pwc-archive/papers-with-abstracts huggingface dataset:
Cylindrical Battery Fault Detection under Extreme Fast Charging: A Physics-based Learning Approach
Authors: Roya Firoozi, Sara Sattarzadeh, Satadru Dey
Date: 2021-05-05 00:00:00
Tasks: Fault Detection

High power operation in extreme fast charging significantly increases the risk of internal faults in Electric Vehicle batteries which can lead to accelerated battery failure. Early detection of these faults is crucial for battery safety and widespread deployment of fast charging. In this setting, we propose a real-time {detection} framework for battery voltage and thermal faults. A major challenge in battery fault detection arises from the effect of uncertainties originating from sensor inaccuracies, nominal aging, or unmodelled dynamics. Inspired by physics-based learning, we explore a detection paradigm that combines physics-based models, model-based detection observers, and data-driven learning techniques to address this challenge. Specifically, we construct the {detection} observers based on an experimentally identified electrochemical-thermal model, and subsequently design the observer tuning parameters following Lyapunov's stability theory. Furthermore, we utilize Gaussian Process Regression technique to learn the model and measurement uncertainties which in turn aid the {detection} observers in distinguishing faults and uncertainties. Such uncertainty learning essentially helps suppressing their effects, potentially enabling early detection of faults. We perform simulation and experimental case studies on the proposed fault {detection} scheme verifying the potential of physics-based learning in early detection of battery faults.
Optimal Phasor Measurement Unit Placement Using a Honey Bee Mating Optimization (HBMO) Technique Considering Measurement Loss and Line Outages
Authors: Lorena Palmero, Ugur Saritac, Ali Dehghan Chaharabi
Date: 2021-05-05 00:00:00

Phasor measurement units (PMUs) are important devices for protection, monitoring, and control of modern power systems. Unlike the supervisory control and data acquisition (SCADA) system which only measure the magnitude, PMUs can provide a set of synchronized phasor measurements with high sampling rate with the accuracy of less than one second, for better system monitoring especially during the outages. Since the PMUs are costly devices, system operator is not able to install a PMU at each bus to maximize the observability of system. To this end, determination of the number of PMUs in the system for having a 100% observability is pivotal. In this paper, honey bee mating optimization (HBMO) approach is utilized to allocate PMUs in modern power systems. Moreover, the impact of line outages, as well as measurement loses, are considered. The simulation results show the effectiveness of the proposed method on solving the PMU placement problem.
Numerical Gaussian process Kalman filtering for spatiotemporal systems
Authors: Armin Küper, Steffen Waldherr
Date: 2021-05-05 00:00:00
Tasks: BIG-bench Machine Learning, Gaussian Processes, Physics-informed machine learning

We present a novel Kalman filter for spatiotemporal systems called the numerical Gaussian process Kalman filter (GPKF). Numerical Gaussian processes have recently been introduced as a physics informed machine learning method for simulating time-dependent partial differential equations without the need for spatial discretization. We bring numerical GPs into probabilistic state space form. This model is linear and its states are Gaussian distributed. These properties enable us to embed the numerical GP state space model into the recursive Kalman filter algorithm. We showcase the method using two case studies.
A Characterization of All Passivizing Input-Output Transformations of a Passive-Short System
Authors: Miel Sharf, Daniel Zelazo
Date: 2019-11-09 00:00:00
Tasks: All, LEMMA

Passivity theory is one of the cornerstones of control theory, as it allows one to prove stability of a large-scale system while treating each component separately. In practice, many systems are not passive, and must be passivized in order to be included in the framework of passivity theory. Input-output transformations are the most general tool for passivizing systems, generalizing output-feedback and input-feedthrough. In this paper, we classify all possible input-output transformations that map a system with given shortage of passivity to a system with prescribed excess of passivity. We do so by using the connection between passivity theory and cones for SISO systems, and using the S-lemma for MIMO systems. We also present several possible applications of our results, including simultaneous passivation of multiple systems or with respect to multiple equilibria, as well as optimization problems such as $\mathcal{L}_2$-gain minimization. We also exhibit our results in a case study about synchronization in a network of non-passive faulty agents.
An Ensemble Forecasting Technique for Photovoltaic Power Generation
Authors: Rustam Kumar
Date: 2021-05-05 00:00:00

