pwc-archive/papers-with-abstracts huggingface dataset:
Extended Graph Assessment Metrics for Graph Neural Networks
Authors: Tamara T. Mueller, Sophie Starck, Leonhard F. Feiner, Kyriaki-Margarita Bintsi, Daniel Rueckert, Georgios Kaissis
Date: 2023-07-13 00:00:00
Tasks: regression

When re-structuring patient cohorts into so-called population graphs, initially independent data points can be incorporated into one interconnected graph structure. This population graph can then be used for medical downstream tasks using graph neural networks (GNNs). The construction of a suitable graph structure is a challenging step in the learning pipeline that can have severe impact on model performance. To this end, different graph assessment metrics have been introduced to evaluate graph structures. However, these metrics are limited to classification tasks and discrete adjacency matrices, only covering a small subset of real-world applications. In this work, we introduce extended graph assessment metrics (GAMs) for regression tasks and continuous adjacency matrices. We focus on two GAMs in specific: \textit{homophily} and \textit{cross-class neighbourhood similarity} (CCNS). We extend the notion of GAMs to more than one hop, define homophily for regression tasks, as well as continuous adjacency matrices, and propose a light-weight CCNS distance for discrete and continuous adjacency matrices. We show the correlation of these metrics with model performance on different medical population graphs and under different learning settings.
Robust constrained nonlinear Model Predictive Control with Gated Recurrent Unit model -- Extended version
Authors: Irene Schimperna, Lalo Magni
Date: 2023-07-19 00:00:00
Tasks: model, Model Predictive Control

In this paper we propose a robust Model Predictive Control where a Gated Recurrent Unit network model is used to learn the input-output dynamic of the system under control. Robust satisfaction of input and output constraints and recursive feasibility in presence of model uncertainties are achieved using a constraint tightening approach. Moreover, new terminal cost and terminal set are introduced in the Model Predictive Control formulation to guarantee Input-to-State Stability of the closed loop system with respect to the uncertainty term.
Solving scalability issues in calculating PV hosting capacity in low voltage distribution networks
Authors: Tomislav Antic, Andrew Keane, Tomislav Capuder
Date: 2023-07-19 00:00:00

The share of end-users with installed rooftop photovoltaic (PV) systems is continuously growing. Since most end-users are located at the low voltage (LV) level and due to technical limitations of LV networks, it is necessary to calculate PV hosting capacity. Most approaches in calculating a network's hosting capacity are based on three-phase optimal power flow (OPF) formulations. Linearized and relaxed three-phase OPF formulations respectively lose their accuracy and exactness when applied to solve the hosting capacity problem, and only non-linear programming (NLP) models guarantee the exact solution. Compared to linearized or relaxed models, NLP models require a higher computational time for finding an optimal solution. The binary variables uplift the problem to mixed-integer (MI)NLP and increase the computational burden. To resolve the scalability issues in calculating the hosting capacity of single-phase connected PVs, we propose a method that does not entail binary variables but still ensures that PVs are not connected to more than one phase at a time. Due to a risk of a sub-optimal solution, the proposed approach is compared to the results obtained by the MINLP formulation. The comparison includes values of the solution time and technical quantities such as network losses, voltage deviations, and voltage unbalance factor.
Using Circulation to Mitigate Spurious Equilibria in Control Barrier Function -- Extended Version
Authors: Vinicius Mariano Goncalves, Prashanth Krishnamurthy, Anthony Tzes, Farshad Khorrami
Date: 2023-07-19 00:00:00

Control Barrier Functions and Quadratic Programming are increasingly used for designing controllers that consider critical safety constraints. However, like Artificial Potential Fields, they can suffer from the stable spurious equilibrium point problem, which can result in the controller failing to reach the goal. To address this issue, we propose introducing circulation inequalities as a constraint. These inequalities force the system to explicitly circulate the obstacle region in configuration space, thus avoiding undesirable equilibria. We conduct a theoretical analysis of the proposed framework and demonstrate its efficacy through simulation studies. By mitigating spurious equilibria, our approach enhances the reliability of CBF-based controllers, making them more suitable for real-world applications.
A Physics-Informed Data-Driven Fault Location Method for Transmission Lines Using Single-Ended Measurements with Field Data Validation
Authors: Yiqi Xing, Yu Liu, Dayou Lu, Xinchen Zou, Xuming He
Date: 2023-07-19 00:00:00

