pwc-archive/papers-with-abstracts huggingface dataset:
Robust Approximate Simulation for Hierarchical Control of Piecewise Affine Systems under Bounded Disturbances
Authors: Zihao Song, Vince Kurtz, Shirantha Welikala, Panos J. Antsaklis, Hai Lin
Date: 2022-03-04 00:00:00

Piecewise affine (PWA) systems are widely applied in many practical cases such as the control of nonlinear systems and hybrid dynamics. However, most of the existing PWA control methods have poor scalability with respect to the number of modes and system dimensions and may not be robust to the disturbances in performance. In this paper, we present a robust approximate simulation based control method for PWA systems under bounded external disturbances. First, a lower-dimensional linear system (abstraction) and an associated interface are designed to enable the output of the PWA system (concrete system) to track the output of the abstraction. Then, a Lyapunov-like simulation function is designed to show the boundedness of the output errors between the two systems. Furthermore, the results obtained for linear abstraction are extended to the case that a simpler PWA system is the abstraction. To illustrate the effectiveness of the proposed approach, simulation results are provided for two design examples.
High Order Robust Adaptive Control Barrier Functions and Exponentially Stabilizing Adaptive Control Lyapunov Functions
Authors: Max H. Cohen, Calin Belta
Date: 2022-03-03 00:00:00

This paper studies the problem of utilizing data-driven adaptive control techniques to guarantee stability and safety of uncertain nonlinear systems with high relative degree. We first introduce the notion of a High Order Robust Adaptive Control Barrier Function (HO-RaCBF) as a means to compute control policies guaranteeing satisfaction of high relative degree safety constraints in the face of parametric model uncertainty. The developed approach guarantees safety by initially accounting for all possible parameter realizations but adaptively reduces uncertainty in the parameter estimates leveraging data recorded online. We then introduce the notion of an Exponentially Stabilizing Adaptive Control Lyapunov Function (ES-aCLF) that leverages the same data as the HO-RaCBF controller to guarantee exponential convergence of the system trajectory. The developed HO-RaCBF and ES-aCLF are unified in a quadratic programming framework, whose efficacy is showcased via two numerical examples that, to our knowledge, cannot be addressed by existing adaptive control barrier function techniques.
PercepNet+: A Phase and SNR Aware PercepNet for Real-Time Speech Enhancement
Authors: Xiaofeng Ge, Jiangyu Han, Yanhua Long, Haixin Guan
Date: 2022-03-04 00:00:00
Tasks: Speech Enhancement

PercepNet, a recent extension of the RNNoise, an efficient, high-quality and real-time full-band speech enhancement technique, has shown promising performance in various public deep noise suppression tasks. This paper proposes a new approach, named PercepNet+, to further extend the PercepNet with four significant improvements. First, we introduce a phase-aware structure to leverage the phase information into PercepNet, by adding the complex features and complex subband gains as the deep network input and output respectively. Then, a signal-to-noise ratio (SNR) estimator and an SNR switched post-processing are specially designed to alleviate the over attenuation (OA) that appears in high SNR conditions of the original PercepNet. Moreover, the GRU layer is replaced by TF-GRU to model both temporal and frequency dependencies. Finally, we propose to integrate the loss of complex subband gain, SNR, pitch filtering strength, and an OA loss in a multi-objective learning manner to further improve the speech enhancement performance. Experimental results show that, the proposed PercepNet+ outperforms the original PercepNet significantly in terms of both PESQ and STOI, without increasing the model size too much.
Selective Pseudo-labeling and Class-wise Discriminative Fusion for Sound Event Detection
Authors: Yunhao Liang, Yanhua Long, Yijie Li, Jiaen Liang
Date: 2022-03-04 00:00:00
Tasks: Event Detection, Sound Event Detection