To cater the rapidly growing demand for electricity leading to the integration of renewable energy sources in power system. Due to intermittent nature of renewables, it also brings challenges for research community during the planning and operation stage in power system. Therefore it is primary necessity of the community to develop an accurate forecasting technique to solve the intermittency problem. In this report, A forecasting technique is proposed based on ensemble of state of the art forecasting techniques. For performance comparison among the techniques, GEFCom2014 meteorological data are used to predict the photovoltaic power, and the obtained results are included in this report.
Improved current reference calculation for MMCs interal energy balancing control
Authors: Daniel Westerman Spier, Eduardo Prieto-Araujo, Joaquim López-Mestre, Oriol Gomis-Bellmunt
Date: 2021-05-05 00:00:00

The paper addresses an improved inner current reference calculation to be employed in the control of modular multilevel converters operating during either balanced or unbalanced conditions. The suggested reference calculation is derived based on the AC and DC additive and differential voltage components applied to the upper and lower arms of the converter. In addition, the impacts caused not only by the AC network's impedances but also by the MMC's arm impedances are also considered during the derivation of the AC additive current reference expressions. Another issue discussed in this article regards that singular voltage conditions, where the positive-sequence component is equal to the negative one, may occur not only in the AC network but also internally (within the converter's applied voltages). Several different inner current reference calculation methods are compared and their applicability during the former fault conditions is analyzed. The paper presents a detailed formulation of the inner current reference calculation and applies it to different unbalanced AC grid faults where it is shown that the presented approach can be potentially used to maintain the internal energy of the converter balanced during normal and fault conditions.
Fast Spline Trajectory Planning: Minimum Snap and Beyond
Authors: Declan Burke, Airlie Chapman, Iman Shames
Date: 2021-05-04 00:00:00
Tasks: Trajectory Planning

In this paper, we study spline trajectory generation via the solution of two optimisation problems: (i) a quadratic program (QP) with linear equality constraints and (ii) a nonlinear and nonconvex optimisation program. We propose an efficient algorithm to solve (i), which we then leverage to use in an iterative algorithm to solve (ii). Both the first algorithm and each iteration of the second algorithm have linear computational complexity in the number of spline segments. The scaling of each algorithm is such that we are able to solve the two problems faster than state-of-the-art methods and in times amenable to real-time trajectory generation requirements. The trajectories we generate are applicable to differentially flat systems, a broad class of mechanical systems, which we demonstrate by planning trajectories for a quadrotor.
Model-Free Incremental Adaptive Dynamic Programming Based Approximate Robust Optimal Regulation
Authors: Cong Li, Yongchao Wang, Fangzhou Liu, Qingchen Liu, Martin Buss
Date: 2021-05-04 00:00:00

This paper presents a new formulation for model-free robust optimal regulation of continuous-time nonlinear systems. The proposed reinforcement learning based approach, referred to as incremental adaptive dynamic programming (IADP), exploits measured data to allow the design of the approximate optimal incremental control strategy, which stabilizes the controlled system incrementally under model uncertainties, environmental disturbances, and input saturation. By leveraging the time delay estimation (TDE) technique, we first exploit sensory data to reduce the requirement of a complete dynamics, where measured data are adopted to construct an incremental dynamics that reflects the system evolution in an incremental form. Then, the resulting incremental dynamics serves to design the approximate optimal incremental control strategy based on adaptive dynamic programming, which is implemented as a simplified single critic structure to get the approximate solution to the value function of the Hamilton-Jacobi-Bellman equation. Furthermore, for the critic artificial neural network, experience data are used to design an off-policy weight update law with guaranteed weight convergence. Rather importantly, to address the unintentionally introduced TDE error, we incorporate a TDE error bound related term into the cost function, whereby the TDE error is attenuated during the optimization process. The system stability proof and the weight convergence proof are provided. Numerical simulations are conducted to validate the effectiveness and superiority of our proposed IADP, especially regarding the reduced control energy expenditure and the enhanced robustness.
On Moment Matching for Stochastic Systems
Authors: Giordano Scarciotti, Andrew R. Teel
Date: 2021-05-04 00:00:00

