pwc-archive/papers-with-abstracts huggingface dataset:
Ultrasensitive Textile Strain Sensors Redefine Wearable Silent Speech Interfaces with High Machine Learning Efficiency
Authors: Chenyu Tang, Muzi Xu, Wentian Yi, Zibo Zhang, Edoardo Occhipinti, Chaoqun Dong, Dafydd Ravenscroft, Sung-Min Jung, Sanghyo Lee, Shuo Gao, Jong Min Kim, Luigi G. Occhipinti
Date: 2023-11-27 00:00:00
Tasks: Efficient Neural Network

Our research presents a wearable Silent Speech Interface (SSI) technology that excels in device comfort, time-energy efficiency, and speech decoding accuracy for real-world use. We developed a biocompatible, durable textile choker with an embedded graphene-based strain sensor, capable of accurately detecting subtle throat movements. This sensor, surpassing other strain sensors in sensitivity by 420%, simplifies signal processing compared to traditional voice recognition methods. Our system uses a computationally efficient neural network, specifically a one-dimensional convolutional neural network with residual structures, to decode speech signals. This network is energy and time-efficient, reducing computational load by 90% while achieving 95.25% accuracy for a 20-word lexicon and swiftly adapting to new users and words with minimal samples. This innovation demonstrates a practical, sensitive, and precise wearable SSI suitable for daily communication applications.
Spatial Diarization for Meeting Transcription with Ad-Hoc Acoustic Sensor Networks
Authors: Tobias Gburrek, Joerg Schmalenstroeer, Reinhold Haeb-Umbach
Date: 2023-11-27 00:00:00
Tasks: Diversity

We propose a diarization system, that estimates "who spoke when" based on spatial information, to be used as a front-end of a meeting transcription system running on the signals gathered from an acoustic sensor network (ASN). Although the spatial distribution of the microphones is advantageous, exploiting the spatial diversity for diarization and signal enhancement is challenging, because the microphones' positions are typically unknown, and the recorded signals are initially unsynchronized in general. Here, we approach these issues by first blindly synchronizing the signals and then estimating time differences of arrival (TDOAs). The TDOA information is exploited to estimate the speakers' activity, even in the presence of multiple speakers being simultaneously active. This speaker activity information serves as a guide for a spatial mixture model, on which basis the individual speaker's signals are extracted via beamforming. Finally, the extracted signals are forwarded to a speech recognizer. Additionally, a novel initialization scheme for spatial mixture models based on the TDOA estimates is proposed. Experiments conducted on real recordings from the LibriWASN data set have shown that our proposed system is advantageous compared to a system using a spatial mixture model, which does not make use of external diarization information.
Robustness-Guaranteed Observer-Based Control Strategy with Modularity for Cleantech EMLA-Driven Heavy-Duty Robotic Manipulator
Authors: Mehdi Heydari Shahna, Mohammad Bahari, Jouni Mattila
Date: 2023-11-27 00:00:00

This paper introduces an innovative observer-based modular control strategy in a class of n_a-degree-of-freedom (DoF) fully electrified heavy-duty robotic manipulators (HDRMs) to (1) guarantee robustness in the presence of uncertainties and disturbances, (2) address the complexities arising from several interacting mechanisms, (3) ensure uniformly exponential stability, and (4) enhance overall control performance. To begin, the dynamic model of HDRM actuation systems, which exploits the synergy between cleantech electromechanical linear actuators (EMLAs) and permanent magnet synchronous motors (PMSMs), is investigated. In addition, the reference trajectories of each joint are computed based on direct collocation with B-spline curves to extract the key kinematic and dynamic quantities of HDRMs. To guarantee robust tracking of the computed trajectories by the actual motion states, a novel control methodology, called robust subsystem-based adaptive (RSBA) control, is enhanced through an adaptive state observer. The RSBA control addresses inaccuracies inherent in motion, including modeling errors, non-triangular uncertainties, and both torque and voltage disturbances, to which the EMLA-driven HDRM is susceptible. Furthermore, this approach is presented in a unified generic equation format for all subsystems to mitigate the complexities of the overall control system. By applying the RSBA architecture, the uniformly exponential stability of the EMLA-driven HDRM is proven based on the Lyapunov stability theory. The proposed RSBA control performance is validated through simulations and experiments of the scrutinized PMSM-powered EMLA-actuated mechanisms.
Sleep When Everything Looks Fine: Self-Triggered Monitoring for Signal Temporal Logic Tasks
Authors: Chuwei Wang, Xinyi Yu, Jianing Zhao, Lars Lindemann, Xiang Yin
Date: 2023-11-27 00:00:00

