pwc-archive/papers-with-abstracts huggingface dataset:
Split Happens! Imprecise and Negative Information in Gaussian Mixture Random Finite Set Filtering
Authors: Keith A. LeGrand, Silvia Ferrari
Date: 2022-07-22 00:00:00
Tasks: Object, Object Tracking, State Estimation

In object tracking and state estimation problems, ambiguous evidence such as imprecise measurements and the absence of detections can contain valuable information and thus be leveraged to further refine the probabilistic belief state. In particular, knowledge of a sensor's bounded field-of-view can be exploited to incorporate evidence of where an object was not observed. This paper presents a systematic approach for incorporating knowledge of the field-of-view geometry and position and object inclusion/exclusion evidence into object state densities and random finite set multi-object cardinality distributions. The resulting state estimation problem is nonlinear and solved using a new Gaussian mixture approximation based on recursive component splitting. Based on this approximation, a novel Gaussian mixture Bernoulli filter for imprecise measurements is derived and demonstrated in a tracking problem using only natural language statements as inputs. This paper also considers the relationship between bounded fields-of-view and cardinality distributions for a representative selection of multi-object distributions, which can be used for sensor planning, as is demonstrated through a problem involving a multi-Bernoulli process with up to one-hundred potential objects.
ALTO: A Large-Scale Dataset for UAV Visual Place Recognition and Localization
Authors: Ivan Cisneros, Peng Yin, Ji Zhang, Howie Choset, Sebastian Scherer
Date: 2022-07-19 00:00:00
Tasks: Benchmarking, Image Registration, Visual Odometry, Visual Place Recognition

We present the ALTO dataset, a vision-focused dataset for the development and benchmarking of Visual Place Recognition and Localization methods for Unmanned Aerial Vehicles. The dataset is composed of two long (approximately 150km and 260km) trajectories flown by a helicopter over Ohio and Pennsylvania, and it includes high precision GPS-INS ground truth location data, high precision accelerometer readings, laser altimeter readings, and RGB downward facing camera imagery. In addition, we provide reference imagery over the flight paths, which makes this dataset suitable for VPR benchmarking and other tasks common in Localization, such as image registration and visual odometry. To the author's knowledge, this is the largest real-world aerial-vehicle dataset of this kind. Our dataset is available at https://github.com/MetaSLAM/ALTO.
Transplantation of Conversational Speaking Style with Interjections in Sequence-to-Sequence Speech Synthesis
Authors: Raul Fernandez, David Haws, Guy Lorberbom, Slava Shechtman, Alexander Sorin
Date: 2022-07-25 00:00:00
Tasks: Data Augmentation, Speech Synthesis, Style Transfer, text-to-speech, Text to Speech, Voice Conversion

Sequence-to-Sequence Text-to-Speech architectures that directly generate low level acoustic features from phonetic sequences are known to produce natural and expressive speech when provided with adequate amounts of training data. Such systems can learn and transfer desired speaking styles from one seen speaker to another (in multi-style multi-speaker settings), which is highly desirable for creating scalable and customizable Human-Computer Interaction systems. In this work we explore one-to-many style transfer from a dedicated single-speaker conversational corpus with style nuances and interjections. We elaborate on the corpus design and explore the feasibility of such style transfer when assisted with Voice-Conversion-based data augmentation. In a set of subjective listening experiments, this approach resulted in high-fidelity style transfer with no quality degradation. However, a certain voice persona shift was observed, requiring further improvements in voice conversion.
Unsupervised data selection for Speech Recognition with contrastive loss ratios
Authors: Chanho Park, Rehan Ahmad, Thomas Hain
Date: 2022-07-25 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