In recent years, exploring effective sound separation (SSep) techniques to improve overlapping sound event detection (SED) attracts more and more attention. Creating accurate separation signals to avoid the catastrophic error accumulation during SED model training is very important and challenging. In this study, we first propose a novel selective pseudo-labeling approach, termed SPL, to produce high confidence separated target events from blind sound separation outputs. These target events are then used to fine-tune the original SED model that pre-trained on the sound mixtures in a multi-objective learning style. Then, to further leverage the SSep outputs, a class-wise discriminative fusion is proposed to improve the final SED performances, by combining multiple frame-level event predictions of both sound mixtures and their separated signals. All experiments are performed on the public DCASE 2021 Task 4 dataset, and results show that our approaches significantly outperforms the official baseline, the collar-based F 1, PSDS1 and PSDS2 performances are improved from 44.3%, 37.3% and 54.9% to 46.5%, 44.5% and 75.4%, respectively.
MANNER: Multi-view Attention Network for Noise Erasure
Authors: Hyun Joon Park, Byung Ha Kang, WooSeok Shin, Jin Sob Kim, Sung Won Han
Date: 2022-03-04 00:00:00
Tasks: Decoder, Speech Enhancement

In the field of speech enhancement, time domain methods have difficulties in achieving both high performance and efficiency. Recently, dual-path models have been adopted to represent long sequential features, but they still have limited representations and poor memory efficiency. In this study, we propose Multi-view Attention Network for Noise ERasure (MANNER) consisting of a convolutional encoder-decoder with a multi-view attention block, applied to the time-domain signals. MANNER efficiently extracts three different representations from noisy speech and estimates high-quality clean speech. We evaluated MANNER on the VoiceBank-DEMAND dataset in terms of five objective speech quality metrics. Experimental results show that MANNER achieves state-of-the-art performance while efficiently processing noisy speech.
Mapping evolving population geography in China
Authors: Lei Dong, Rui Du, Yu Liu
Date: 2022-03-04 00:00:00

China's demographic changes have important global economic and geopolitical implications. Yet, our understanding of such transitions at the micro-spatial scale remains limited due to spatial inconsistency of the census data caused by administrative boundary adjustments. To fill this gap, we manually collected and built a population census panel from 2010 to 2020 at both the county and prefectural-city levels. We show that the massive internal migration drives China's increasing population concentration and regional disparity, resulting in severe population aging in shrinking cities and increasing gender imbalance in growing cities.
Preventive and Active Safety Applications
Authors: Levent Guvenc
Date: 2020-12-15 00:00:00

Road vehicle safety systems can be broadly classified into the two categories of passive and active systems. The aim of passive safety systems is to reduce risk of injury to the occupants of the vehicle during and after an accident like a crash or rollover. Passive safety systems include the design of safety restraints, design for crashworthiness, seat belts and air bags. In contrast to passive systems, the aim in active safety is to prevent an accident from occurring in the first place. As such, it makes sense to call them preventive systems also. Here, the concentration is on preventive and active safety systems. The current state of the art in some key preventive and active safety systems is presented in this paper, wherein the various techniques used are also explained briefly. In some cases, the presentation is complemented with results obtained in the research group of the author. A road map of expected future developments in the area of preventive and safety applications is also presented.
Yaw Stability Control System Development and Implementation for a Fully Electric Vehicle
Authors: Kerim Kahraman, Mutlu Senturk, Mumin Tolga Emirler, Ismail Meric Can Uygan, Bilin Aksun-Guvenc, Levent Guvenc, Baris Efendioglu
Date: 2020-12-08 00:00:00

