pwc-archive/papers-with-abstracts huggingface dataset:
Preserving Topology of Network Systems: Metric, Analysis, and Optimal Design
Authors: Yushan Li, Zitong Wang, Jianping He, Cailian Chen, Xinping Guan
Date: 2023-07-31 00:00:00

Preserving the topology from being inferred by external adversaries has become a paramount security issue for network systems (NSs), and adding random noises to the nodal states provides a promising way. Nevertheless, recent works have revealed that the topology cannot be preserved under i.i.d. noises in the asymptotic sense. How to effectively characterize the non-asymptotic preservation performance still remains an open issue. Inspired by the deviation quantification of concentration inequalities, this paper proposes a novel metric named trace-based variance-expectation ratio. This metric effectively captures the decaying rate of the topology inference error, where a slower rate indicates better non-asymptotic preservation performance. We prove that the inference error will always decay to zero asymptotically, as long as the added noises are non-increasing and independent (milder than the i.i.d. condition). Then, the optimal noise design that produces the slowest decaying rate for the error is obtained. More importantly, we amend the noise design by introducing one-lag time dependence, achieving the zero state deviation and the non-zero topology inference error in the asymptotic sense simultaneously. Extensions to a general class of noises with multi-lag time dependence are provided. Comprehensive simulations verify the theoretical findings.
Successive Pose Estimation and Beam Tracking for mmWave Vehicular Communication Systems
Authors: Cen Liu, Guangxu Zhu, Fan Liu, Yuanwei Liu, Kaibin Huang
Date: 2023-07-30 00:00:00
Tasks: Pose Estimation, Radar odometry

The millimeter wave (mmWave) radar sensing-aided communications in vehicular mobile communication systems is investigated. To alleviate the beam training overhead under high mobility scenarios, a successive pose estimation and beam tracking (SPEBT) scheme is proposed to facilitate mmWave communications with the assistance of mmWave radar sensing. The proposed SPEBT scheme first resorts to a Fast Conservative Filtering for Efficient and Accurate Radar odometry (Fast-CFEAR) approach to estimate the vehicle pose consisting of 2-dimensional position and yaw from radar point clouds collected by mmWave radar sensor. Then, the pose estimation information is fed into an extend Kalman filter to perform beam tracking for the line-of-sight channel. Owing to the intrinsic robustness of mmWave radar sensing, the proposed SPEBT scheme is capable of operating reliably under extreme weather/illumination conditions and large-scale global navigation satellite systems (GNSS)-denied environments. The practical deployment of the SPEBT scheme is verified through rigorous testing on a real-world sensing dataset. Simulation results demonstrate that the proposed SPEBT scheme is capable of providing precise pose estimation information and accurate beam tracking output, while reducing the proportion of beam training overhead to less than 5% averagely.
SAMbA: Speech enhancement with Asynchronous ad-hoc Microphone Arrays
Authors: Nicolas Furnon, Romain Serizel, Slim Essid, Irina Illina
Date: 2023-07-31 00:00:00
Tasks: Speech Enhancement

Speech enhancement in ad-hoc microphone arrays is often hindered by the asynchronization of the devices composing the microphone array. Asynchronization comes from sampling time offset and sampling rate offset which inevitably occur when the microphones are embedded in different hardware components. In this paper, we propose a deep neural network (DNN)-based speech enhancement solution that is suited for applications in ad-hoc microphone arrays because it is distributed and copes with asynchronization. We show that asynchronization has a limited impact on the spatial filtering and mostly affects the performance of the DNNs. Instead of resynchronising the signals, which requires costly processing steps, we use an attention mechanism which makes the DNNs, thus our whole pipeline, robust to asynchronization. We also show that the attention mechanism leads to the asynchronization parameters in an unsupervised manner.
Modulation-Enhanced Excitation for Continuous-Time Reinforcement Learning via Symmetric Kronecker Products
Authors: Brent A. Wallace, Jennie Si
Date: 2023-07-31 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning

This work introduces new results in continuous-time reinforcement learning (CT-RL) control of affine nonlinear systems to address a major algorithmic challenge due to a lack of persistence of excitation (PE). This PE design limitation has previously stifled CT-RL numerical performance and prevented these algorithms from achieving control synthesis goals. Our new theoretical developments in symmetric Kronecker products enable a proposed modulation-enhanced excitation (MEE) framework to make PE significantly more systematic and intuitive to achieve for real-world designers. MEE is applied to the suite of recently-developed excitable integral reinforcement learning (EIRL) algorithms, yielding a class of enhanced high-performance CT-RL control design methods which, due to the symmetric Kronecker product algebra, retain EIRL's convergence and closed-loop stability guarantees. Through numerical evaluation studies, we demonstrate how our new MEE framework achieves substantial improvements in conditioning when approximately solving the Hamilton-Jacobi-Bellman equation to obtain optimal controls. We use an intuitive example to provide insights on the central excitation issue under discussion, and we demonstrate the effectiveness of the proposed procedure on a real-world hypersonic vehicle (HSV) application.
Model predictive control for the prescription of antithyroid agents
Authors: Maylin Menzel, Tobias M. Wolff, Johannes W. Dietrich, Matthias A. Müller
Date: 2023-07-31 00:00:00
Tasks: Model Predictive Control

Although hyperthyroidism is a common disease, the pharmaceutical therapy is based on a trial-and-error approach. We extend a mathematical model of the pituitary-thyroid feedback loop such that the intake of one antithyroid agent, namely methimazole (MMI), can be considered and use a model predictive control (MPC) scheme to determine suitable dosages.
Towards Energy Efficient Control for Commercial Heavy-Duty Mobile Cranes: Modeling Hydraulic Pressures using Machine Learning
Authors: Abdolreza Taheri, Robert Pettersson, Pelle Gustafsson, Joni Pajarinen, Reza Ghabcheloo
Date: 2023-07-31 00:00:00

A sizable part of the fleet of heavy-duty machinery in the construction equipment industry uses the conventional valve-controlled load-sensing hydraulics. Rigorous climate actions towards reducing CO$_{2}$ emissions has sparked the development of solutions to lower the energy consumption and increase the productivity of the machines. One promising solution to having a better balance between energy and performance is to build accurate models (digital twins) of the real systems using data together with recent advances in machine learning/model-based optimization to improve the control systems. With a particular focus on real-world machines with multiple flow-controlled actuators and shared variable-displacement pumps, this paper presents a generalized machine learning approach to modeling the working pressure of the actuators and the overall pump pressures. The procedures for deriving reaction forces and flow rates as important input variables to the surrogate models are described in detail. Using data from a real loader crane testbed, we demonstrate training and validation of individual models, and showcase the accuracy of pressure predictions in five different experiments under various utilizations and pressure levels.
Errors Dynamics in Affine Group Systems
Authors: Xinghan Li, Jianqi Chen, Han Zhang, Jieqiang Wei, Junfeng Wu
Date: 2023-07-31 00:00:00

Errors dynamics captures the evolution of the state errors between two distinct trajectories, that are governed by the same system rule but initiated or perturbed differently. In particular, state observer error dynamics analysis in matrix Lie group is fundamental in practice. In this paper, we focus on the error dynamics analysis for an affine group system under external disturbances or random noises. To this end, we first discuss the connections between the notions of affine group systems and linear group systems. We provide two equivalent characterizations of a linear group system. Such characterizations are based on the homeomorphism of its transition flow and linearity of its Lie algebra counterpart, respectively. Next, we investigate the evolution of a linear group system and we assume it is diffused by a Brownian motion in tangent spaces. We further show that the dynamics projected in the Lie algebra is governed by a stochastic differential equation with a linear drift term. We apply these findings in analyzing the error dynamics. Under differentiable disturbance, we derive an ordinary differential equation characterizing the evolution of the projected errors in the Lie algebra. In addition, the counterpart with stochastic disturbances is derived for the projected errors in terms of a stochastic differential equation. Explicit and accurate derivation of error dynamics is provided for matrix group $SE_N(3)$, which plays a vital role especially in robotic applications.
Power Transfer between Two Antenna Arrays in the Near Field
Authors: Krishan Kumar Tiwari, Giuseppe Caire
Date: 2023-07-31 00:00:00
Tasks: Unity

