pwc-archive/papers-with-abstracts huggingface dataset:
A Foundation for Wireless Channel Prediction and Full Ray Makeup Estimation Using an Unmanned Vehicle
Authors: Chitra R. Karanam, Yasamin Mostofi
Date: 2022-02-20 00:00:00

In this paper, we consider the problem of wireless channel prediction, where we are interested in predicting the channel quality at unvisited locations in an area of interest, based on a small number of prior received power measurements collected by an unmanned vehicle in the area. We propose a new framework for channel prediction that can not only predict the detailed variations of the received power, but can also predict the detailed makeup of the wireless rays (i.e., amplitude, angle-of-arrival, and phase of all the incoming paths). More specifically, we show how an enclosure-based robotic route design ensures that the received power measurements at the prior measurement locations can be utilized to fully predict detailed ray parameters at unvisited locations. We then show how to first estimate the detailed ray parameters at the prior measurement route and then fully extend them to predict the detailed ray makeup at unvisited locations in the workspace. We experimentally validate our proposed framework through extensive real-world experiments in three different areas, and show that our approach can accurately predict the received channel power and the detailed makeup of the rays at unvisited locations in an area, considerably outperforming the state-of-the-art in wireless channel prediction.
The PCG-AIID System for L3DAS22 Challenge: MIMO and MISO convolutional recurrent Network for Multi Channel Speech Enhancement and Speech Recognition
Authors: Jingdong Li, Yuanyuan Zhu, Dawei Luo, Yun Liu, Guohui Cui, Zhaoxia Li
Date: 2022-02-21 00:00:00
Tasks: Denoising, Speech Denoising, Speech Enhancement, speech-recognition, Speech Recognition

This paper described the PCG-AIID system for L3DAS22 challenge in Task 1: 3D speech enhancement in office reverberant environment. We proposed a two-stage framework to address multi-channel speech denoising and dereverberation. In the first stage, a multiple input and multiple output (MIMO) network is applied to remove background noise while maintaining the spatial characteristics of multi-channel signals. In the second stage, a multiple input and single output (MISO) network is applied to enhance the speech from desired direction and post-filtering. As a result, our system ranked 3rd place in ICASSP2022 L3DAS22 challenge and significantly outperforms the baseline system, while achieving 3.2% WER and 0.972 STOI on the blind test-set.
L-SpEx: Localized Target Speaker Extraction
Authors: Meng Ge, Chenglin Xu, Longbiao Wang, Eng Siong Chng, Jianwu Dang, Haizhou Li
Date: 2022-02-21 00:00:00
Tasks: Target Speaker Extraction

Speaker extraction aims to extract the target speaker's voice from a multi-talker speech mixture given an auxiliary reference utterance. Recent studies show that speaker extraction benefits from the location or direction of the target speaker. However, these studies assume that the target speaker's location is known in advance or detected by an extra visual cue, e.g., face image or video. In this paper, we propose an end-to-end localized target speaker extraction on pure speech cues, that is called L-SpEx. Specifically, we design a speaker localizer driven by the target speaker's embedding to extract the spatial features, including direction-of-arrival (DOA) of the target speaker and beamforming output. Then, the spatial cues and target speaker's embedding are both used to form a top-down auditory attention to the target speaker. Experiments on the multi-channel reverberant dataset called MC-Libri2Mix show that our L-SpEx approach significantly outperforms the baseline system.
Can Social Robots Effectively Elicit Curiosity in STEM Topics from K-1 Students During Oral Assessments?
Authors: Alexander Johnson, Alejandra Martin, Marlen Quintero, Alison Bailey, Abeer Alwan
Date: 2022-02-19 00:00:00