There is growing interest in fully electric vehicles in the automotive industry as it becomes increasingly more difficult to meet new and upcoming emission regulations based on internal combustion engines. Fully electric vehicles do not have an internal combustion engine. Hence, drive torque change for a traction control system and for a yaw stability control system has to be through the electric motor used for traction. The regenerative braking capability of fully electric vehicles has to be taken into account in designing braking controllers like ABS and yaw stability control through differential braking. Fully electric vehicles are usually lighter vehicles with different dynamic characteristics than that of their predecessors using internal combustion engines. As such, their yaw stability control systems have to be re-designed and tested. This paper reports the initial results of ongoing work on yaw stability controller design for a fully electric vehicle. Two different implementations on a research prototype fully electric light commercial vehicle are considered. The first implementation uses the production yaw stability control system in the internal combustion engine powered conventional vehicle. The drive torque change commands from the production ECU are read, modified and sent to the electric motor driver in trying to mimic the conventional vehicle. The differential braking commands are the same as in the conventional vehicle. In the second implementation, a generic yaw stability control system that calculates and issues its own drive torque change commands and differential braking commands is designed and implemented. Offline simulations on a validated model and a hardware-in-the-loop simulation system are used in designing the yaw stability control system.
Comparing Generator Unavailability Models with Empirical Distributions from Open Energy Datasets
Authors: Matthew Deakin, David Greenwood, David J. Brayshaw, Hannah Bloomfield
Date: 2022-03-04 00:00:00

The modelling of power station outages is an integral part of power system planning. In this work, models of the unavailability of the fleets of eight countries in Northwest Europe are constructed and subsequently compared against empirical distributions derived using data from the open-access ENTSO-e Transparency Platform. Summary statistics of non-sequential models highlight limitations with the empirical modelling, with very variable results across countries. Additionally, analysis of time sequential models suggests a clear need for fleet-specific analytic model parameters. Despite a number of challenges and ambiguities associated with the empirical distributions, it is suggested that a range of valuable qualitative and quantitative insights can be gained by comparing these two complementary approaches for modelling and understanding generator unavailabilities.
An Operator-Theoretic Approach to Robust Event-Triggered Control of Network Systems with Frequency-Domain Uncertainties
Authors: Shiqi Zhang, Zhongkui Li
Date: 2022-03-04 00:00:00

In this paper, we study the robustness of the event-triggered consensus algorithms against frequency-domain uncertainties. It is revealed that the sampling errors resulted by event triggering are essentially images of linear finite-gain $\mathcal{L}_2$-stable operators acting on the consensus errors of the sampled states and the event-triggered mechanism is equivalent to a negative feedback loop introduced additionally to the feedback system. In virtue of this, the robust consensus problem of the event-triggered network systems subject to additive dynamic uncertainties and network multiplicative uncertainties are considered, respectively. In both cases, quantitative relationships among the parameters of the controllers, the Laplacian matrix of the network topology, and the robustness against aperiodic event triggering and frequency-domain uncertainties are unveiled. Furthermore, the event-triggered dynamic average consensus (DAC) problem is also investigated, wherein the sampling errors are shown to be images of nonlinear finite-gain operators. The robust performance of the proposed DAC algorithm is analyzed, which indicates that the robustness and the performance are negatively related to the eigenratio of the Laplacian matrix. Simulation examples are also provided to verify the obtained results.
Benchmarking real-time algorithms for in-phase auditory stimulation of low amplitude slow waves with wearable EEG devices during sleep
Authors: Maria Laura Ferster, Giulia Da Poian, Kiran Menachery, Simon J. Schreiner, Caroline Lustenberger, Angelina Maric, Reto Huber, Christian Baumann, Walter Karlen
Date: 2022-03-04 00:00:00
Tasks: Benchmarking, Computational Efficiency, EEG, Electroencephalogram (EEG)