We present numerical results with a focus on power transfer between two standard linear antenna arrays placed in the near field, where a much smaller active multi-antenna feeder (AMAF) space feeds a far larger passive array referred to as a reflective intelligent surface (RIS). The interest is in the regime of focal length to diameter ratio ($F/D$) less than unity. We address the question of center feed vs. end feed for array fed array antenna architectures and present the following novel findings and contributions: 1. In the regime of $F/D$ ratio less than one, the AMAF-RIS power transfer deviates from the classical inverse square law. Furthermore, the behavior of the power transmission coefficient is more sensitive to the $F/D$ ratio than to a particular RIS size. 2. For an end feed, non-eigenmodes provide better beam shapes than the eigenmodes, which are still inferior to beam shapes from the center feed eigenmodes. 3. The center feed provides more power gain than an end feed. This clearly illustrates that the center feed configuration should be used for array fed arrays, in contrast to the classical parabolic geometry.
Further methods for encrypted linear dynamic controllers utilizing re-encryption
Authors: Junsoo Kim
Date: 2023-07-31 00:00:00

Homomorphic encryption (HE) applied to a networked controller enables secure operation, but in most cases it allows for addition and multiplication over integers only, because of computation efficiency. Several related results deal with such constraints by means of re-encrypted controller output, based on which the controller can be re-constructed and operate over integers. This paper presents two modified methods based on the output re-encryption, which will further reduce the required communication effort and computational burden, respectively.
Mathematical meal models for simulation of human metabolism
Authors: Tobias K. S. Ritschel, Asbjørn Thode Reenberg, Peter Emil Carstensen, Jacob Bendsen, John Bagterp Jørgensen
Date: 2023-07-31 00:00:00

We present and critically discuss five commonly used mathematical models of the meal glucose rate of appearance in humans. Such models are key to simulation of the metabolism in healthy people, people with diabetes, and obese people, and they are central to developing effective treatments and prevention strategies. Furthermore, we discuss important aspects of systematic mathematical modeling of human metabolism, including meal consumption modeling, stoichiometry and reaction kinetics, and general-purpose model components.
Quick Updates for the Perturbed Static Output Feedback Control Problem in Linear Systems
Authors: MirSaleh Bahavarnia, Ahmad F. Taha
Date: 2023-07-30 00:00:00

This paper introduces a method for efficiently updating a nominal stabilizing static output feedback (SOF) controller in perturbed linear systems. As operating points and state-space matrices change in dynamic systems, accommodating updates to the SOF controller are necessary. Traditional methods address such changes by re-solving for the updated SOF gain, which is often \textit{(i)} computationally expensive due to the NP-hard nature of the problem or \textit{(ii)} infeasible due the limitations of its semidefinite programming relaxations. To overcome this, we leverage the concept of \textit{minimum destabilizing real perturbation} to formulate a norm minimization problem that yields fast, reliable controller updates. This approach accommodates a variety of known perturbations, including abrupt changes, model inaccuracies, and equilibrium-dependent linearizations. We also introduce geometric metrics to quantify the proximity to instability and rigorously define stability-guaranteed regions. Extensive numerical simulations validate the efficiency and robustness of the proposed method. We demonstrate the results on the SOF control of mutlti-machine power networks with changing operating points, and demonstrate that the computed quick updates produce comparable solutions to the SOF ones, while requiring orders of magnitude less time.
Quantitative modeling and simulation of biochemical processes in the human body
Authors: Jacob Bendsen, Peter Emil Carstensen, Asbjørn Thode Reenberg, Tobias K. S. Ritschel, John Bagterp Jørgensen
Date: 2023-07-30 00:00:00