Data driven transmission line fault location methods have the potential to more accurately locate faults by extracting fault information from available data. However, most of the data driven fault location methods in the literature are not validated by field data for the following reasons. On one hand, the available field data during faults are very limited for one specific transmission line, and using field data for training is close to impossible. On the other hand, if simulation data are utilized for training, the mismatch between the simulation system and the practical system will cause fault location errors. To this end, this paper proposes a physics-informed data-driven fault location method. The data from a practical fault event are first analyzed to extract the ranges of system and fault parameters such as equivalent source impedances, loading conditions, fault inception angles (FIA) and fault resistances. Afterwards, the simulation system is constructed with the ranges of parameters, to generate data for training. This procedure merges the gap between simulation and practical power systems, and at the same time considers the uncertainty of system and fault parameters in practice. The proposed data-driven method does not require system parameters, only requires instantaneous voltage and current measurements at the local terminal, with a low sampling rate of several kHz and a short fault time window of half a cycle before and after the fault occurs. Numerical experiments and field data experiments clearly validate the advantages of the proposed method over existing data driven methods.
Control of Small Spacecraft by Optimal Output Regulation: A Reinforcement Learning Approach
Authors: Joao Leonardo Silva Cotta, Omar Qasem, Paula do Vale Pereira, Hector Gutierrez
Date: 2023-07-18 00:00:00
Tasks: reinforcement-learning

The growing number of noncooperative flying objects has prompted interest in sample-return and space debris removal missions. Current solutions are both costly and largely dependent on specific object identification and capture methods. In this paper, a low-cost modular approach for control of a swarm flight of small satellites in rendezvous and capture missions is proposed by solving the optimal output regulation problem. By integrating the theories of tracking control, adaptive optimal control, and output regulation, the optimal control policy is designed as a feedback-feedforward controller to guarantee the asymptotic tracking of a class of reference input generated by the leader. The estimated state vector of the space object of interest and communication within satellites is assumed to be available. The controller rejects the nonvanishing disturbances injected into the follower satellite while maintaining the closed-loop stability of the overall leader-follower system. The simulation results under the Basilisk-ROS2 framework environment for high-fidelity space applications with accurate spacecraft dynamics, are compared with those from a classical linear quadratic regulator controller, and the results reveal the efficiency and practicality of the proposed method.
Platelet Inventory Management with Approximate Dynamic Programming
Authors: Hossein Abouee-Mehrizi, Mahdi Mirjalili, Vahid Sarhangian
Date: 2023-07-18 00:00:00
Tasks: Management

We study a stochastic perishable inventory control problem with endogenous (decision-dependent) uncertainty in shelf-life of units. Our primary motivation is determining ordering policies for blood platelets. Determining optimal ordering quantities is a challenging task due to the short maximum shelf-life of platelets (3-5 days after testing) and high uncertainty in daily demand. We formulate the problem as an infinite-horizon discounted Markov Decision Process (MDP). The model captures salient features observed in our data from a network of Canadian hospitals and allows for fixed ordering costs. We show that with uncertainty in shelf-life, the value function of the MDP is non-convex and key structural properties valid under deterministic shelf-life no longer hold. Hence, we propose an Approximate Dynamic Programming (ADP) algorithm to find approximate policies. We approximate the value function using a linear combination of basis functions and tune the parameters using a simulation-based policy iteration algorithm. We evaluate the performance of the proposed policy using extensive numerical experiments in parameter regimes relevant to the platelet inventory management problem. We further leverage the ADP algorithm to evaluate the impact of ignoring shelf-life uncertainty. Finally, we evaluate the out-of-sample performance of the ADP algorithm in a case study using real data and compare it to the historical hospital performance and other benchmarks. The ADP policy can be computed online in a few minutes and results in more than 50% lower expiry and shortage rates compared to the historical performance. In addition, it performs better or as well as an exact policy that ignores uncertainty in shelf-life and becomes hard to compute for larger instance of the problem.
Assessing the value of energy storage systems for distribution grid applications
Authors: Sahar Moghimian Hoosh, Henni Ouerdane, Vladimir Terzija, David Pozo
Date: 2023-07-18 00:00:00
Tasks: Management