Auditory stimulation of EEG slow waves (SW) during non-rapid eye movement (NREM) sleep has shown to improve cognitive function when it is delivered at the up-phase of SW. SW enhancement is particularly desirable in subjects with low-amplitude SW such as older adults or patients suffering from neurodegeneration such as Parkinson disease (PD). However, existing algorithms to estimate the up-phase suffer from a poor phase accuracy at low EEG amplitudes and when SW frequencies are not constant. We introduce two novel algorithms for real-time EEG phase estimation on autonomous wearable devices. The algorithms were based on a phase-locked loop (PLL) and, for the first time, a phase vocoder (PV). We compared these phase tracking algorithms with a simple amplitude threshold approach. The optimized algorithms were benchmarked for phase accuracy, the capacity to estimate phase at SW amplitudes between 20 and 60 microV, and SW frequencies above 1 Hz on 324 recordings from healthy older adults and PD patients. Furthermore, the algorithms were implemented on a wearable device and the computational efficiency and the performance was evaluated on simulated sleep EEG, as well as prospectively during a recording with a PD patient. All three algorithms delivered more than 70% of the stimulation triggers during the SW up-phase. The PV showed the highest capacity on targeting low-amplitude SW and SW with frequencies above 1 Hz. The testing on real-time hardware revealed that both PV and PLL have marginal impact on microcontroller load, while the efficiency of the PV was 4% lower than the PLL. Active auditory stimulation did not influence the phase tracking. This work demonstrated that phase-accurate auditory stimulation can be delivered during home-based sleep interventions with a wearable device also in populations with low-amplitude SW.
Robust Event-Based Control: Bridge Time-Domain Triggering and Frequency-Domain Uncertainties
Authors: Shiqi Zhang, Zhongkui Li
Date: 2022-03-04 00:00:00

This paper considers the robustness of event-triggered control of general linear systems against additive or multiplicative frequency-domain uncertainties. It is revealed that in static or dynamic event triggering mechanisms, the sampling errors are images of affine operators acting on the sampled outputs. Though not belonging to $\mathcal{RH}_\infty$, these operators are finite-gain $\mathcal{L}_2$ stable with operator-norm depending on the triggering conditions and the norm bound of the uncertainties. This characterization is further extended to the general integral quadratic constraint (IQC)-based triggering mechanism. As long as the triggering condition characterizes an $\mathcal{L}_2$-to-$\mathcal{L}_2$ mapping relationship (in other words, small-gain-type constraints) between the sampled outputs and the sampling errors, the robust event-triggered controller design problem can be transformed into the standard $H_\infty$ synthesis problem of a linear system having the same order as the controlled plant. Algorithms are provided to construct the robust controllers for the static, dynamic and IQC-based event triggering cases.
Controllability of Multilayer Networked Sampled-data Systems
Authors: Zixuan Yang, Xiaofan Wang, Lin Wang
Date: 2022-03-04 00:00:00

This paper explores the state controllability of multilayer networked sampled-data systems with inter-layer couplings, where zero-order holders (ZOHs) are on the control and transmission channels. The effects of both single- and multi-rate sampling on controllability of multilayer networked linear time-invariant (LTI) systems are analyzed, with some sufficient and/or necessary controllability conditions derived. Under specific conditions, the pathological sampling of single node systems could be eliminated by the network structure and inner couplings among different nodes and different layers. The representative drive-response inter-layer coupling mode is studied, and it reveals that the whole system could be controllable due to the inter-layer couplings even if the response layer is uncontrollable itself. Moreover, simulated examples show that the modification of sampling rate on local channels could lay a positive or negative effect on the controllability of the whole system. All the results indicate that the controllability of the multilayer networked sampled-data system is collectively affected by mutually coupled factors.
Grid-Forming Inverter-based Wind Turbine Generators: Comprehensive Review, Comparative Analysis, and Recommendations
Authors: Thai-Thanh Nguyen, Tuyen Vu, Sumit Paudyal, Frede Blaabjerg, Thanh Long Vu
Date: 2022-03-04 00:00:00

