pwc-archive/papers-with-abstracts huggingface dataset:
Modelling and Analysis of Magnetic Fields from Skeletal Muscle for Valuable Physiological Measurements
Authors: Siming Zuo, Kianoush Nazarpour, Dario Farina, Philip Broser, Hadi Heidari
Date: 2021-04-05 00:00:00

MagnetoMyoGraphy (MMG) is a method of studying muscle function via weak magnetic fields generated from human active organs and tissues. The correspondence between MMG and electromyography means directly derived from the Maxwell-Amp\`ere law. Here, upon briefly describing the principles of voltage distribution inside skeletal muscles due to the electrical stimulation, we provide a protocol to determine the effects of the magnetic field generated from a time-changing action potential propagating in a group of skeletal muscle cells. The position-dependent and the magnetic field behaviour on account of the different currents in muscle fibres are performed in temporal, spectral and spatial domains. The procedure covers identification of the fibre subpopulations inside the fascicles of a given nerve section, characterization of soleus skeletal muscle currents, check of axial intracellular currents, calculation of the generated magnetic field ultimately. We expect this protocol to take approximately 2-3 hours to complete for the whole finite-element analysis.
Robust Control Co-Design with Receding-Horizon MPC
Authors: Austin L. Nash, Herschel C. Pangborn, Neera Jain
Date: 2021-04-05 00:00:00
Tasks: Management, Model Predictive Control

Control co-design (CCD) is a technique for improving the closed-loop performance of systems through the coordinated design of both plant parameters and an optimal control policy. While model predictive control (MPC) is an attractive control strategy for many systems, embedding it within a CCD algorithm presents challenges because obtaining a closed-form solution for this receding-horizon optimization strategy is often not feasible. This paper meets that challenge by including a robust MPC formulation within the inner loop of a CCD algorithm. As exemplified by application to an aircraft thermal management system, the proposed algorithm closely matches the plant design of an open-loop benchmark. However, unlike the open-loop approach, the proposed algorithm can leverage MPC control variables designed a priori to achieve robust online operation under disturbance profiles that differ from those used for design.
Graph Neural Networks Based Detection of Stealth False Data Injection Attacks in Smart Grids
Authors: Osman Boyaci, Amarachi Umunnakwe, Abhijeet Sahu, Mohammad Rasoul Narimani, Muhammad Ismail, Katherine Davis, Erchin Serpedin
Date: 2021-04-05 00:00:00
Tasks: Graph Neural Network

False data injection attacks (FDIAs) represent a major class of attacks that aim to break the integrity of measurements by injecting false data into the smart metering devices in power grids. To the best of authors' knowledge, no study has attempted to design a detector that automatically models the underlying graph topology and spatially correlated measurement data of the smart grids to better detect cyber attacks. The contributions of this paper to detect and mitigate FDIAs are twofold. First, we present a generic, localized, and stealth (unobservable) attack generation methodology and publicly accessible datasets for researchers to develop and test their algorithms. Second, we propose a Graph Neural Network (GNN) based, scalable and real-time detector of FDIAs that efficiently combines model-driven and data-driven approaches by incorporating the inherent physical connections of modern AC power grids and exploiting the spatial correlations of the measurement. It is experimentally verified by comparing the proposed GNN based detector with the currently available FDIA detectors in the literature that our algorithm outperforms the best available solutions by 3.14%, 4.25%, and 4.41% in F1 score for standard IEEE testbeds with 14, 118, and 300 buses, respectively.
A Minimum-Footprint Implementation of Discrete-Time ADRC
Authors: Gernot Herbst
Date: 2021-04-05 00:00:00

To foster the adoption of active disturbance rejection control (ADRC) and support its deployment even on low-cost embedded systems, this article introduces the most efficient implementation of linear discrete-time ADRC to date. While maintaining all features and the exact performance characteristics of the state-space form, computational efforts and storage requirements are reduced to a minimum compared to all existing implementations. This opens up new possibilities to use ADRC in applications with high sample rates, tight timing constraints, or low computational power.
Nodal Frequency Performance of Power Networks
Authors: Huisheng Gao, Hui Yuan, Huanhai Xin, Linbin Huang, Chaoyou Feng
Date: 2021-04-05 00:00:00

This paper investigates how a disturbance in the power network affects the nodal frequencies of certain network buses. To begin with, we show that the inertia of a single generator is in inverse proportion to the initial rate of change of frequency (RoCoF) under disturbances. Then, we present how the initial RoCoF of the nodal frequencies are related to the inertia constants of multiple generators in a power network, which leads to a performance metric to analyze nodal frequency performance. To be specific, the proposed metric evaluates the impact of disturbances on the nodal frequency performance. The validity and effectiveness of the proposed metric are illustrated via simulations on a multi-machine power system.
Suboptimal multirate MPC for five-level inverters
Authors: Joaquin G. Ordonez, Francisco Gordillo, Pablo Montero-Robina, Daniel Limon
Date: 2021-04-05 00:00:00
Tasks: Model Predictive Control