Online monitoring is a widely used technique in assessing if the performance of the system satisfies some desired requirements during run-time operation. Existing works on online monitoring usually assume that the monitor can acquire system information periodically at each time instant. However, such a periodic mechanism may be unnecessarily energy-consuming as it essentially requires to turn on sensors consistently. In this paper, we proposed a novel self-triggered mechanism for model-based online monitoring of discrete-time dynamical system under specifications described by signal temporal logic (STL) formulae. Specifically, instead of sampling the system state at each time instant, a self-triggered monitor can actively determine when the next system state is sampled in addition to its monitoring decision regarding the satisfaction of the task. We propose an effective algorithm for synthesizing such a self-triggered monitor that can correctly evaluate a given STL formula on-the-fly while maximizing the time interval between two observations. We show that, compared with the standard online monitor with periodic information, the proposed self-triggered monitor can significantly reduce observation burden while ensuring that no information of the STL formula is lost. Case studies are provided to illustrate the proposed monitoring mechanism.
Development and Validation of a Dynamic Operating Envelopes-enabled Demand Response Scheme in Low-voltage Distribution Networks
Authors: Gayan Lankeshwara, Rahul Sharma, M. R. Alam, Ruifeng Yan, Tapan K. Saha
Date: 2023-11-27 00:00:00

Dynamic operating envelopes (DOEs) offer an attractive solution for maintaining network integrity amidst increasing penetration of distributed energy resources (DERs) in low-voltage (LV) networks. Currently, the focus of DOEs primarily revolves around active power exports of rooftop photovoltaic (PV) generation, often neglecting the impact of demand response (DR). This paper presents a two-stage, coordinated approach for residential DR participation in electricity markets under the DOE framework. In the first stage, the distribution network service provider (DNSP) adopts a convex hull technique to establish DOEs at each customer point-of-connection (POC). In the second stage, the demand response aggregator (DRA) utilises DOEs assigned by the DNSP to develop a hierarchical control scheme for tracking a load set-point signal without jeopardising network statutory limits. To assess the effectiveness of the proposed control scheme in a practical setting, software-in-the-loop (SIL) tests are performed in a grid simulator, considering a real residential feeder with realistic household load and generation profiles. Simulation validations suggest that the DRA can provide precise DR while honouring network statutory limits and maintaining end-user thermal comfort. Furthermore, the overall approach is compliant with the market dispatch interval and preserves end-user data privacy.
Demonstration of Programmable Brain-Inspired Optoelectronic Neuron in Photonic Spiking Neural Network with Neural Heterogeneity
Authors: Yun-jhu Lee, Mehmet Berkay On, Luis El Srouji, Li Zhang, Mahmoud Abdelghany, S. J. Ben Yoo
Date: 2023-11-27 00:00:00

Photonic Spiking Neural Networks (PSNN) composed of the co-integrated CMOS and photonic elements can offer low loss, low power, highly-parallel, and high-throughput computing for brain-inspired neuromorphic systems. In addition, heterogeneity of neuron dynamics can also bring greater diversity and expressivity to brain-inspired networks, potentially allowing for the implementation of complex functions with fewer neurons. In this paper, we design, fabricate, and experimentally demonstrate an optoelectronic spiking neuron that can simultaneously achieve high programmability for heterogeneous biological neural networks and maintain high-speed computing. We demonstrate that our neuron can be programmed to tune four essential parameters of neuron dynamics under 1GSpike/s input spiking pattern signals. A single neuron circuit can be tuned to output three spiking patterns, including chattering behaviors. The PSNN consisting of the optoelectronic spiking neuron and a Mach-Zehnder interferometer (MZI) mesh synaptic network achieves 89.3% accuracy on the Iris dataset. Our neuron power consumption is 1.18 pJ/spike output, mainly limited by the power efficiency of the vertical-cavity-lasers, optical coupling efficiency, and the 45 nm CMOS platform used in this experiment, and is predicted to achieve 36.84 fJ/spike output with a 7 nm CMOS platform (e.g. ASAP7) integrated with silicon photonics containing on-chip micron-scale lasers.
Multi-Feeder Restoration using Multi-Microgrid Formation and Management
Authors: Valliappan Muthukaruppan, Rongxing Hu, Ashwin Shirsat, Mesut Baran, Ning Lu, Wenyuan Tang, David Lubkeman
Date: 2023-11-26 00:00:00
Tasks: energy management, Management

