pwc-archive/papers-with-abstracts huggingface dataset:
BART based semantic correction for Mandarin automatic speech recognition system
Authors: Yun Zhao, Xuerui Yang, Jinchao Wang, Yongyu Gao, Chao Yan, Yuanfu Zhou
Date: 2021-03-26 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Language Modeling, Language Modelling, speech-recognition, Speech Recognition

Although automatic speech recognition (ASR) systems achieved significantly improvements in recent years, spoken language recognition error occurs which can be easily spotted by human beings. Various language modeling techniques have been developed on post recognition tasks like semantic correction. In this paper, we propose a Transformer based semantic correction method with pretrained BART initialization, Experiments on 10000 hours Mandarin speech dataset show that character error rate (CER) can be effectively reduced by 21.7% relatively compared to our baseline ASR system. Expert evaluation demonstrates that actual improvement of our model surpasses what CER indicates.
Dynamic Matching Markets in Power Grid: Concepts and Solution using Deep Reinforcement Learning
Authors: Majid Majidi, Deepan Muthirayan, Masood Parvania, Pramod P. Khargonekar
Date: 2021-04-12 00:00:00
Tasks: Deep Reinforcement Learning, Diversity, reinforcement-learning, Reinforcement Learning (RL)

Traditional bulk load flexibility options, such as load shifting and load curtailment, for managing uncertainty in power markets limit the diversity of options and ignore the preferences of the individual loads, thus reducing efficiency and welfare. This paper proposes an alternative to bulk load flexibility options for managing uncertainty in power markets: a reinforcement learning based dynamic matching market. We propose a novel hybrid learning-based model for maximizing social welfare in the dynamic matching market. The key features of our model is a fixed rule-based function and a learnable component that can be trained by data gathered online with no prior knowledge or expert supervision. The output of the learnable component is a probability distribution over the matching decisions for the individual customers. The proposed hybrid model enables the learning algorithm to find an effective matching policy that simultaneously satisfies the customers' servicing preferences. The simulations show that the learning algorithm learns an effective matching policy for different generation-consumption profiles and exhibits better performance compared to standard online matching heuristics such as Match on Arrival, Match to the Highest, and Match to the Earliest Deadline policies.
A Dynamic Response Recovery Framework Using Ambient Synchrophasor Data
Authors: Shaohui Liu, Hao Zhu, Vassilis Kekatos
Date: 2021-04-12 00:00:00

Wide-area dynamic studies are of paramount importance to ensure the stability and reliability of power grids. The rising deployment synchrophasor and other sensing technologies has made data-driven modeling and analysis possible using the synchronized fast-rate dynamic measurements. This paper presents a general model-free framework of inferring the grid dynamic responses using the ubiquitous ambient data collected during normal grid operations. Building upon the second-order dynamic model, we have established the connection from the cross-correlation of various types of angle, frequency, and line flow data at any two locations, to their corresponding dynamic responses. The theoretical results enabled a fully data-driven framework for estimating the latter using real-time ambient data. Numerical results using the WSCC 9-bus system and a synthetic 2000-bus Texas system have demonstrated the effectiveness of proposed approaches for dynamic modeling of realistic power systems.
Deep Reinforcement Learning Based Controller for Active Heave Compensation
Authors: Shrenik Zinage, Abhilash Somayajula
Date: 2021-04-12 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Heave compensation is an essential part in various offshore operations. It is used in various applications, which include on-loading or off-loading systems, offshore drilling, landing helicopter on oscillating structures, and deploying and retrieving manned submersibles. In this paper, a reinforcement learning (RL) based controller is proposed for active heave compensation using a deep deterministic policy gradient (DDPG) algorithm. A DDPG algorithm which is a model-free, online reinforcement learning method, is adopted to capture the experience of the agent during the training trials. The simulation results demonstrate up to 10 % better heave compensation performance of RL controller as compared to a tuned Proportional-Derivative Control. The performance of the proposed method is compared with respect to heave compensation, offset tracking, disturbance rejection, and noise attenuation.
Stochastic Stability of Discrete-time Phase-coupled Oscillators over Uncertain and Random Networks
Authors: Matin Jafarian, Mohammad H. Mamduhi, Karl H. Johansson
Date: 2021-04-12 00:00:00