This paper presents the results of a pilot study that introduces social robots into kindergarten and first-grade classroom tasks. This study aims to understand 1) how effective social robots are in administering educational activities and assessments, and 2) if these interactions with social robots can serve as a gateway into learning about robotics and STEM for young children. We administered a commonly-used assessment (GFTA3) of speech production using a social robot and compared the quality of recorded responses to those obtained with a human assessor. In a comparison done between 40 children, we found no significant differences in the student responses between the two conditions over the three metrics used: word repetition accuracy, number of times additional help was needed, and similarity of prosody to the assessor. We also found that interactions with the robot were successfully able to stimulate curiosity in robotics, and therefore STEM, from a large number of the 164 student participants.
LPC Augment: An LPC-Based ASR Data Augmentation Algorithm for Low and Zero-Resource Children's Dialects
Authors: Alexander Johnson, Ruchao Fan, Robin Morris, Abeer Alwan
Date: 2022-02-19 00:00:00
Tasks: Data Augmentation

This paper proposes a novel linear prediction coding-based data aug-mentation method for children's low and zero resource dialect ASR. The data augmentation procedure consists of perturbing the formant peaks of the LPC spectrum during LPC analysis and reconstruction. The method is evaluated on two novel children's speech datasets with one containing California English from the Southern CaliforniaArea and the other containing a mix of Southern American English and African American English from the Atlanta, Georgia area. We test the proposed method in training both an HMM-DNN system and an end-to-end system to show model-robustness and demonstrate that the algorithm improves ASR performance, especially for zero resource dialect children's task, as compared to common data augmentation methods such as VTLP, Speed Perturbation, and SpecAugment.
A Pricing Rule for Third-Party Platoon Coordination Service Provider
Authors: Ting Bai, Alexander Johansson, ShaoYuan Li, Jonas Mårtensson
Date: 2022-02-21 00:00:00
Tasks: Model Predictive Control

We model a platooning system including trucks and a third-party service provider that performs platoon coordination, distributes the platooning profit within platoons, and charges the trucks in exchange for its services. This paper studies one class of pricing rules, where the third-party service provider keeps part of the platooning profit each time a platoon is formed. Furthermore, we propose a platoon coordination solution based on distributed model predictive control in which the pricing rule is integrated. To evaluate the effect of the pricing on the platooning system, we perform a simulation over the Swedish road network. The simulation shows that the platooning rate and profit highly depend on the pricing. This suggests that pricing needs to be set carefully to obtain a satisfactory platooning system in the future.
Estimation of Evaporator Valve Sizes in Supermarket Refrigeration Cabinets
Authors: Kenneth Leerbeck, Peder Bacher, Christian Heerup, Henrik Madsen
Date: 2022-02-21 00:00:00
Tasks: regression, Time Series Analysis

In many applications, e.g. fault diagnostics and optimized control of supermarket refrigeration systems, it is important to determine the heat demand of the cabinets. This can easily be achieved by measuring the mass flow through each cabinet, however, that is expensive and not feasible in large-scale deployments. Therefore it is important to be able to estimate the valve sizes from the monitoring data, which is typically measured. The valve size is measured by an area, which can be used to calculate mass flow through the valve -- this estimated value is referred to as the valve constant. A novel method for estimating the cabinet evaporator valve constants is proposed in the present paper. It is demonstrated using monitoring data from a refrigeration system in a supermarket consisting of data sampled at a one-minute sampling rate, however it is shown that a sampling time of around 10-20 minutes is adequate for the method. Through thermodynamic analysis of a two-stage CO2 refrigeration system, a linear regression model for estimating valve constants was developed using time series data. The linear regression requires that transient dynamics are not present in the data, which depends on multiple factors e.g. the sampling time. If dynamics are not modeled it can be detected by a significant auto-correlation of the residuals. In order to include the dynamics in the model, an Auto-Regressive Moving Average model with eXogenous variables (ARMAX) was applied, and it is shown how it effectively eliminates the auto-correlation and provides more unbiased estimates, as well as improved the accuracy estimates. Furthermore, it is shown that the sample time has a huge impact on the valve estimates. Thus, a method for selecting the optimal sampling time is introduced. It works individually for each of the evaporators, by exploring their respective frequency spectrum.
On the Distributed Estimation from Relative Measurements: a Graph-Based Convergence Analysis
Authors: Marco Fabris, Giulia Michieletto, Angelo Cenedese
Date: 2022-02-21 00:00:00
Tasks: State Estimation