In this paper we study the problem of model reduction by moment matching for stochastic systems. We characterize the mathematical object which generalizes the notion of moment to stochastic differential equations and we find a class of models which achieve moment matching. However, differently from the deterministic case, these reduced-order models cannot be considered "simpler" because of the high computational cost paid to determine the moment. To overcome this difficulty, we relax the moment matching problem in two different ways and we present two classes of reduced-order models which, approximately matching the stochastic moment, are computationally tractable.
Plasmonic metamaterial based virus detection system: a review
Authors: Mohammad Muntasir Hassan, Farhan Sadik Sium, Fariba Islam, Sajid Muhaimin Choudhury
Date: 2020-11-29 00:00:00

Our atmosphere is constantly changing and new pathogens are erupting now and then and the existing pathogens are mutating continuously. Some of these pathogens, such as SARS-CoV-2, become so deadly that they put the whole technological advancement of healthcare under challenge. Within this very decade several other deadly virus outbreaks were witnessed by humans such as Zika virus, Ebola virus, MERS-coronavirus etc. Though conventional techniques have succeeded in detecting these viruses to some extent, these techniques are time-consuming, costly, and require trained human-resources. Plasmonic metamaterial-based biosensors might pave the way to low-cost rapid virus detection. So this review discusses in details the latest development in plasmonics and metamaterial-based biosensors for virus, viral particles and antigen detection and the future direction of research in this field. Emergence of quantum properties in biosensing, application of machine learning, artificial intelligence and novel materials in biosensing is also discussed in brief.
Non-equilibrium phase separation in mixtures of catalytically active particles: size dispersity and screening effects
Authors: Vincent Ouazan-Reboul, Jaime Agudo-Canalejo, Ramin Golestanian
Date: 2021-05-05 00:00:00

Biomolecular condensates in cells are often rich in catalytically-active enzymes. This is particularly true in the case of the large enzymatic complexes known as metabolons, which contain different enzymes that participate in the same catalytic pathway. One possible explanation for this self-organization is the combination of the catalytic activity of the enzymes and a chemotactic response to gradients of their substrate, which leads to a substrate-mediated effective interaction between enzymes. These interactions constitute a purely non-equilibrium effect and show exotic features such as non-reciprocity. Here, we analytically study a model describing the phase separation of a mixture of such catalytically-active particles. We show that a Michaelis-Menten-like dependence of the particles' activities manifests itself as a screening of the interactions, and that a mixture of two differently-sized active species can exhibit phase separation with transient oscillations. We also derive a rich stability phase diagram for a mixture of two species with both concentration-dependent activity and size dispersity. This work highlights the variety of possible phase separation behaviours in mixtures of chemically-active particles, which provides an alternative pathway to the passive interactions more commonly associated with phase separation in cells. Our results highlight non-equilibrium organizing principles that can be important for biologically relevant liquid-liquid phase separation.
Order flow in the financial markets from the perspective of the Fractional Lévy stable motion
Authors: Vygintas Gontis
Date: 2021-05-05 00:00:00
Tasks: Model Selection, Time Series, Time Series Analysis