This article studies stochastic relative phase stability, i.e., stochastic phase-cohesiveness, of discrete-time phase-coupled oscillators. Stochastic phase-cohesiveness in two types of networks is studied. First, we consider oscillators coupled with 2{\pi}-periodic odd functions over underlying undirected graphs subject to both multiplicative and additive stochastic uncertainties. We prove stochastic phase-cohesiveness of the network with respect to two specific, namely in-phase and anti-phase, sets by deriving sufficient coupling conditions. We show the dependency of these conditions on the size of the mean values of additive and multiplicative uncertainties, as well as the sign of the mean values of multiplicative uncertainties. Furthermore, we discuss the results under a relaxation of the odd property of the coupling function. Second, we study an uncertain network in which the multiplicative uncertainties are governed by the Bernoulli process representing the well-known Erdos-Renyi network. We assume constant exogenous frequencies and derive sufficient conditions for achieving both stochastic phase-cohesive and phase-locked solutions, i.e., stochastic phase-cohesiveness with respect to the origin. For the latter case, where identical exogenous frequencies are assumed, we prove that any positive probability of connectivity leads to phase-locking. Thorough analyses are provided, and insights obtained from stochastic analysis are discussed, along with numerical simulations to validate the analytical results.
Optimal Network Topology of Multi-Agent Systems subject to Computation and Communication Latency (with proofs)
Authors: Luca Ballotta, Mihailo R. Jovanović, Luca Schenato
Date: 2021-01-25 00:00:00

We study minimum-variance feedback-control design for a networked control system with retarded dynamics, where inter-agent communication is subject to latency. We prove that such a design can be solved efficiently for circular formations and compute near-optimal control gains in closed form. We show that the centralized control architecture is in general suboptimal when the communication increase with the number of links, and propose a control-driven optimization of the network topology.
Prospect-theoretic Q-learning
Authors: Vivek S. Borkar, Siddharth Chandak
Date: 2021-04-12 00:00:00
Tasks: Q-Learning

We consider a prospect theoretic version of the classical Q-learning algorithm for discounted reward Markov decision processes, wherein the controller perceives a distorted and noisy future reward, modeled by a nonlinearity that accentuates gains and underrepresents losses relative to a reference point. We analyze the asymptotic behavior of the scheme by analyzing its limiting differential equation and using the theory of monotone dynamical systems to infer its asymptotic behavior. Specifically, we show convergence to equilibria, and establish some qualitative facts about the equilibria themselves.
On Thevenin-Norton and Maximum power transfer theorems
Authors: H. Narayanan, Hariharan Narayanan
Date: 2021-04-12 00:00:00

In this paper we show how to compute port behaviour of multiports which have port equations of the general form $B v_P - Q i_P = s,$ which cannot even be put into the hybrid form, indeed may have number of equations ranging from $0$ to $2n,$ where $n$ is the number of ports. We do this through repeatedly solving with different source inputs, a larger network obtained by terminating the multiport by its adjoint through a gyrator. The method works for linear multiports which are consistent for arbitrary internal source values and further have the property that the port conditions uniquely determine internal conditions. We also present the most general version of maximum power transfer theorem possible. This version of the theorem states that `stationarity' (derivative zero condition) of power transfer occurs when the multiport is terminated by its adjoint, provided the resulting network has a solution. If this network does not have a solution there is no port condition for which stationarity holds. This theorem does not require that the multiport has a hybrid immittance matrix.
Two families of indexable partially observable restless bandits and Whittle index computation
Authors: Nima Akbarzadeh, Aditya Mahajan
Date: 2021-04-12 00:00:00

