11-15 00:00:00

This paper presents a modeling and optimization framework to minimize the energy consumption of a fully electric powertrain by optimizing its design and control strategies whilst explicitly accounting for the thermal behavior of the Electric Motor (EM). Specifically, we first derive convex models of the powertrain components, including the battery, the EM, the transmission and a Lumped Parameter Thermal Network (LPTN) capturing the thermal dynamics of the EM. Second, we frame the optimal control problem in time domain, and devise a two-step algorithm to accelerate convergence and efficiently solve the resulting convex problem via nonlinear programming. Subsequently, we present a case study for a compact family car, optimize its transmission design and operation jointly with the regenerative braking and EM cooling control strategies for a finite number of motors and transmission technologies. We validate our proposed models using the high-fidelity simulation software Motor-CAD, showing that the LPTN quite accurately captures the thermal dynamics of the EM, and that the permanent magnets' temperature is the limiting factor during extended driving. Furthermore, our results reveal that powertrains equipped with a continuously variable transmission (CVT) result into a lower energy consumption than with a fixed-gear transmission (FGT), as a CVT can lower the EM losses, resulting in lower EM temperatures. Finally, our results emphasize the significance of considering the thermal behavior when designing an EM and the potential offered by CVTs in terms of downsizing.
Reliability Assessment for Distribution Systems with Embedded Microgrids
Authors: Stine Fleischer Myhre, Olav Bjarte Fosso, Oddbjørn Gjerde, Poul Einar Heegaard
Date: 2021-11-15 00:00:00

The electrical distribution system is moving toward a more decentralized, complex, and dynamic system. The system is experiencing a higher penetration of distributed energy resources, flexible resources, and active end-users, leading to an active distribution system. If these components are used actively, they can have a positive effect on the distribution system's reliability. This paper aims to investigate how a microgrid with renewable energy sources and energy storage might influence the reliability of electricity supply in a radially operated distribution system. The reliability is investigated from both the distribution system and the microgrid perspective with the application of different scenarios. The study includes an extensive sensitivity analysis. In this study, we use our developed reliability assessment tool for distribution networks. The tool is an open available software where the example network and datasets are embedded. The reliability assessment tool is based on Monte Carlo simulations where load flow calculations are included to capture the behavior of the system and system components. In this paper, the reliability tool is extended to include microgrids and microgrid controller opportunities that handle interaction strategies with the distribution network. The model is demonstrated on the IEEE 33-bus network. The result is confirmed through statistical testing showing the statistical significance of providing support from the microgrid on the distribution system's reliability.
Facilitating Satellite-Airborne-Balloon-Terrestrial Integration for Dynamic and Infrastructure-less Networks
Authors: Ahmad Alsharoa, Mohamed-Slim Alouini
Date: 2021-11-15 00:00:00
Tasks: Management

This magazine investigates the potential enhancement of the data throughput of ground users by integrating ground base stations (GBS) with air stations, such as balloon, airborne, and satellite. The objective is to establish dynamic bi-directional wireless services (i.e., uplink and downlink) for ground users in congested and remote areas. The proposed integration involves satellite, high-altitude platforms (HAPs), and tethered balloons (TBs) in the exosphere, stratosphere, and troposphere, respectively, for better altitude reuse coupled with emerging optical or other high-frequency directional transceivers. This will lead to a significant enhancement in scarce spectrum aggregate efficiency. However, the air stations deployment and resource managements in this integrated system faces difficulties. This article tackles resource management challenges by (i) providing wireless services to ground users in remote areas and connecting them with metropolitan and rural areas and (ii) employing HAPs equipped with free-space-optical communication modules as back-hauling backbones. Finally, we illustrate some numerical results to show the benefit of our proposed integrated system.
Discrete-Time Nonlinear Systems Identification with Probabilistic Safety and Stability Constraints
Authors: Iman Salehi, Tyler Taplin, Ashwin P. Dani
Date: 2021-11-14 00:00:00

This paper presents a discrete-time nonlinear system identification method while satisfying the stability and safety properties of the system with high probability. An Extreme Learning Machine (ELM) is used with a Gaussian assumption on the function reconstruction error. A quadratically constrained quadratic program (QCQP) is developed with probabilistic safety and stability constraints that are only required to be satisfied at sampled points inside the invariant region. The proposed method is validated using two simulation examples: a two degrees-of-freedom (DoF) robot manipulator with constraints on joint angles whose trajectories are guaranteed to remain inside a safe set and on motion trajectories data of a hand-drawn shape.
Unified stability criteria for perturbed LTV systems with unstable instantaneous dynamics
Authors: Shenyu Liu
Date: 2021-11-14 00:00:00