We present a whole-body model of human metabolism that utilizes a system of organs and blood vessels to simulate the enzymatic reactions. The model focuses on key organs, including the brain, heart and lungs, liver, gut, and kidney, as well as muscle and adipose tissue. The model equations are formulated using stoichiometry and Michaelis-Menten kinetics to describe the enzymatic reactions. We demonstrate how the model can be used to simulate the effects of prolonged fasting and intermittent fasting on selected metabolite concentrations and glucose flux. Furthermore, by simulating intermittent fasting the effect on the carbohydrate, the protein and the lipid storage is examined. We propose this method as a simple and intuitive approach for modeling the human metabolism, which is general, systematic and easy to incorporate. This could have potential applications in PK/PD drug development and in understanding metabolic disorders.
A Local Measurement Based Comprehensive Protection Scheme for AC Microgrid
Authors: Sindhura Gupta, Susovan Mukhopadhyay, Ambarnath Banerji, Sujit K. Biswas, Prasun Sanki
Date: 2023-07-29 00:00:00

The popularity of low-voltage ac distribution networks is increasing day by day. However, an efficient protection scheme for low-voltage ac distribution systems is still challenging. This paper introduces a protection scheme suitable for low-voltage grid connected and islanded ac microgrid based on local measurements in order to locate, identify and isolate faults. Here current decomposition method is specifically incorporated to accomplish fault type identification. MATLAB/SIMULINK platform is chosen to examine the performance of the proposed scheme both in grid connected and islanded low-voltage ac microgrid. The feasibility of the protection scheme is extensively investigated by simulating all types of faults with substantial variations like different fault location, different fault resistances etc. The test results ensure that the proposed protection scheme is sufficiently reliable and faster for providing complete protection in low-voltage ac microgrid.
On Decidability of Existence of Fortified Supervisors Against Covert Actuator Attackers
Authors: Ruochen Tai, Liyong Lin, Rong Su
Date: 2023-07-29 00:00:00

This work investigates the problem of synthesizing fortified supervisors against covert actuator attackers. For a non-resilient supervisor S, i.e., there exists at least a covert actuator attacker that is capable of inflicting damage w.r.t S, a fortified supervisor S' satisfies two requirements: 1) S' is resilient against any covert actuator attacker, and 2) the original closed-behavior of the closed-loop system under S is preserved, that is, S' is control equivalent to S. By designing a sound and complete procedure, we show the problem of determining the existence of a fortified supervisor against covert actuator attackers is decidable. We also discuss how to extend the decidability result to the case against the worst-case attacker.
A Method to Speed Up Convergence of Iterative Learning Control for High Precision Repetitive Motions
Authors: Richard W. Longman, Shuo Liu, Tarek A. Elsharhawy
Date: 2023-07-29 00:00:00

Various spacecraft have sensors that repeatedly perform a prescribed scanning maneuver, and one may want high precision. Iterative Learning Control (ILC) records previous run tracking error, adjusts the next run command, aiming for zero tracking error in the real world, not our model of the world. In response to a command, feedback control systems perform a convolution sum over all commands given since time zero, with a weighting factor getting smaller going further back in time. ILC learns to eliminate this error through iterations in hardware. It aims to find that command that causes the real world system to actually follow the desired command. The topic of this paper considers the possibility of learning to make our model of the world produce less error. This can be done easily and quickly numerically, and the result then used as a starting point for the ILC iterations performed in hardware. The point is to reduce the number of hardware iteration, converging more quickly to closely tracking the desired trajectory in the world. How to decide how many iterations with our model to make before switching to hardware iterations is presented, with numerical simulations performed to illustrate the approach.
Safe Pricing Mechanisms for Distributed Resource Allocation with Bandit Feedback
Authors: Spencer Hutchinson, Berkay Turan, Mahnoosh Alizadeh
Date: 2023-07-28 00:00:00