It is a challenging task to identify the best possible models based on given empirical data of observed time series. Though the financial markets provide us with a vast amount of empirical data, the best model selection is still a big challenge for researchers. The widely used long-range memory and self-similarity estimators give varying values of the parameters as these estimators themselves are developed for the specific models of time series. Here we investigate from the general fractional L\'evy stable motion perspective the order disbalance time series constructed from the limit order book data of the financial markets. Our results suggest that previous findings of persistence in order flow could be related to the power-law distribution of order sizes and other deviations from the normal distribution. Still, orders have stable estimates of anti-correlation for the 18 randomly selected stocks when Absolute value and Higuchi's estimators are implemented. Though the burst duration analysis based on the first passage problem of time series and implemented in this research gives slightly higher estimates of the Hurst and memory parameters, it qualitatively supports the importance of the power-law distribution of order sizes.
Predictive landscapes hidden beneath biological cellular automata
Authors: Lars Koopmans, Hyun Youk
Date: 2021-05-05 00:00:00
Tasks: Protein Folding

To celebrate Hans Frauenfelder's achievements, we examine energy(-like) "landscapes" for complex living systems. Energy landscapes summarize all possible dynamics of some physical systems. Energy(-like) landscapes can explain some biomolecular processes, including gene expression and, as Frauenfelder showed, protein folding. But energy-like landscapes and existing frameworks like statistical mechanics seem impractical for describing many living systems. Difficulties stem from living systems being high dimensional, nonlinear, and governed by many, tightly coupled constituents that are noisy. The predominant modeling approach is devising differential equations that are tailored to each living system. This ad hoc approach faces the notorious "parameter problem": models have numerous nonlinear, mathematical functions with unknown parameter values, even for describing just a few intracellular processes. One cannot measure many intracellular parameters or can only measure them as snapshots in time. Another modeling approach uses cellular automata to represent living systems as discrete dynamical systems with binary variables. Quantitative (Hamiltonian-based) rules can dictate cellular automata (e.g., Cellular Potts Model). But numerous biological features, in current practice, are qualitatively described rather than quantitatively (e.g., gene is (highly) expressed or not (highly) expressed). Cellular automata governed by verbal rules are useful representations for living systems and can mitigate the parameter problem. However, they can yield complex dynamics that are difficult to understand because much of the existing mathematical tools and theorems apply to continuous but not discrete dynamical systems. Recent studies found ways to overcome this challenge by discovering a predictive "landscape" that yield low-dimensional representations of cellular automata dynamics. We review these studies.
Sustainability of Collusion and Market Transparency in a Sequential Search Market: a Generalization
Authors: Jacopo De Tullio, Giuseppe Puleio
Date: 2021-05-05 00:00:00
Tasks: Mathematical Reasoning

The present work generalizes the analytical results of Petrikaite (2016) to a market where more than two firms interact. As a consequence, for a generic number of firms in the oligopoly model described by Janssen et al (2005), the relationship between the critical discount factor which sustains the monopoly collusive allocation and the share of perfectly informed buyers is non-monotonic, reaching a unique internal point of minimum. The first section locates the work within the proper economic framework. The second section hosts the analytical computations and the mathematical reasoning needed to derive the desired generalization, which mainly relies on the Leibniz rule for the differentiation under the integral sign and the Bounded Convergence Theorem.
Phase-Space Function Recovery for Moving Target Imaging in SAR by Convex Optimization
Authors: Sean Thammakhoune, Bariscan Yonel, Eric Mason, Birsen Yazıcı, Yonina C. Eldar
Date: 2021-05-05 00:00:00
Tasks: Computational Efficiency