This papers highlights the benefit of coordinating resources on mulitple active distribution feeders during severe long duration outages through multi-microgrid formation. A graph-theory based multi-microgrid formation algorithm is developed which is agnostic of the underlying energy management scheme of the microgrids and solved in a rolling horizon fashion. The algorithm is then enhanced to handle multiple feeders where formation of long laterals needs to be avoided due to potential voltage control issues in distribution systems. The algorithm is evaluated on a synthetic two feeder system derived from interconnecting two IEEE 123 node system. The results indicate increased service to loads in the system and better utilization of renewable resources.
A Frequency-Domain Version of Willems' Fundamental Lemma
Authors: T. J. Meijer, S. A. N. Nouwens, V. S. Dolk, W. P. M. H. Heemels
Date: 2023-11-26 00:00:00
Tasks: LEMMA

Willems' fundamental lemma has recently received an impressive amount of attention in the (data-driven) control community. In this paper, we formulate a frequency-domain equivalent of this lemma. In doing so, we bridge the gap between recent developments in data-driven analysis and control and the extensive knowledge on non-parametric frequency-domain identification that has accumulated, particularly in industry, through decades of working with classical (frequency-domain) control and identification techniques. Our formulation also allows for the combination of multiple data sets in the sense that, in the data, multiple input directions may be excited at the same frequency. We also illustrate the usefulness of our results by demonstrating how they can be applied to perform frequency-domain-data-driven simulation.
From OTFS to DD-ISAC: Integrating Sensing and Communications in the Delay Doppler Domain
Authors: Weijie Yuan, Lin Zhou, Saeid K. Dehkordi, Shuangyang Li, Pingzhi Fan, Giuseppe Caire, H. Vincent Poor
Date: 2023-11-26 00:00:00
Tasks: Integrated sensing and communication, ISAC

Next-generation vehicular networks are expected to provide the capability of robust environmental sensing in addition to reliable communications to meet intelligence requirements. A promising solution is the integrated sensing and communication (ISAC) technology, which performs both functionalities using the same spectrum and hardware resources. Most existing works on ISAC consider the Orthogonal Frequency Division Multiplexing (OFDM) waveform. Nevertheless, vehicle motion introduces Doppler shift, which breaks the subcarrier orthogonality and leads to performance degradation. The recently proposed Orthogonal Time Frequency Space (OTFS) modulation, which exploits various advantages of Delay Doppler (DD) channels, has been shown to support reliable communication in high-mobility scenarios. Moreover, the DD waveform can directly interact with radar sensing parameters, which are actually delay and Doppler shifts. This paper investigates the advantages of applying the DD communication waveform to ISAC. Specifically, we first provide a comprehensive overview of implementing DD communications, based on which several advantages of DD-ISAC over OFDM-based ISAC are revealed, including transceiver designs and the ambiguity function. Furthermore, a detailed performance comparison are presented, where the target detection probability and the mean squared error (MSE) performance are also studied. Finally, some challenges and opportunities of DD-ISAC are also provided.
Hurricane and Storm Surges-Induced Power System Vulnerabilities and their Socioeconomic Impact
Authors: Abodh Poudyal, Shishir Lamichhane, Charlotte Wertz, Sajjad Uddin Mahmud, Anamika Dubey
Date: 2023-11-25 00:00:00