We analyze the potential benefits that energy storage systems (ESS) can bring to distribution networks in terms of cost, stability and flexibility. We propose an optimization model for the optimal sizing, siting, and operation of storage systems in distribution grids. A DistFlow formulation is used for modeling the AC power flow. The ESS model is based on a generic formulation that captures the charging and discharging modes' complementarity. The resulting optimization model is stated as a mixed-integer quadratically constrained program (MIQCP) problem. The optimization model is assessed on the modified 33-bus IEEE network, which includes renewable energy resources and ESS. The obtained results show that ESS can offer various important benefits such as overall cost reduction, energy arbitrage, voltage regulation, and congestion management in distribution grids. These findings highlight the significance of utilizing ESS technologies to provide aggregated value through various grid services, extending beyond energy arbitrage alone.
Impedance Reshaping Method of DFIG System Based on Compensating Rotor Current Dynamic to Eliminate PLL Influence
Authors: Xiaoling Xiong, Bochen Luo, Longcan Li, Ziming Sun, Frede Blaabjerg
Date: 2023-07-18 00:00:00

The phase-locked loop (PLL) used in the doubly fed induction generator (DFIG) can cause frequency coupling phenomena, which will give negative resistance characteristics ofthe DFIG at low frequency, resulting in stability issues under weak grid operation. Based on the multi-input-multi-output (MIMO) impedance model of DFIG system, it is found that the frequency coupling phenomena is mainly introduced by the transfer function matrix related to rotor current dynamic. This paper presents an improved impedance reshaping method based on compensating rotor current dynamic to reduce the influence of PLL, in which the rotor current dynamic is compensated before being introduced to the PI controller. Thus, the frequency coupling effect can be almost eliminated and the stability of DFIG is improved a lot. Furthermore, a simplified compensation method is proposed,which can easily be implemented. Robustness analysis is performed to illustrate the availability of the proposed methods when the system operating conditions and parameters vary. Finally, simulations based on MATLAB/Simulink are also carried out, and the results validate the effectiveness of the proposed methods.
A Quick Guide for the Iterated Extended Kalman Filter on Manifolds
Authors: Jianzhu Huai, Xiang Gao
Date: 2023-07-18 00:00:00
Tasks: State Estimation

The extended Kalman filter (EKF) is a common state estimation method for discrete nonlinear systems. It recursively executes the propagation step as time goes by and the update step when a set of measurements arrives. In the update step, the EKF linearizes the measurement function only once. In contrast, the iterated EKF (IEKF) refines the state in the update step by iteratively solving a least squares problem. The IEKF has been extended to work with state variables on manifolds which have differentiable $\boxplus$ and $\boxminus$ operators, including Lie groups. However, existing descriptions are often long, deep, and even with errors. This note provides a quick reference for the IEKF on manifolds, using freshman-level matrix calculus. Besides the bare-bone equations, we highlight the key steps in deriving them.
Functional Observability, Structural Functional Observability and Optimal Sensor Placement
Authors: Yuan Zhang, Tyrone Fernando, Mohamed Darouach
Date: 2023-07-18 00:00:00