This paper proposes an unsupervised data selection method by using a submodular function based on contrastive loss ratios of target and training data sets. A model using a contrastive loss function is trained on both sets. Then the ratio of frame-level losses for each model is used by a submodular function. By using the submodular function, a training set for automatic speech recognition matching the target data set is selected. Experiments show that models trained on the data sets selected by the proposed method outperform the selection method based on log-likelihoods produced by GMM-HMM models, in terms of word error rate (WER). When selecting a fixed amount, e.g. 10 hours of data, the difference between the results of two methods on Tedtalks was 20.23% WER relative. The method can also be used to select data with the aim of minimising negative transfer, while maintaining or improving on performance of models trained on the whole training set. Results show that the WER on the WSJCAM0 data set was reduced by 6.26% relative when selecting 85% from the whole data set.
Low-Complexity Acoustic Echo Cancellation with Neural Kalman Filtering
Authors: Dong Yang, Fei Jiang, Wei Wu, Xuefei Fang, Muyong Cao
Date: 2022-07-23 00:00:00
Tasks: Acoustic echo cancellation

The Kalman filter has been adopted in acoustic echo cancellation due to its robustness to double-talk, fast convergence, and good steady-state performance. The performance of Kalman filter is closely related to the estimation accuracy of the state noise covariance and the observation noise covariance. The estimation error may lead to unacceptable results, especially when the echo path suffers abrupt changes, the tracking performance of the Kalman filter could be degraded significantly. In this paper, we propose the neural Kalman filtering (NKF), which uses neural networks to implicitly model the covariance of the state noise and observation noise and to output the Kalman gain in real-time. Experimental results on both synthetic test sets and real-recorded test sets show that, the proposed NKF has superior convergence and re-convergence performance while ensuring low near-end speech degradation comparing with the state-of-the-art model-based methods. Moreover, the model size of the proposed NKF is merely 5.3 K and the RTF is as low as 0.09, which indicates that it can be deployed in low-resource platforms.
Domain Adapting Deep Reinforcement Learning for Real-world Speech Emotion Recognition
Authors: Thejan Rajapakshe, Rajib Rana, Sara Khalifa, Bjorn W. Schuller
Date: 2022-07-07 00:00:00
Tasks: Cross-corpus, Deep Reinforcement Learning, Domain Adaptation, Emotion Recognition, reinforcement-learning, Reinforcement Learning (RL), Speech Emotion Recognition

Computers can understand and then engage with people in an emotionally intelligent way thanks to speech-emotion recognition (SER). However, the performance of SER in cross-corpus and real-world live data feed scenarios can be significantly improved. The inability to adapt an existing model to a new domain is one of the shortcomings of SER methods. To address this challenge, researchers have developed domain adaptation techniques that transfer knowledge learnt by a model across the domain. Although existing domain adaptation techniques have improved performances across domains, they can be improved to adapt to a real-world live data feed situation where a model can self-tune while deployed. In this paper, we present a deep reinforcement learning-based strategy (RL-DA) for adapting a pre-trained model to a real-world live data feed setting while interacting with the environment and collecting continual feedback. RL-DA is evaluated on SER tasks, including cross-corpus and cross-language domain adaption schema. Evaluation results show that in a live data feed setting, RL-DA outperforms a baseline strategy by 11% and 14% in cross-corpus and cross-language scenarios, respectively.
A Sample-Based Algorithm for Approximately Testing $r$-Robustness of a Digraph
Authors: Yuhao Yi, YuAn Wang, Xingkang He, Stacy Patterson, Karl H. Johansson
Date: 2022-07-25 00:00:00

One of the intensely studied concepts of network robustness is $r$-robustness, which is a network topology property quantified by an integer $r$. It is required by mean subsequence reduced (MSR) algorithms and their variants to achieve resilient consensus. However, determining $r$-robustness is intractable for large networks. In this paper, we propose a sample-based algorithm to approximately test $r$-robustness of a digraph with $n$ vertices and $m$ edges. For a digraph with a moderate assumption on the minimum in-degree, and an error parameter $0<\epsilon\leq 1$, the proposed algorithm distinguishes $(r+\epsilon n)$-robust graphs from graphs which are not $r$-robust with probability $(1-\delta)$. Our algorithm runs in $\exp(O((\ln{\frac{1}{\epsilon\delta}})/\epsilon^2))\cdot m$ time. The running time is linear in the number of edges if $\epsilon$ is a constant.
Hosting Capacity Approach
Authors: Sicheng Gong, Vladimir Ćuk, Tiago Castelo de Oliveira, J. F. G. Cobben
Date: 2022-07-25 00:00:00