We consider the restless bandits with general state space under partial observability with two observational models: first, the state of each bandit is not observable at all, and second, the state of each bandit is observable only if it is chosen. We assume both models satisfy the restart property under which we prove indexability of the models and propose the Whittle index policy as the solution. For the first model, we derive a closed-form expression for the Whittle index. For the second model, we propose an efficient algorithm to compute the Whittle index by exploiting the qualitative properties of the optimal policy. We present detailed numerical experiments for multiple instances of machine maintenance problem. The result indicates that the Whittle index policy outperforms myopic policy and can be close to optimal in different setups.
Priority Lists for Power System Investments: Locating Phasor Measurement Units
Authors: Ilgiz Murzakhanov, David Pozo
Date: 2021-04-11 00:00:00
Tasks: Decision Making

Power systems incrementally and continuously upgrade their components, such as transmission lines, reactive capacitors, or generating units. Decision-making tools often support the selection of the best set of components to upgrade. Optimization models are often used to support decision making at a given point in time. After certain time intervals, re-optimization is performed to find new components to add. In this paper, we propose a decision-making framework for incrementally updating power system components. This is an alternative approach to the classical sequential re-optimization decision making for an investment problem with modeled budget constraints. Our approach provides a priority list as a solution with a list of new components to upgrade. We show that i) our framework is consistent with the evolution of power system upgrades, and ii) in particular circumstances, both frameworks provide the same solution if the problem satisfies submodularity property. We have selected the problem of phasor measurement unit localization and compared the solution with the classical sequential re-optimization framework. For this particular problem, we show that the two approaches provide close results, while only our proposed algorithm is applicable in practice. The cases of 14 and 118 IEEE buses are used to illustrate the proposed methodology.
Experiment design for impulse response identification with signal matrix models
Authors: Andrea Iannelli, Mingzhou Yin, Roy S. Smith
Date: 2020-12-15 00:00:00

This paper formulates an input design approach for truncated infinite impulse response identification in the context of implicit model representations recently used as basis for data-driven simulation and control approaches. Precisely, the considered model consists of a linear combination of the columns of a data (or signal) matrix. An optimal combination for the case of noisy data was recently proposed using a maximum likelihood approach, and the objective here is to optimize the input entries of the data matrix such that the mean-square error matrix of the estimate is minimized. A least-norm problem is derived in terms of the optimality criteria typically considered in the experiment design literature. Numerical results showcase the improved estimation fit achieved with the optimized input.
Data-driven predictive control with estimated prediction matrices and integral action
Authors: P. C. N. Verheijen, G. R. Gonçalves da Silva, M. Lazar
Date: 2021-04-11 00:00:00
Tasks: Model Predictive Control, Position, Prediction

This paper presents a data-driven approach to the design of predictive controllers. The prediction matrices utilized in standard model predictive control (MPC) algorithms are typically constructed using knowledge of a system model such as, state-space or input-output models. Instead, we directly estimate the prediction matrices relating future outputs with current and future inputs from measured data, off-line. On-line, the developed data--driven predictive controller reduces to solving a quadratic program with a similar structure and complexity as linear MPC. Additionally, we develop a new procedure for estimating prediction matrices from data for predictive controllers with integral action, corresponding to the rate-based formulation of linear MPC. The effectiveness of the developed data-driven predictive controller is illustrated on position control of a linear motor model.
Nonlinear parameter-varying state-feedback design for a gyroscope using virtual control contraction metrics
Authors: Ruigang Wang, Patrick J. W. Koelwijn, Ian R. Manchester, Roland Tóth
Date: 2021-04-11 00:00:00

In this paper, we present a virtual control contraction metric (VCCM) based nonlinear parameter-varying (NPV) approach to design a state-feedback controller for a control moment gyroscope (CMG) to track a user-defined trajectory set. This VCCM based nonlinear stabilization and performance synthesis approach, which is similar to linear parameter-varying (LPV) control approaches, allows to achieve exact guarantees of exponential stability and $\mathcal{L}_2$-gain performance on nonlinear systems with respect to all trajectories from the predetermined set, which is not the case with the conventional LPV methods. Simulation and experimental studies conducted in both fully- and under-actuated operating modes of the CMG show effectiveness of this approach compared to standard LPV control methods.
MPC Controller Tuning using Bayesian Optimization Techniques
Authors: Qiugang Lu, Ranjeet Kumar, Victor M. Zavala
Date: 2020-09-29 00:00:00
Tasks: Bayesian Optimization