In this paper, new characterizations for functional observability, functional detectability, and structural functional observability (SFO) are developed, and based on them, the related optimal sensor placement problems are investigated. A novel concept of modal functional observability coinciding with the notion of modal observability is proposed. This notion introduces necessary and sufficient conditions for functional observability and detectability in a unified way without resorting to system observability decomposition, and facilitates the design of a functionally observable/detectable system. Afterwards, SFO is redefined rigorously from a generic perspective, contrarily to the definition of structural observability. A complete graph-theoretic characterization for SFO is proposed. Based on these results, the problems of selecting the minimal sensors from a prior set to achieve functional observability and SFO are shown to be NP-hard. Nevertheless, supermodular set functions are established, leading to greedy heuristics that can find approximation solutions to these problems with provable guarantees in polynomial time. A closed-form solution along with a constructive procedure is also given for the unconstrained case on systems with diagonalizable state matrices. Notably, our results also yield a polynomial-time verifiable case for structural target controllability, a problem that may be hard otherwise.
Adaptive Data-Driven Prediction in a Building Control Hierarchy: A Case Study of Demand Response in Switzerland
Authors: Jicheng Shi, Yingzhao Lian, Christophe Salzmann, Colin N. Jones
Date: 2023-07-17 00:00:00
Tasks: LEMMA

By providing various services, such as Demand Response (DR), buildings can play a crucial role in the energy market due to their significant energy consumption. However, effectively commissioning buildings for such desired functionalities requires significant expert knowledge and design effort, considering the variations in building dynamics and intended use. In this study, we introduce an adaptive data-driven prediction scheme based on Willems' Fundamental Lemma within the building control hierarchy. This scheme offers a versatile, flexible, and user-friendly interface for diverse prediction and control objectives. We provide an easy-to-use tuning process and an adaptive update pipeline for the scheme, both validated through extensive prediction tests. We evaluate the proposed scheme by coordinating a building and an energy storage system to provide Secondary Frequency Control (SFC) in a Swiss DR program. Specifically, we integrate the scheme into a three-layer hierarchical SFC control framework, and each layer of this hierarchy is designed to achieve distinct operational goals. Apart from its flexibility, our approach significantly improves cost efficiency, resulting in a 28.74% reduction in operating costs compared to a conventional control scheme, as demonstrated by a 52-day experiment in an actual building. Our findings emphasize the potential of the proposed scheme to reduce the commissioning costs of advanced building control strategies and to facilitate the adoption of new techniques in building control.
AC Power Cycling Test Setup and Condition Monitoring Tools for SiC-Based Traction Inverters
Authors: Masoud Farhadi, Bhanu Teja Vankayalapati, Rahman Sajadi, Bilal Akin
Date: 2023-07-15 00:00:00

AC power cycling tests allow the most realistic reliability assessment by applying close to real stress to the device or module under test to meet functional safety standards, which is highly critical for traction applications. This paper presents a comprehensive guideline and shares critical know-how to develop a 120 KVA AC power cycling test setup for high-power Silicon Carbide (SiC) modules. As of today, traction applications can not generate an early warning signal for drivers to replace critical components on time. For this purpose, the suitable precursors for all dominant failure mechanisms are discussed, and the corresponding condition monitoring tools are proposed to monitor the device aging on power converters. These condition monitoring tools are integrated into the built-in desaturation protection circuit of the electric vehicle (EV) gate driver for low-cost, practical implementation. The on-resistance of all twelve switches is monitored online as a temperature-sensitive electrical parameter (TSEP) to measure the junction temperature of the devices. To avoid heavy processing load in the microcontroller, the out-of-order equivalent time sampling technique is developed for data sampling, which leads to a measurement error of less than 1.5%. In addition, the design considerations regarding common mode noise and aging effect on DESAT protection are investigated, and experimental findings are presented.
Continuous Approximation of Stochastic Maps for Modeling Asymmetric Cell Division
Authors: Aviv Arcobi, Stanislav Burov
Date: 2023-07-18 00:00:00