In this paper, we present an approach for ground moving target imaging (GMTI) and velocity recovery using synthetic aperture radar. We formulate the GMTI problem as the recovery of a phase-space reflectivity (PSR) function which represents the strengths and velocities of the scatterers in a scene of interest. We show that the discretized PSR matrix can be decomposed into a rank-one, and a highly sparse component corresponding to the stationary and moving scatterers, respectively. We then recover the two distinct components by solving a constrained optimization problem that admits computationally efficient convex solvers within the proximal gradient descent and alternating direction method of multipliers frameworks. Using the structural properties of the PSR matrix, we alleviate the computationally expensive steps associated with rank-constraints, such as singular value thresholding. Our optimization-based approach has several advantages over state-of-the-art GMTI methods, including computational efficiency, applicability to dense target environments, and arbitrary imaging configurations. We present extensive simulations to assess the robustness of our approach to both additive noise and clutter, with increasing number of moving targets. We show that both solvers perform well in dense moving target environments, and low-signal-to-clutter ratios without the need for additional clutter suppression techniques.
Deep Learning for Needle Detection in a Cannulation Simulator
Authors: Jianxin Gao, Ju Lin, Irfan Kil, Ravikiran B. Singapogu, Richard E. Groff
Date: 2021-05-05 00:00:00
Tasks: Deep Learning

Cannulation for hemodialysis is the act of inserting a needle into a surgically created vascular access (e.g., an arteriovenous fistula) for the purpose of dialysis. The main risk associated with cannulation is infiltration, the puncture of the wall of the vascular access after entry, which can cause medical complications. Simulator-based training allows clinicians to gain cannulation experience without putting patients at risk. In this paper, we propose to use deep-learning-based techniques for detecting, based on video, whether the needle tip is in or has infiltrated the simulated fistula. Three categories of deep neural networks are investigated in this work: modified pre-trained models based on VGG-16 and ResNet-50, light convolutional neural networks (light CNNs), and convolutional recurrent neural networks (CRNNs). CRNNs consist of convolutional layers and a long short-term memory (LSTM) layer. A data set of cannulation experiments was collected and analyzed. The results show that both the light CNN and the CRNN achieve better performance than the pre-trained baseline models. The CRNN was implemented in real time on commodity hardware for use in the cannulation simulator, and the performance was verified. Deep-learning video analysis is a viable method for detecting needle state in a low cost cannulation simulator. Our data sets and code are released at https://github.com/axin233/DL_for_Needle_Detection_Cannulation
Sign-sensitivity of metabolic networks: which structures determine the sign of the responses
Authors: Nicola Vassena
Date: 2021-05-05 00:00:00
Tasks: Sensitivity

Perturbations are ubiquitous in metabolism. A central tool to understand and control their influence on metabolic networks is sensitivity analysis, which investigates how the network responds to external perturbations. We follow here a structural approach: the analysis is based on the network stoichiometry only and it does not require any quantitative knowledge of the reaction rates. We consider perturbations of reaction rates and metabolite concentrations, at equilibrium, and we investigate the responses in the network. For general metabolic systems, this paper focuses on the sign of the responses, i.e. whether a response is positive, negative or whether its sign depends on the parameters of the system. In particular, we identify and describe the subnetworks that are the main players in the sign description. These subnetworks are associated to certain kernel vectors of the stoichiometric matrix and are thus independent from the chosen kinetics.
Convergence of maximum likelihood supertree reconstruction
Authors: Lam Si Tung Ho, Vu Dinh
Date: 2021-05-04 00:00:00

Supertree methods are tree reconstruction techniques that combine several smaller gene trees (possibly on different sets of species) to build a larger species tree. The question of interest is whether the reconstructed supertree converges to the true species tree as the number of gene trees increases (that is, the consistency of supertree methods). In this paper, we are particularly interested in the convergence rate of the maximum likelihood supertree. Previous studies on the maximum likelihood supertree approach often formulate the question of interest as a discrete problem and focus on reconstructing the correct topology of the species tree. Aiming to reconstruct both the topology and the branch lengths of the species tree, we propose an analytic approach for analyzing the convergence of the maximum likelihood supertree method. Specifically, we consider each tree as one point of a metric space and prove that the distance between the maximum likelihood supertree and the species tree converges to zero at a polynomial rate under some mild conditions. We further verify these conditions for the popular exponential error model of gene trees.