This chapter proposes an evolved concept of "hosting capacity" using the term of "feasible region". Through converting the grid model into a more compact one, "hosting capacity region" not only is promising to further exploit the grid potential for power delivery, but also benefits grid operation feasibility investigation with concise formulas. The hosting capacity region assessment schemes are exploited as well. Facing the derived hosting capacity, originally complicated energy storage optimization problems can be represented algebraically, which is more efficient and friendly for computer processing. Case studies based on a 10.5kV Dutch grid have been implemented, eventually confirming the validity of relevant assessment and optimization methods.
Localization of Coordinated Cyber-Physical Attacks in Power Grids Using Moving Target Defense and Deep Learning
Authors: Yexiang Chen, Subhash Lakshminarayana, Fei Teng
Date: 2022-07-25 00:00:00
Tasks: Meta-Learning

As one of the most sophisticated attacks against power grids, coordinated cyber-physical attacks (CCPAs) damage the power grid's physical infrastructure and use a simultaneous cyber attack to mask its effect. This work proposes a novel approach to detect such attacks and identify the location of the line outages (due to the physical attack). The proposed approach consists of three parts. Firstly, moving target defense (MTD) is applied to expose the physical attack by actively perturbing transmission line reactance via distributed flexible AC transmission system (D-FACTS) devices. MTD invalidates the attackers' knowledge required to mask their physical attack. Secondly, convolution neural networks (CNNs) are applied to localize line outage position from the compromised measurements. Finally, model agnostic meta-learning (MAML) is used to accelerate the training speed of CNN following the topology reconfigurations (due to MTD) and reduce the data/retraining time requirements. Simulations are carried out using IEEE test systems. The experimental results demonstrate that the proposed approach can effectively localize line outages in stealthy CCPAs.
Cyber-Resilient Frequency Control of Power Grids with Energy Storage Systems
Authors: Jairo Giraldo, Masood Parvania
Date: 2022-07-25 00:00:00

The integration of synchronous generators and energy storage systems operated through communication networks introduces new challenges and vulnerabilities to the electric grid, where cyber attacks can corrupt sensor measurements or control inputs and interrupt functions such as frequency regulation. This paper proposes a defense methodology for the design of resilient operating constraints imposed on each generation and storage unit in order to prevent any attack sequence from driving the system's frequency to unsafe conditions. The resilient operating constraints are found by using ellipsoidal approximations of the reachable set of the power system, leading to a convex optimization problem with linear matrix inequalities. Numerical results in a single-area power system with synchronous generation and energy storage demonstrate how the resilient constraints provide security guarantees against any type of attack affecting frequency measurements or controller setpoints.
Moving-Horizon State Estimation for Power Networks and Synchronous Generators
Authors: Milos Katanic, John Lygeros, Gabriela Hug
Date: 2022-07-25 00:00:00
Tasks: State Estimation

Power network and generators state estimation are usually tackled as separate problems. We propose a dynamic scheme for the simultaneous estimation of the network and the generator states. The estimation is formulated as an optimization problem on a moving-horizon of past observations. The framework is a generalization of static state estimation; it can handle incomplete model knowledge and does not require static network observability by PMUs. The numerical results show an improved estimation accuracy compared to static state estimation. Moreover, accurate estimation of the internal states of generators without PMUs on their terminals can be achieved. Finally, we highlight the capability of the proposed estimator to detect and identify bad data.
Koopman Form of Nonlinear Systems with Inputs
Authors: Lucian Cristian Iacob, Roland Tóth, Maarten Schoukens
Date: 2022-07-25 00:00:00
Tasks: Form, Model Predictive Control