High penetration of wind power with conventional grid following controls for inverter-based wind turbine generators (WTGs) weakens the power grid, challenging the power system stability. Grid-forming (GFM) controls are emerging technologies that can address such stability issues. Numerous methodologies of GFM inverters have been developed in the literature; however, their applications for WTGs have not been thoroughly explored. As WTGs need to incorporate multiple control functions to operate reliably in different operational regions, the GFM control should be appropriately developed for the WTGs. This paper presents a review of GFM controls for WTGs, which covers the latest developments in GFM controls and includes multi-loop and single-loop GFM, virtual synchronous machine-based GFM, and virtual inertia control-based GFM. A comparison study for these GFM-based WTGs regarding normal and abnormal operating conditions together with black-start capability is then performed. The control parameters of these GFM types are properly designed and optimized to enable a fair comparison. In addition, the challenges of applying these GFM controls to wind turbines are discussed, which include the impact of DC-link voltage control strategy and the current saturation algorithm on the GFM control performance, black-start capability, and autonomous operation capability. Finally, recommendations and future developments of GFM-based wind turbines to increase the power system reliability are presented.
Autonomous and Resilient Control for Optimal LEO Satellite Constellation Coverage Against Space Threats
Authors: Yuhan Zhao, Quanyan Zhu
Date: 2022-03-03 00:00:00
Tasks: Model Predictive Control

LEO satellite constellation coverage has served as the base platform for various space applications. However, the rapidly evolving security environment such as orbit debris and adversarial space threats are greatly endangering the security of satellite constellation and integrity of the satellite constellation coverage. As on-orbit repairs are challenging, a distributed and autonomous protection mechanism is necessary to ensure the adaptation and self-healing of the satellite constellation coverage from different attacks. To this end, we establish an integrative and distributed framework to enable resilient satellite constellation coverage planning and control in a single orbit. Each satellite can make decisions individually to recover from adversarial and non-adversarial attacks and keep providing coverage service. We first provide models and methodologies to measure the coverage performance. Then, we formulate the joint resilient coverage planning-control problem as a two-stage problem. A coverage game is proposed to find the equilibrium constellation deployment for resilient coverage planning and an agent-based algorithm is developed to compute the equilibrium. The multi-waypoint Model Predictive Control (MPC) methodology is adopted to achieve autonomous self-healing control. Finally, we use a typical LEO satellite constellation as a case study to corroborate the results.
Neural mechanisms underlying the temporal organization of naturalistic animal behavior
Authors: Luca Mazzucato
Date: 2022-03-04 00:00:00

Naturalistic animal behavior exhibits a strikingly complex organization in the temporal domain, whose variability stems from at least three sources: hierarchical, contextual, and stochastic. What are the neural mechanisms and computational principles generating such complex temporal features? In this review, we provide a critical assessment of the existing behavioral and neurophysiological evidence for these sources of temporal variability in naturalistic behavior. We crystallize recent studies which converge on an emergent mechanistic theory of temporal variability based on attractor neural networks and metastable dynamics, arising from the coordinated interactions between mesoscopic neural circuits. We highlight the crucial role played by structural heterogeneities and by noise arising in mesoscopic circuits. We assess the shortcomings and missing links in the current theoretical and experimental literature and propose new directions of investigations to fill these gaps.
Distinct excitatory and inhibitory bump wandering in a stochastic neural field
Authors: Heather L Cihak, Tahra L Eissa, Zachary P Kilpatrick
Date: 2022-03-04 00:00:00

Localized persistent cortical neural activity is a validated neural substrate of parametric working memory. Such activity `bumps' represent the continuous location of a cue over several seconds. Pyramidal (excitatory) and interneuronal (inhibitory) subpopulations exhibit tuned bumps of activity, linking neural dynamics to behavioral inaccuracies observed in memory recall. However, many bump attractor models collapse these subpopulations into a single joint excitatory/inhibitory (lateral inhibitory) population, and do not consider the role of interpopulation neural architecture and noise correlations. Both factors have a high potential to impinge upon the stochastic dynamics of these bumps, ultimately shaping behavioral response variance. In our study, we consider a neural field model with separate excitatory/inhibitory (E/I) populations and leverage asymptotic analysis to derive a nonlinear Langevin system describing E/I bump interactions. While the E bump attracts the I bump, the I bump stabilizes but can also repel the E bump, which can result in prolonged relaxation dynamics when both bumps are perturbed. Furthermore, the structure of noise correlations within and between subpopulations strongly shapes the variance in bump position. Surprisingly, higher interpopulation correlations reduce variance.