This paper introduces a probabilistic framework to quantify community vulnerability towards power losses due to extreme weather events. To analyze the impact of weather events on the power grid, the wind fields of historical hurricanes from 2000 to 2018 on the Texas coast are modeled using their available parameters, and probabilistic storm surge scenarios are constructed utilizing the hurricane characteristics. The vulnerability of hurricanes and storm surges is evaluated on a 2000 bus synthetic power grid model on the geographical footprint of Texas. The load losses, obtained via branch and substation outages, are then geographically represented at the county level and integrated with the publicly available Social Vulnerability Index to evaluate the Integrated Community Vulnerability Index (ICVI), which reflects the impacts of these extreme weather events on the socioeconomic and community power systems. The analysis concludes that the compounded impact of power outages due to extreme weather events can amplify the vulnerability of affected communities. Such analysis can help the system planners and operators make an informed decision.
Resilience-driven Planning of Electric Power Systems Against Extreme Weather Events
Authors: Abodh Poudyal, Shishir Lamichhane, Anamika Dubey, Josue Campos do Prado
Date: 2023-11-25 00:00:00

With the increasing frequency of natural disasters, operators must prioritize improvements in the existing electric power grid infrastructure to enhance the resilience of the grid. Resilience to extreme weather events necessitates lowering the impacts of high-impact, low-probability (HILP) events, which is only possible when such events are considered during the planning stage. This paper proposes a two-stage stochastic planning model where the generation dispatch, line hardening, line capacity expansion, and distributed generation sizing and siting decisions are proactively decided to minimize the overall load shed and its risk for extreme weather scenarios, where the risk is modeled using conditional value-at-risk. To alleviate computational complexity without sacrificing solution quality, a representative scenario sampling method is used. Finally, the overall framework is tested on a standard IEEE reliability test system to evaluate the effectiveness of the proposed approach. Several planning portfolios are presented that can help system planners identify trade-offs between system resilience, planning budget, and risk aversion.
Active disturbance rejection control for unmanned tracked vehicles in leader-follower scenarios: discrete-time implementation and field test validation
Authors: Salem-Bilal Amokrane, Mohammed Zouaoui Laidouni, Touati Adli, Rafal Madonski, Momir Stanković
Date: 2023-11-25 00:00:00

This paper presents a systematic design of an active disturbance rejection control (ADRC) system for unmanned tracked vehicles (UTVs) in leader-follow formation. Two ADRC controllers are designed for the lateral and the longitudinal channels of the UTV based on control errors in the cross-track and the along-track directions. Through simulations, the proposed ADRC approach is first shown to outperform the conventional PI/PID controllers in scenarios involving sudden changes in the leader motion dynamics, slippage disturbances, and measurement noise. Then, a comprehensive experimental validation of the proposed leader-follower control is performed using a laboratory UTV equipped with a camera and laser sensors (to enable the calculation of error signals). In order to provide more effective interaction between the human (leader) and the UTV (follower) during the leader-follower task, a camera-based subsystem for human pose recognition is developed and deployed. Finally, the experimental results obtained outdoors demonstrate that the proposed ADRC-based leader-follower UTV control system achieves high tracking capabilities, robustness against slippage disturbances, and adaptability to changing environmental conditions.
Adaptive time delay based control of non-collocated oscillatory systems
Authors: Michael Ruderman
Date: 2023-11-25 00:00:00

Time delay based control, recently proposed for non-collocated fourth-order systems, has several advantages over an observer-based state-feedback compensation of the low-damped oscillations in output. In this paper, we discuss a practical infeasibility of such observer-based approach and bring forward application of the time delay based controller, which is simple in both the structure and design. Moreover, robust estimation of the output oscillation frequency is used and extended by a bias canceling. The latter is required for positioning the oscillatory passive loads. This way, an adaptive version of time delay-based control is realized that does not require prior knowledge of the mass and stiffness parameters. The results are demonstrated on the oscillatory experimental setup with constraints in the operation range and control value.
Completeness of Riemannian metrics: an application to the control of constrained mechanical systems
Authors: José Ángel Acosta, Anthony Bloch, David Martín de Diego
Date: 2023-11-25 00:00:00