We present a Bayesian optimization (BO) framework for tuning model predictive controllers (MPC) of central heating, ventilation, and air conditioning (HVAC) plants. This approach treats the functional relationship between the closed-loop performance of MPC and its tuning parameters as a black-box. The approach is motivated by the observation that evaluating the closed-loop performance of MPC by trial-and-error is time-consuming (e.g., every closed-loop simulation can involve solving thousands of optimization problems). The proposed BO framework seeks to quickly identify the optimal tuning parameters by strategically exploring and exploiting the space of the tuning parameters. The effectiveness of the BO framework is demonstrated by using an MPC controller for a central HVAC plant using realistic data. Here, the BO framework tunes back-off terms for thermal storage tanks to minimize year-long closed-loop costs. Simulation results show that BO can find the optimal back-off terms by conducting 13 year-long simulations, which significantly reduces the computational burden of a naive grid search. We also find that the back-off terms obtained with BO reduce the closed-loop costs.
Improving the Feasibility of Moment-Based Safety Analysis for Stochastic Dynamics
Authors: Peter Du, Katherine Driggs-Campbell, Roy Dong
Date: 2021-04-11 00:00:00

Given a stochastic dynamical system modelled via stochastic differential equations (SDEs), we evaluate the safety of the system through characterizations of its exit time moments. We lift the (possibly nonlinear) dynamics into the space of the occupation and exit measures to obtain a set of linear evolution equations which depend on the infinitesimal generator of the SDE. Coupled with appropriate semidefinite positive matrix constraints, this yields a moment-based approach for the computation of exit time moments of SDEs with polynomial drift and diffusion dynamics. However, the existing moment approach suffers from drawbacks which impede its applicability to the analysis of higher dimensional physical systems. To apply the existing approach, the dynamics of the systems are limited to polynomials of the state - excluding a large majority of real world examples. Computational scalability is also poor as the dimensionality of the state increases, largely due to the combinatorial growth of the optimization program. In this paper, we propose changes to the existing moment method to make feasible the safety analysis of higher dimensional physical systems. The restriction to polynomial dynamics is lifted by using a state augmentation method which allows one to generate the evolution equations for a broader class of nonlinear stochastic systems. We then reformulate the constraints of the optimization to mitigate the computational limitations associated with an increase in state dimensionality. We employ our methodology on two example processes to characterize their safety via expected exit times and demonstrate the ability to handle multi-dimensional physical systems that were previously unsupported by the existing SDP method of moments.
Distributed Estimation over Directed Graphs Resilient to Sensor Spoofing
Authors: Shamik Bhattacharyya, Kiran Rokade, Rachel Kalpana Kalaimani
Date: 2021-04-10 00:00:00
Tasks: Adversarial Attack

This paper addresses the problem of distributed estimation of an unknown dynamic parameter by a multi-agent system over a directed communication network in the presence of an adversarial attack on the agents' sensors. The mode of attack of the adversaries is to corrupt the sensor measurements of some of the agents, while the communication and information processing capabilities of those agents remain unaffected. To ensure that all the agents, both normal as well as those under attack, are able to correctly estimate the parameter value, the Resilient Estimation through Weight Balancing (REWB) algorithm is introduced. The only condition required for the REWB algorithm to guarantee resilient estimation is that at any given point in time, less than half of the total number of agents are under attack. The paper discusses the development of the REWB algorithm using the concepts of weight balancing of directed graphs, and the consensus+innovations approach for linear estimation. Numerical simulations are presented to illustrate the performance of our algorithm over directed graphs under different conditions of adversarial attacks.
On Controllability and Persistency of Excitation in Data-Driven Control: Extensions of Willems' Fundamental Lemma
Authors: Yue Yu, Shahriar Talebi, Henk J. van Waarde, Ufuk Topcu, Mehran Mesbahi, Behçet Açıkmeşe
Date: 2021-02-05 00:00:00
Tasks: LEMMA, Model Predictive Control