For a multi-agent system state estimation resting upon noisy measurements constitutes a problem related to several application scenarios. Adopting the standard least-squares approach, in this work we derive both the (centralized) analytic solution to this issue and two distributed iterative schemes, which allow to establish a connection between the convergence behavior of consensus algorithm toward the optimal estimate and the theory of the stochastic matrices that describe the network system dynamics. This study on the one hand highlights the role of the topological links that define the neighborhood of agent nodes, while on the other allows to optimize the convergence rate by easy parameter tuning. The theoretical findings are validated considering different network topologies by means of numerical simulations.
Optimal Time-Invariant Formation Tracking for a Second-Order Multi-Agent System
Authors: Marco Fabris, Angelo Cenedese, John Hauser
Date: 2022-02-21 00:00:00
Tasks: Position

Given a multi-agent linear system, we formalize and solve a trajectory optimization problem that encapsulates trajectory tracking, distance-based formation control and input energy minimization. To this end, a numerical projection operator Newton's method is developed to find a solution by the minimization of a cost functional able to capture all these different tasks. To stabilize the formation, a particular potential function has been designed, allowing to obtain specified geometrical configurations while the barycenter position and velocity of the system follows a desired trajectory.
MILP-based optimal day-ahead scheduling for system-centric CEMS supporting different types of homes and energy trading
Authors: Huy Truong Dinh, Dongwan Kim, Daehee Kim
Date: 2022-02-21 00:00:00
Tasks: energy management, energy trading, Management, Scheduling

Optimal day-ahead scheduling for a system-centric community energy management system (CEMS) is proposed to provide economic benefits and user comfort of energy management at the community level. Our proposed community includes different types of homes and allows prosumers to trade energy locally using mid-market rate pricing. A mathematical model of the community is constructed and the optimization problem of this model is transformed into an MILP problem that can be solved in a short time. By solving this MILP problem, the optimization of the overall energy cost of the community and satisfaction of the thermal comfort at every home are achieved. For comparison, we also establish two different scenarios for the same community: a prosumer-centric CEMS and no CEMS. The simulation results demonstrate that the overall energy cost of the community with the system-centric CEMS is the smallest among the three scenarios and is only half that of the community with the prosumer-centric CEMS. Moreover, by using linear transformation, the computational time of the optimization problem of the proposed system-centric CEMS is only 118.2 s for a 500-home community, which is a short time for day-ahead scheduling of a community.
Formal Analysis of the Sampling Behaviour of Stochastic Event-Triggered Control
Authors: Giannis Delimpaltadakis, Luca Laurenti, Manuel Mazo Jr
Date: 2022-02-21 00:00:00

Analyzing Event-Triggered Control's (ETC) sampling behaviour is of paramount importance, as it enables formal assessment of its sampling performance and prediction of its sampling patterns. In this work, we formally analyze the sampling behaviour of stochastic linear periodic ETC (PETC) systems by computing bounds on associated metrics. Specifically, we consider functions over sequences of state measurements and intersampling times that can be expressed as average, multiplicative or cumulative rewards, and introduce their expectations as metrics on PETC's sampling behaviour. We compute bounds on these expectations, by constructing appropriate Interval Markov Chains equipped with suitable reward structures, that abstract stochastic PETC's sampling behaviour. Our results are illustrated on a numerical example, for which we compute bounds on the expected average intersampling time and on the probability of triggering with the maximum possible intersampling time in a finite horizon.
A Lifting Approach to Learning-Based Self-Triggered Control with Gaussian Processes
Authors: Wang Zhijun, Kazumune Hashimoto, Wataru Hashimoto, Shigemasa Takai
Date: 2022-02-21 00:00:00
Tasks: Gaussian Processes