We introduce a mathematical technique based on modifying a given Riemannian metric and we investigate its applicability to controlling and stabilizing constrained mechanical systems. In essence our result is based on the construction of a complete Riemannian metric in the modified space where the constraint is included. In particular this can be applied to the controlled Lagrangians technique Bloch et al. [2000b, 2001] modifying its metric to additionally cover mechanical systems with configuration constraints via control. The technique used consists of approximating incomplete Riemannian metrics by complete ones, modifying the evolution near a boundary and finding a controller satisfying a given design criterion.
Sequencing-enabled Hierarchical Cooperative CAV On-ramp Merging Control with Enhanced Stability and Feasibility
Authors: Sixu Li, Yang Zhou, Xinyue Ye, Jiwan Jiang, Meng Wang
Date: 2023-11-25 00:00:00
Tasks: Model Predictive Control

This paper develops a sequencing-enabled hierarchical connected automated vehicle (CAV) cooperative on-ramp merging control framework. The proposed framework consists of a two-layer design: the upper level control sequences the vehicles to harmonize the traffic density across mainline and on-ramp segments while enhancing lower-level control efficiency through a mixed-integer linear programming formulation. Subsequently, the lower-level control employs a longitudinal distributed model predictive control (MPC) supplemented by a virtual car-following (CF) concept to ensure asymptotic local stability, l_2 norm string stability, and safety. Proofs of asymptotic local stability and l_2 norm string stability are mathematically derived. Compared to other prevalent asymptotic local-stable MPC controllers, the proposed distributed MPC controller greatly expands the initial feasible set. Additionally, an auxiliary lateral control is developed to maintain lane-keeping and merging smoothness while accommodating ramp geometric curvature. To validate the proposed framework, multiple numerical experiments are conducted. Results indicate a notable outperformance of our upper-level controller against a distance-based sequencing method. Furthermore, the lower-level control effectively ensures smooth acceleration, safe merging with adequate spacing, adherence to proven longitudinal local and string stability, and rapid regulation of lateral deviations.
Data-Driven Nonlinear State Observation using Video Measurements
Authors: Cormak Weeks, Wentao Tang
Date: 2023-11-25 00:00:00

State observation is necessary for feedback control but often challenging for nonlinear systems. While Kazantzis-Kravaris/Luenberger (KKL) observer gives a generic design, its model-based numerical solution is difficult. In this paper, we propose a simple method to determine a data-driven KKL observer, namely to (i) transform the measured output signals by a linear time-invariant dynamics, and (ii) reduce the dimensionality to principal components. This approach is especially suitable for systems with rich measurements and low-dimensional state space, for example, when videos can be obtained in real time. We present an application to a video of the well-known Belousov-Zhabotinsky (B-Z) reaction system with severe nonlinearity, where the data-driven KKL observer recovers an oscillatory state orbit with slow damping.
Voltage Constrained Heavy Duty Vehicle Electrification: Formulation and Case Study
Authors: Apurv Shukla, Rayan El Helou, Le Xie
Date: 2023-11-24 00:00:00

The electrification of heavy-duty vehicles (HDEVs) is a rapidly emerging avenue for decarbonization of energy and transportation sectors. Compared to light duty vehicles, HDEVs exhibit unique travel and charging patterns over long distances. In this paper, we formulate an analytically tractable model that considers the routing decisions for the HDEVs and their charging implications on the power grid. Our model captures the impacts of increased vehicle electrification on the transmission grid, with particular focus on HDEVs. We jointly model transportation and power networks coupling them through the demand generated for charging requirements of HDEVs. In particular, the voltage constraint violation is explicitly accounted for in the proposed model given the signifcant amount of charging power imposed by HDEVs. We obtain optimal routing schedules and generator dispatch satisfying mobility constraints of HDEVs while minimizing voltage violations in electric transmission network. Case study based on an IEEE 24-bus system is presented using realistic data of transit data of HDEVs. The numerical results suggest that the proposed model and algorithm effectively mitigate the voltage violation when a significant amount of HDEVs are integrated to the power transmission network. Such mitigation includes reduction in the voltage magnitude, geographical dispersion of voltage violations and worst-case voltage violations at critical nodes.