In societal-scale infrastructures, such as electric grids or transportation networks, pricing mechanisms are often used as a way to shape users' demand in order to lower operating costs and improve reliability. Existing approaches to pricing design for safety-critical networks often require that users are queried beforehand to negotiate prices, which has proven to be challenging to implement in the real-world. To offer a more practical alternative, we develop learning-based pricing mechanisms that require no input from the users. These pricing mechanisms aim to maximize the utility of the users' consumption by gradually estimating the users' price response over a span of $T$ time steps (e.g., days) while ensuring that the infrastructure network's safety constraints that limit the users' demand are satisfied at all time steps. We propose two different algorithms for the two different scenarios when, 1) the utility function is chosen by the central coordinator to achieve a social objective and 2) the utility function is defined by the price response under the assumption that the users are self-interested agents. We prove that both algorithms enjoy $\tilde{O} (T^{2/3})$ regret with high probability. We then apply these algorithms to demand response pricing for the smart grid and numerically demonstrate their effectiveness.
Automated Data-Driven Model Extraction and Validation of Inverter Dynamics with Grid Support Function
Authors: Sunil Subedi, Bidur Poudel, Pooja Aslami, Robert Fourney, Hossein Moradi Rekabdarkolaee, Reinaldo Tonkoski, Timothy M. Hansen
Date: 2023-07-28 00:00:00
Tasks: Model extraction

This research focuses on the evolving dynamics of the power grid, where traditional synchronous generators are being replaced by non-synchronous power electronic converter (PEC)-interfaced renewable energy sources. The non-linear dynamics must be accurately modeled to ensure the stability of future converter-dominated power systems (CDPS). However, obtaining comprehensive dynamic models becomes more complex and computationally intensive as the system grows. This study proposes a scalable and automated data-driven partitioned modeling framework for CDPS dynamics. The method constructs reduced-ordered dynamic linear transfer function models using input-output measurements from a PEC switching model. Validation experiments were conducted on single-house and multi-house scenarios, demonstrating high accuracy (over 97%) and significant computational speed improvements (6.5 times faster) compared to comprehensive models. This framework and modeling approach offers valuable insights for efficient analysis of power system dynamics, aiding in planning, operation, and dispatch.
A Schrödinger Equation for Evolutionary Dynamics
Authors: Vi D. Ao, Duy V. Tran, Kien T. Pham, Duc M. Nguyen, Huy D. Tran, Tuan K. Do, Van H. Do, Trung V. Phan
Date: 2023-07-29 00:00:00

We establish an analogy between the Fokker-Planck equation describing evolutionary landscape dynamics and the Schr\"{o}dinger equation which characterizes quantum mechanical particles, showing how a population with multiple genetic traits evolves analogously to a wavefunction under a multi-dimensional energy potential in imaginary time. Furthermore, we discover within this analogy that the stationary population distribution on the landscape corresponds exactly to the ground-state wavefunction. This mathematical equivalence grants entry to a wide range of analytical tools developed by the quantum mechanics community, such as the Rayleigh-Ritz variational method and the Rayleigh-Schr\"{o}dinger perturbation theory, allowing us to not only make reasonable quantitative assessments but also explore fundamental biological inquiries. We demonstrate the effectiveness of these tools by estimating the population success on landscapes where precise answers are elusive, and unveiling the ecological consequences of stress-induced mutagenesis -- a prevalent evolutionary mechanism in pathogenic and neoplastic systems. We show that, even in a unchanging environment, a sharp mutational burst resulting from stress can always be advantageous, while a gradual increase only enhances population size when the number of relevant evolving traits is limited. Our interdisciplinary approach offers novel insights, opening up new avenues for deeper understanding and predictive capability regarding the complex dynamics of evolving populations.
Synthetic pre-training for neural-network interatomic potentials
Authors: John L. A. Gardner, Kathryn T. Baker, Volker L. Deringer
Date: 2023-07-24 00:00:00
Tasks: Graph Neural Network

Machine learning (ML) based interatomic potentials have transformed the field of atomistic materials modelling. However, ML potentials depend critically on the quality and quantity of quantum-mechanical reference data with which they are trained, and therefore developing datasets and training pipelines is becoming an increasingly central challenge. Leveraging the idea of "synthetic" (artificial) data that is common in other areas of ML research, we here show that synthetic atomistic data, themselves obtained at scale with an existing ML potential, constitute a useful pre-training task for neural-network interatomic potential models. Once pre-trained with a large synthetic dataset, these models can be fine-tuned on a much smaller, quantum-mechanical one, improving numerical accuracy and stability in computational practice. We demonstrate feasibility for a series of equivariant graph-neural-network potentials for carbon, and we carry out initial experiments to test the limits of the approach.