Willems' fundamental lemma asserts that all trajectories of a linear time-invariant system can be obtained from a finite number of measured ones, assuming that controllability and a persistency of excitation condition hold. We show that these two conditions can be relaxed. First, we prove that the controllability condition can be replaced by a condition on the controllable subspace, unobservable subspace, and a certain subspace associated with the measured trajectories. Second, we prove that the persistency of excitation requirement can be relaxed if the degree of a certain minimal polynomial is tightly bounded. Our results show that data-driven predictive control using online data is equivalent to model predictive control, even for uncontrollable systems. Moreover, our results significantly reduce the amount of data needed in identifying homogeneous multi-agent systems.
Preemptive periodic epidemic control reduces life and healthcare system costs without aggravation of social and economic losses
Authors: Giuseppe De Nicolao, Marta Colaneri, Alessandro Di Filippo, Franco Blanchini, Paolo Bolzern, Patrizio Colaneri, Giulia Giordano, Raffaele Bruno
Date: 2021-04-06 00:00:00

Many countries are managing COVID-19 epidemic by switching between lighter and heavier restrictions. While an open-close and a close-open cycle have comparable socio-economic costs, the former leads to a much heavier burden in terms of deaths and pressure on the healthcare system. An empirical demonstration of the toll ensuing from procrastination was recently observed in Israel, where both cycles were enforced from late August to mid-December 2020, yielding some 1,600 deaths with open-close compared to 440 with close-open.
Artificial neural network as a universal model of nonlinear dynamical systems
Authors: Pavel V. Kuptsov, Anna V. Kuptsova, Nataliya V. Stankevich
Date: 2021-03-06 00:00:00
Tasks: Time Series, Time Series Analysis

We suggest a universal map capable to recover a behavior of a wide range of dynamical systems given by ODEs. The map is built as an artificial neural network whose weights encode a modeled system. We assume that ODEs are known and prepare training datasets using the equations directly without computing numerical time series. Parameter variations are taken into account in the course of training so that the network model captures bifurcation scenarios of the modeled system. Theoretical benefit from this approach is that the universal model admits using common mathematical methods without needing to develop a unique theory for each particular dynamical equations. Form the practical point of view the developed method can be considered as an alternative numerical method for solving dynamical ODEs suitable for running on contemporary neural network specific hardware. We consider the Lorenz system, the Roessler system and also Hindmarch-Rose neuron. For these three examples the network model is created and its dynamics is compared with ordinary numerical solutions. High similarity is observed for visual images of attractors, power spectra, bifurcation diagrams and Lyapunov exponents.
A review of artificial intelligence methods combined with Raman spectroscopy to identify the composition of substances
Authors: Liangrui Pan, Peng Zhang, Chalongrat Daengngam, Mitchai Chongcheawchamnan
Date: 2021-04-05 00:00:00

In general, most of the substances in nature exist in mixtures, and the noninvasive identification of mixture composition with high speed and accuracy remains a difficult task. However, the development of Raman spectroscopy, machine learning, and deep learning techniques have paved the way for achieving efficient analytical tools capable of identifying mixture components, making an apparent breakthrough in the identification of mixtures beyond the traditional chemical analysis methods. This article summarizes the work of Raman spectroscopy in identifying the composition of substances as well as provides detailed reviews on the preprocessing process of Raman spectroscopy, the analysis methods and applications of artificial intelligence. This review summarizes the work of Raman spectroscopy in identifying the composition of substances and reviews the preprocessing process of Raman spectroscopy, the analysis methods and applications of artificial intelligence. Finally, the advantages and disadvantages and development prospects of Raman spectroscopy are discussed in detail.