This paper investigates the design of self-triggered control for networked control systems (NCS), where the dynamics of the plant is unknown apriori. To deal with the nature of the self-triggered control, in which state measurements are transmitted to the controller a-periodically, we propose to lift the continuous-time dynamics to a novel dynamical model by taking an inter-event time as an additional input, and then, the lifted model is learned by the Gaussian processes (GP) regression. Moreover, we propose a learning-based approach, in which a self-triggered controller is learned by minimizing a cost function, such that it can take inter-sample behavior into account. By employing the lifting approach, we can utilize a gradient-based policy update as an efficient method to optimize both control and communication policies. Finally, we summarize the overall algorithm and provide a numerical simulation to illustrate the effectiveness of the proposed approach.
Entropy of Generating Series for Nonlinear Input-Output Systems and Their Interconnections
Authors: W. Steven Gray
Date: 2022-02-21 00:00:00

This paper has two main objectives. The first is to introduce a notion of entropy that is well suited for the analysis of nonlinear input-output systems that have a Chen-Fliess series representation. The latter is defined in terms of its generating series over a noncommutative alphabet. The idea is to assign an entropy to a generating series as an element of a graded vector space. The second objective is to describe the entropy of generating series originating from interconnected systems of Chen-Fliess series that arise in the context of control theory. It is shown that one set of interconnections can never increase entropy as defined here, while a second set has the potential to do so. The paper concludes with a brief introduction to an entropy ultrametric space and some open questions.
Distributed Strategies for Dynamic Coverage with Limited Sensing Capabilities
Authors: Marco Fabris, Angelo Cenedese
Date: 2022-02-21 00:00:00

In this work, it is presented the development of a novel distributed algorithm performing robotic coverage, clustering and dispatch around an event in static-obstacle structured environments without relying on metric information. Specifically, the aim is to account for the trade-off between local communication given by bearing visibility sensors installed on each agent involved, optimal deployment in closed unknown scenarios and focus of a group of agents on one point of interest. The particular targets of this study can be summarized as 1. the computation, under certain topological assumptions, of a lower bound for the number of required agents, which are provided by a realistic geometric model (e.g. a round shape) to emphasize physical limitations; 2. the minimization of the number of nodes and links maintaining a distributed approach over a connected communication graph; 3. the identification of an activation cluster around an event with a radial decreasing intensity, sensed by each agent; 4. the attempt to send the agents belonging to the cluster towards the most intense point in the scenario by minimizing a weighted isoperimetric functional.
Tuning of passivity-based controllers for mechanical systems
Authors: Carmen Chan-Zheng, Pablo Borja, Jacquelien M. A. Scherpen
Date: 2022-02-21 00:00:00

This manuscript describes several approaches to tune the parameters of a class of passivity-based controllers for standard nonlinear mechanical systems. In particular, we are interested in controllers that preserve the mechanical system structure in closed-loop. To this end, first, we provide tuning rules for stabilization, i.e., the rate of convergence (exponential stability) and stability margin (input-to-state stability). Then, we provide guidelines to remove the overshoot while prescribing the rise time. Additionally, we propose a methodology to tune the gyroscopic-related parameters. We also provide remarks on the damping phenomena to facilitate the practical implementation of our approaches. We conclude this paper with experimental results obtained from applying our tuning rules to an underactuated and a fully-actuated mechanical configuration.
Integrated Fault Diagnosis and Control Design for DER Inverters using Machine Learning Methods
Authors: Forouzan Fallah, Amin Ramezani, Ali Mehrizi-Sani
Date: 2022-02-21 00:00:00
Tasks: BIG-bench Machine Learning, Fault Detection, Fault Diagnosis, Time Series, Time Series Analysis