The Koopman framework proposes a linear representation of finite-dimensional nonlinear systems through a generally infinite-dimensional globally linear embedding. Originally, the Koopman formalism has been derived for autonomous systems. In applications for systems with inputs, generally a linear time invariant (LTI) form of the Koopman model is assumed, as it facilitates the use of control techniques such as linear quadratic regulation and model predictive control. However, it can be easily shown that this assumption is insufficient to capture the dynamics of the underlying nonlinear system. Proper theoretical extension for actuated continuous-time systems with a linear or a control-affine input has been worked out only recently, however extensions to discrete-time systems and general continuous-time systems have not been developed yet. In the present paper, we systematically investigate and analytically derive lifted forms under inputs for a rather wide class of nonlinear systems in both continuous and discrete time. We prove that the resulting lifted representations give Koopman models where the state transition is linear, but the input matrix becomes state-dependent (state and input-dependent in the discrete-time case), giving rise to a specially structured linear parameter-varying (LPV) description of the underlying system. We also provide error bounds on how much the dependency of the input matrix contributes to the resulting representation and how well the system behaviour can be approximated by an LTI Koopman representation. The introduced theoretical insight greatly helps for performing proper model structure selection in system identification with Koopman models as well as making a proper choice for LTI or LPV techniques for the control of nonlinear systems through the Koopman approach.
The Quantum Advantage in Decentralized Control
Authors: Shashank A. Deshpande, Ankur A. Kulkarni
Date: 2022-07-25 00:00:00

It is known in the context of decentralised control that there exist control strategies consistent with the requirements of a given information structure, yet physically unimplementable through any amount of passive common randomness. This imposes a natural set of limitations on what is achievable through common randomness in both cooperative and competitive settings. We show that it is possible to breach these limitations with the use of quantum-physical architectures. In particular, we present a class of stochastic strategies that leverage quantum entanglement to produce strategic distributions which compose a strict superclass of strategies implemented through passive common randomness. We investigate numerically, the `quantum advantage' offered by this new class over a parametric family of cooperative decision problems with static information structure. We demonstrate through variations across the parametric family that fundamental decision theoretic elements such as information and the cost determine the manifestation of quantum advantage in a given control problem. Our work motivates a novel decision and control paradigm with an enlarged space of control policies achievable by means of quantum architectures.
Unleashing the potential of price-based congestion management schemes: a unifying approach to compare alternative models under multiple objectives
Authors: Ennio Cascetta, Marcello Montanino
Date: 2022-07-25 00:00:00
Tasks: Management

A wide range of price-based congestion management schemes were proposed in the literature ranging from marginal cost road pricing to trip based multimodal pricing. The underlying models were formulated under different theoretical assumptions and with varying, and sometimes conflicting objectives. This paper presents a unifying framework under which different approaches can be compared based on their respective assumptions. The unifying modelling framework is referred to as trip pricing model, which extends path-differentiated pricing to multimodal paths, i.e., trips on whatever mode or combinations of modes, and generalizes road pricing schemes (which are shown to be a special case). By setting both positive (tolls) and negative (incentives) prices, revenue-neutral schemes are also shown to be special cases, with no a-priori assumption on which paths/modes to toll/incentivize. For model comparison, the pricing design problem is formulated in a multi-objective optimization framework, which combines traffic efficiency, environmental sustainability, users' acceptance, social and spatial equity, as pricing objectives. The trip pricing scheme is compared with traditional road pricing schemes, and with their revenue-neutral variants. First-best and second-best pricing schemes, designed in single- and multi-objective optimization frameworks, are compared on the Nguyen-Dupuis network. Results suggest a vast potential for multi-objective trip congestion pricing over single-objective road pricing. In addition, the application of both positive and negative prices is shown to significantly increase the expected acceptance of pricing schemes and to preserve efficiency and sustainability objectives. The results should promote the design of more effective pricing policies, i.e., set of pricing rules easier to communicate, based on now available technologies of passengers' tracking and pricing.
Non-cascaded Control Barrier Functions for the Safe Control of Quadrotors
Authors: Weifeng Zeng, Huanhui Cao, Wenjie Lu, Hao Xiong
Date: 2022-07-25 00:00:00