Cell size control and homeostasis is a major topic in cell biology yet to be fully understood. Several growth laws like the timer, adder, and sizer were proposed, and mathematical approaches that model cell growth and division were developed. This study focuses on utilizing stochastic map modeling for investigating asymmetric cell division. We establish a mapping between the description of cell growth and division and the Ornstein-Uhlenbeck process with dichotomous noise. We leverage this mapping to achieve analytical solutions and derive a closed-form expression for the stable cell size distribution under asymmetric division. To validate our findings, we conduct numerical simulations encompassing several cell growth scenarios. Our approach allows us to obtain a precise criterion for a bi-phasic behavior of the cell size. While for the case of the sizer scenario, a transition from the uni-modal phase to bi-modal is always possible, given sufficiently large asymmetry at the division, the affine-linear approximation of the adder scenario invariably yields uni-modal distribution.
A Computational Topology-based Spatiotemporal Analysis Technique for Honeybee Aggregation
Authors: Golnar Gharooni-Fard, Morgan Byers, Varad Deshmukh, Elizabeth Bradley, Carissa Mayo, Chad Topaz, Orit Peleg
Date: 2023-07-19 00:00:00
Tasks: Change Point Detection, Dimensionality Reduction, Topological Data Analysis

A primary challenge in understanding collective behavior is characterizing the spatiotemporal dynamics of the group. We employ topological data analysis to explore the structure of honeybee aggregations that form during trophallaxis, which is the direct exchange of food among nestmates. From the positions of individual bees, we build topological summaries called CROCKER matrices to track the morphology of the group as a function of scale and time. Each column of a CROCKER matrix records the number of topological features, such as the number of components or holes, that exist in the data for a range of analysis scales at a given point in time. To detect important changes in the morphology of the group from this information, we first apply dimensionality reduction techniques to these matrices and then use classic clustering and change-point detection algorithms on the resulting scalar data. A test of this methodology on synthetic data from an agent-based model of honeybees and their trophallaxis behavior shows two distinct phases: a dispersed phase that occurs before food is introduced, followed by a food-exchange phase during which aggregations form. We then move to laboratory data, successfully detecting the same two phases across multiple experiments. Interestingly, our method reveals an additional phase change towards the end of the experiments, suggesting the possibility of another dispersed phase that follows the food-exchange phase.
Single shot diagnosis of ion channel dysfunction from assimilation of cell membrane dynamics
Authors: Paul G Morris, Joseph D. Taylor, Julian F. R. Paton, Alain Nogaret
Date: 2023-07-19 00:00:00

Many neurological diseases originate in the dysfunction of cellular ion channels. Their diagnosis presents a challenge especially when alterations in the complement of ion channels are a priori unknown. Current approaches based on voltage clamps lack the throughput necessary to identify the mutations causing changes in electrical activity. Here, we introduce a single-shot method for diagnosing changes in the complement of ion channels from changes in the electrical activity of a cell. We developed data assimilation (DA) to estimate the parameters of individual ion channels and from these parameters reconstruct the ionic currents of hippocampal CA1 neurons to within 11% of their actual value. DA correctly predicts which ionic current is altered and by how much after we blocked the BK, SK, A and HCN channels with selective antagonists of known potency. We anticipate our assay will transform the treatment of neurological disease through comprehensive diagnosis and drug screening.
Exploring Cognitive Paradoxes in Video Games: A Quantum Mechanical Perspective
Authors: Ivan S. Maksymov, Ganna Pogrebna
Date: 2023-06-23 00:00:00
Tasks: Decision Making

This paper introduces a quantum-mechanical model that bridges the realms of cognition and quantum mechanics, offering a novel perspective on decision-making under risk and perceptual reversals. By integrating quantum theories addressing decision-theoretic anomalies with examples from immersive video games like "Deal or No Deal", we seek to elucidate complex human cognitive behaviours. Study 1 showcases the proposed quantum model's superiority over traditional decision-making approaches using the "Deal or No Deal" video game experiment. In Study 2, we apply our model to bistable perceptions, taking the Necker cube from the Necker game as a primary example. While previous works have hinted at connections between quantum mechanics and cognition, Study 3 provides a more tangible link, likening the physics that underpins quantum tunnelling to an eye blink's role in perceptual reversals. Conclusively, our model displays a promising ability to interpret diverse optical illusions and psychological phenomena, marking a significant stride in understanding human decision making.