In this work the stability of perturbed linear time-varying systems is studied. The main features of the problem are threefold. Firstly, the time-varying dynamics is not required to be continuous but allowed to have jumps. Also the system matrix is not assumed to be always Hurwitz. In addition, there is nonlinear time-varying perturbation which may be persistent. We first propose several mild regularity assumptions, under which the total variations of the system matrix and its abscissa are well-defined over arbitrary time interval. We then state our main result of the work, which requires the combined assessment of the total variation of the system matrix, the measure when the system is not sufficiently "stable" and the estimate of the perturbation to be upper bounded by a function affine in time. When this condition is met, we prove that the neighborhood of the origin, whose size depends on the magnitude of the perturbation, is uniformly globally exponentially stable for the system. We make several remarks, connecting our results with the known stability theory from continuous linear time-varying systems and switched systems. Finally, a numerical example is included to further illustrate the application of the main result.
Jumping Fluid Models and Delay Stability of Max-Weight Dynamics under Heavy-Tailed Traffic
Authors: Arsalan SharifNassab, John N. Tsitsiklis
Date: 2021-11-14 00:00:00
Tasks: Open-Ended Question Answering, Scheduling

We say that a random variable is $light$-$tailed$ if moments of order $2+\epsilon$ are finite for some $\epsilon>0$; otherwise, we say that it is $heavy$-$tailed$. We study queueing networks that operate under the Max-Weight scheduling policy, for the case where some queues receive heavy-tailed and some receive light-tailed traffic. Queues with light-tailed arrivals are often delay stable (that is, expected queue sizes are uniformly bounded over time) but can also become delay unstable because of resource-sharing with other queues that receive heavy-tailed arrivals. Within this context, and for any given "tail exponents" of the input traffic, we develop a necessary and sufficient condition under which a queue is robustly delay stable, in terms of $jumping$ $fluid$ models - an extension of traditional fluid models that allows for jumps along coordinates associated with heavy-tailed flows. Our result elucidates the precise mechanism that leads to delay instability, through a coordination of multiple abnormally large arrivals at possibly different times and queues and settles an earlier open question on the sufficiency of a particular fluid-based criterion. Finally, we explore the power of Lyapunov functions in the study of delay stability.
Eco-Coasting Strategies Using Road Grade Preview: Evaluation and Online Implementation Based on Mixed Integer Model Predictive Control
Authors: Yongjun Yan, Nan Li, Jinlong Hong, Bingzhao Gao, Hong Chen, Jing Sun, Ziyou Song
Date: 2021-11-14 00:00:00
Tasks: Model Predictive Control

Coasting has been widely used in the eco-driving guidelines to reduce fuel consumption by profiting from kinetic energy. However, the comprehensive comparison between different coasting strategies and online performance of the eco-coasting strategy using road grade preview are still unclear because of the oversimplification and the integer variable in the optimal control problems. Herein, two different coasting strategies (fuel cut-off and engine start/stop) are proposed to reveal the potential benefit of eco-coasting using the road grade preview. Engine drag torque and energy cost used for engine restart are considered in the modeling to give a fair evaluation of the offline and online performance. The offline performance of these two coasting methods is evaluated through dynamic programming (DP) under various driving scenarios with different slope profiles. Offline simulation shows that the engine start/stop method outperforms the fuel cut-off method in terms of fuel consumption and travel time by getting rid of the engine drag torque. Then, online performance of these two coasting methods is evaluated using Mixed Integer Model Predictive Control (MIMPC). A novel operational constraint on the minimum off steps is added in the MIMPC formulation to avoid frequent switch of the integer variables which represent the fuel cut-off and the engine start/stop mechanism. Simulation results show that, for both fuel cut-off and engine start/stop coasting methods, the MPC controller reduces fuel consumption to a level comparable to DP without sacrificing the travel time.
Integrating Counter-UAS Systems into the UTM System for Reliable Decision Making
Authors: Abdulhadi, Shoufan, Ruba, Alkadi
Date: 2021-11-14 00:00:00
Tasks: Decision Making, Management