This paper employs a supervised machine learning (ML) algorithm to propose an integrated fault detection and diagnosis (FDD) and fault-tolerant control (FTC) strategy to detect, diagnose, and classify the grid faults and correct the input voltage before affecting the grid-connected distributed energy resources (DER) inverters. This controller can mitigate the impact of grid faults on inverters by predicting and modifying the time series of their input voltage. Simulation results show the effectiveness of the proposed controller and evaluate its operating performance.
Performance Analysis of Optimally Coordinated Connected and Automated Vehicles in a Mixed Traffic Environment
Authors: Alejandra Valencia, A M Ishtiaque Mahbub, Andreas A. Malikopoulos
Date: 2022-02-20 00:00:00
Tasks: Trajectory Planning

Trajectory planning of connected and automated vehicles (CAVs) poses significant challenges in a mixed traffic environment due to the presence of human-driven vehicles (HDVs). In this paper, we apply a framework that allows coordination of CAVs and HDVs traveling through a traffic corridor consisting of an on-ramp merging, a speed reduction zone, and a roundabout. We study the impact of different penetration rates of CAVs and traffic volumes on the efficiency of the corridor. We provide extensive simulation results and report on the benefits in terms of total travel time and fuel economy.
Microwave Sensing of Elemental Sulfur Deposition in Gas Pipelines
Authors: Musab G. Magam, Hussein Attia, Khurram Karim Qureshi, Sharif I. M. Sheikh
Date: 2022-02-20 00:00:00

A non-intrusive conformal electromagnetic-based monitoring of elemental sulfur deposition within a natural gas-carrying pipeline is presented. The deposited sulfur behaves as a superstrate layer above a sensing microstrip patch antenna that is optimally placed on the inner wall of the gas pipeline. Increasing the superstrate thickness by the sulfur deposition alters the antenna resonance behavior, which can be monitored externally. The effect of uneven or bumpy sulfur deposition is studied. Sensing antennas positioned outside a plexiglass pipeline are also investigated to observe the change in the antenna impedance matching with accumulating sulfur superstrate. Lab-based measured results agreed well with the simulated responses using commercial electromagnetic software. The proposed low-cost and easy-to-implement detection technique exhibits an accurate estimation of the deposited sulfur thickness inside the natural gas-carrying pipelines.
Analysis and optimization of a novel energy storage flywheel for improved energy capacity
Authors: Xiaojun Li, Alan Palazzolo
Date: 2022-02-20 00:00:00

Kinetic/Flywheel energy storage systems (FESS) have re-emerged as a vital technology in many areas such as smart grid, renewable energy, electric vehicle, and high-power applications. FESSs are designed and optimized to have higher energy per mass (specific energy) and volume (energy density). Prior research, such as the use of high-strength materials and the reduction of stress concentration, primarily focused on designing and optimizing the rotor itself. However, a modern FESS includes other indispensable components such as magnetic bearings and a motor/generator that requires a shaft. The shaft significantly impacts the flywheel design. This paper investigates several typical flywheel designs and their stress analysis. A simplified analysis method is given for designing rotor-shaft assembly. It is found that the shaftless flywheel design approach can double the energy density level when compared to typical designs. The shaftless flywheel is further optimized using finite element analysis with the magnetic bearing and motor/generators' design considerations.
Multi-objective Distributed Optimization for Zonal Distribution System with Multi-Microgrids
Authors: Tao Xu, Lemeng Liang, Zuozheng Liu, Rujing Wang, Lingxu Guo
Date: 2022-02-20 00:00:00
Tasks: Distributed Optimization, Management

The issue of voltage variations caused by integration of renewables has been addressed in this paper through distributed management of Microgrids (MGs). The distribution network (DN) takes the network losses and voltage quality as objectives, an alternating direction method of multipliers (ADMM) with adaptive penalty modulation has been proposed to realize distributed optimization with improved convergence. On the basis of satisfying the optimization target on the system level, the individual MG manages the controllable resources inside the MG to minimize the operating cost on the lower level. The feasibility and effectiveness of the proposed method has been demonstrated on a modified IEEE 33-bus system.