Researchers have developed various cascaded controllers and non-cascaded controllers for the navigation and control of quadrotors in recent years. It is vital to ensure the safety of a quadrotor both in normal state and in abnormal state if a controller tends to make the quadrotor unsafe. To this end, this paper proposes a non-cascaded Control Barrier Function (CBF) for a quadrotor controlled by either cascaded controllers or a non-cascaded controller. Incorporated with a Quadratic Programming (QP), the non-cascaded CBF can simultaneously regulate the magnitude of the total thrust and the torque of the quadrotor determined a controller, so as to ensure the safety of the quadrotor both in normal state and in abnormal state. The non-cascaded CBF establishes a non-conservative forward invariant safe region, in which the controller of a quadrotor is fully or partially effective in the navigation or the pose control of the quadrotor. The non-cascaded CBF is applied to a quadrotor performing trajectory tracking and a quadrotor performing aggressive roll maneuvers in simulations to evaluate the effectiveness of the non-cascaded CBF.
Distributed Coordination of Charging Stations with Shared Energy Storage in a Distribution Network
Authors: Dongxiang Yan, Yue Chen
Date: 2022-07-25 00:00:00
Tasks: Management

Electric vehicle (EV) charging stations have experienced rapid growth, whose impacts on the power grid have become non-negligible. Though charging stations can install energy storage to reduce their impacts on the grid, the conventional "one charging station, one energy storage" method may be uneconomical due to the high upfront cost of energy storage. Shared energy storage can be a potential solution. However, effective management of charging stations with shared energy storage in a distribution network is challenging due to the complex coupling, competing interests, and information asymmetry between different agents. To address the aforementioned challenges, this paper first proposes an equilibrium model to characterize the interaction among charging stations, shared energy storage, and the distribution network. We prove that the equilibrium coincides with the centralized optimization result with trading prices equaling the value of dual variables at optimum. Then, to achieve the efficient equilibrium, a distributed coordination mechanism with a prediction and a correction step is developed to guide the behaviors of different agents with proof of convergence. Numerical experiments and comprehensive performance comparisons are conducted to validate the theoretical results and show the advantages of the proposed mechanism.
Optimal Utilization of Third-Party Demand Response Resources in Vertically Integrated Utilities: A Game Theoretic Approach
Authors: Sayyid Mohssen Sajjadi, Meng Wu
Date: 2022-07-25 00:00:00

This paper studies the optimal mechanisms for the vertically integrated utility to dispatch and incentivize the third-party demand response (DR) providers in its territory. A framework is proposed, with three-layer coupled Stackelberg and simultaneous games, to study the interactions and competitions among the profit-seeking process of the utility, the third-party DR providers, and the individual end users (EUs) in the DR programs. Two coupled single-leader-multiple-followers Stackelberg games with a three-layer structure are proposed to capture the interactions among the utility (modeled in the upper layer), the third-party DR providers (modeled in the middle layer), and the EUs in each DR program (modeled in the lower layer). The competitions among the EUs in each DR program is captured through a non-cooperative simultaneous game. An inconvenience cost function is proposed to model the DR provision willingness and capacity of different EUs. The Stackelberg game between the middle-layer DR provider and the lower-layer EUs is solved by converting the original bi-level programming to a singlelevel programming. This converted single-level programming is embedded in an iterative algorithm toward solving the entire coupled games framework. Case studies are performed on IEEE 34-bus and IEEE 69-bus test systems to illustrate the application of the proposed framework.