Despite significant progress, the deployment of UAV technology in commercial and civil applications is still lagging. This is essentially due to the risks associated with drone flights and the lack of coordinated technologies that would mitigate these risks. While Unmanned Aircraft System Traffic Management systems (UTM) are being developed worldwide to enable safe operation, the counter-drone technology operates on an all-enemy basis and regards any sighted drone as a threat. This situation is essentially caused by the lack of information exchange between stakeholders. Without the exchange of relevant information, a counter-drone system can misclassify drones and initiate erroneous interdiction procedures. This paper proposes a system that integrates counter-drone technology into the UTM system for information exchange and coordination using a set of clarification protocols towards accountable response to sighted drones. The system functionality and performance were evaluated by simulation.
Simultaneous estimation of parameters and the state of an optical parametric oscillator system
Authors: Qi Yu, Shota Yokoyama, Daoyi Dong, David McManus, Hidehiro Yonezawa
Date: 2021-11-14 00:00:00

In this paper, we consider the filtering problem of an optical parametric oscillator (OPO). The OPO pump power may fluctuate due to environmental disturbances, resulting in uncertainty in the system modeling. Thus, both the state and the unknown parameter may need to be estimated simultaneously. We formulate this problem using a state-space representation of the OPO dynamics. Under the assumption of Gaussianity and proper constraints, the dual Kalman filter method and the joint extended Kalman filter method are employed to simultaneously estimate the system state and the pump power. Numerical examples demonstrate the effectiveness of the proposed algorithms.
Stability Analysis of Nash Equilibrium for 2-Agent Loss-Aversion-Based Noncooperative Switched Systems
Authors: Yuyue Yan, Tomohisa Hayakawa
Date: 2020-12-14 00:00:00
Tasks: Sensitivity

The stability property of the loss-aversion-based noncooperative switched systems with quadratic payoffs is investigated. In this system, each agent adopts the lower sensitivity parameter in the myopic pseudo-gradient dynamics for the case of losing utility than gaining utility, and both systems' dynamics and switching events (conditions) are depending on agents' payoff functions. Sufficient conditions under which agents' state converges towards the Nash equilibrium are derived in accordance with the location of the Nash equilibrium. In the analysis, the mode transition sequence and interesting phenomena which we call flash switching instants are characterized. Finally, we present several numerical examples to illustrate the properties of our results.
Estimation of Dynamical Systems in Noisy Conditions and with Constraints
Authors: Krishan Mohan Nagpal
Date: 2020-11-05 00:00:00

When measurements from dynamical systems are noisy, it is useful to have estimation algorithms that have low sensitivity to measurement noises and outliers. In the first set of results described in this paper we obtain optimal estimators for linear dynamical systems with $\epsilon$ insensitive loss functions. The $\epsilon$ insensitive loss function, which is often used in Support Vector Machines, provides greater robustness when the measurements are biased and very noisy as the algorithm tolerates small errors in prediction which in turn makes the estimates less sensitive to measurement noises. Apart from $\epsilon$ insensitive quadratic loss function, estimation algorithms are also derived for $\epsilon$ insensitive Huber M loss function which provides robustness in presence of both small noises as well as outliers. Robustness in presence of outliers is achieved with Huber cost function based estimator as the error penalty function switches from quadratic to linear for errors beyond certain threshold. The second set of results in the paper describe algorithms for estimation when apart from general description of dynamics of the system, one also has additional information about states and exogenous signals such as known range of some states or prior information about the maximum magnitude of noises/disturbances. While the proposed approaches have similarities to Kalman-Bucy or $\mathcal{H}_2$ smoothing algorithm, the algorithms are not linear in measurements but are easily implemented as optimal estimates are obtained by solving a standard quadratic optimization problem with linear constraints. For all cases, algorithms are proposed not only for filtering and smoothing but also for prediction of future states.
Optimal Planning of Single-Port and Multi-Port Charging Stations for Electric Vehicles in Medium Voltage Distribution Networks
Authors: Biswarup Mukherjee, Fabrizio Sossan
Date: 2021-11-13 00:00:00

This paper describes a method based on mixed-integer linear programming to cost-optimally locate and size chargers for electric vehicles (EVs) in distribution grids as a function of the driving demand. The problem accounts for the notion of single-port chargers (SPCs), where a charger can interface one EV maximum, and multi-port chargers (MPCs), where the same charger can interface multiple EVs. The advantage of MPCs is twofold. First, multiple ports allow arbitraging the charging among multiple vehicles without requiring the drivers to plug and unplug EVs. Second, the charger's power electronics is not sized for the total number of charging ports, enabling cost savings when the grid constraints are bottleneck of the problem. The proposed method can account for different charger typologies, such as slow and fast chargers, and model the drivers' flexibility of plugging and unplugging their EVs. Simulation results from a synthetic case study show that implementing MPCs is beneficial over both SPCs and drivers' flexibility in terms of total investments required for the charging infrastructure.