The application of multilevel converters to renewable energy systems is a growing topic due to their advantages in energy efficiency. Regarding its control, model predictive control (MPC) has become very appealing due to its natural consideration of discrete inputs, its optimization capability, and the present-day availability of powerful processing hardware. The main drawback of MPC compared to other control techniques in this field is that the control input is held constant during the sampling period, and it is usually difficult or even impossible to reduce this sampling period because of hardware limitations. For this reason, a multirate MPC algorithm is proposed, which allows to change the control input several times within the sampling period. The optimization problem is simplified and made suboptimal to substantially decrease computational burden. This approach is tested in simulation on a three-phase, five-level diode-clamped converter (DCC) operating in inverted mode with a three-phase resistive load. Results show significant reduction in harmonic distortion at the cost of an increase in the number of commutations with respect to a standard MPC operating at the same sampling period.
Learning in Centralized Nonlinear Model Predictive Control: Application to an Autonomous Tractor-Trailer System
Authors: Erkan Kayacan, Erdal Kayacan, Herman Ramon, Wouter Saeys
Date: 2021-04-05 00:00:00
Tasks: Model Predictive Control, parameter estimation

One of the most critical tasks in tractor operation is the accurate steering during field operations, e.g., accurate trajectory following during mechanical weeding or spraying, to avoid damaging the crop or planting when there is no crop yet. To automate the trajectory following problem of an autonomous tractor-trailer system and also increase its steering accuracy, a nonlinear model predictive control approach has been proposed in this paper. For the state and parameter estimation, moving horizon estimation has been chosen since it considers the state and the parameter estimation within the same problem and also constraints both on inputs and states can be incorporated. The experimental results show the accuracy and the efficiency of the proposed control scheme in which the mean values of the Euclidean error for the tractor and the trailer, respectively, are 6.44 and 3.61 cm for a straight line trajectory and 49.78 and 41.52 cm for a curved line trajectory.
DSRC-Enabled Train Safety Communication System at Unmanned Crossings
Authors: Junsung Choi, Vuk Marojevic, Carl B. Dietrich, Seungyoung Ahn
Date: 2021-04-05 00:00:00

Although wireless technology is available for safety-critical applications, few applications have been used to improve train crossing safety. To prevent potential collisions between trains and vehicles, we present a Dedicated Short-Range Communication (DSRC)-enabled train safety communication system targeting to implement at unmanned crossings. Since our application's purpose is preventing collisions between trains and vehicles, we present a method to calculate the minimum required warning time for head-to-head collision at the train crossing. Furthermore, we define the best- and worst-case scenarios and provide practical measurements at six operating crossings in the U.S. with numerous system configurations such as modulation scheme, transmission power, antenna type, train speed, and vehicle braking distances. From our measurements, we find that the warning application coverage range is independent of the train speed, that the omnidirectional antenna with high transmission power is the best configuration for our system, and that the latency values are mostly less than 5 ms. We use the radio communication coverage to evaluate the time to avoid collision and introduce the safeness level metric. From the measured data, we observe that the DSRC-enabled train safety communication system is feasible for up to 35 mph train speeds which is providing more than 25-30 s time to avoid the collision for 25-65 mph vehicle speeds. Higher train speeds are expected to be safe, but more measurements beyond the 200 m mark with respect to a crossing considered here are needed for a definite conclusion.
Development and Modeling of a Low-voltage DC Distribution Nanogrid with Distributed Generation Systems
Authors: Pedro Ferreira Torres
Date: 2021-04-04 00:00:00

The concept of direct current distribution minigrids has been gaining ground in academia and industry regarding the development of distribution grid applications with high penetration of distributed energy sources and storage systems. The adoption of a direct current distribution system facilitates the integration of sources such as photovoltaic and wind generation and storage systems such as batteries, as these technologies in general operate intrinsically in direct current. This work presents the development of a direct current distribution nanogrid (DCDN) installed in the laboratory of the Grupo de Estudos e Desenvolvimento de Alternativas Energeticas (GEDAE), of the Universidade Federal do Para. The developed grid is composed of three photovoltaic generation and storage systems in battery banks and three load banks, distributed over the 200 m grid in a ring topology, with a 24 Vdc bus. Two simulation methodologies were developed and can reproduce the nanogrid operational behavior under static and dynamic conditions, allowing the evaluation of the grid performance over a day of operation. Tests are also presented with measurements at strategic points of the grid to evaluate the system behavior under specific operating conditions, being normal or under contingency. The results attest the nanogrid ability to reliably supply the loads, as long as it respects the limitations of the implemented power generation and storage capacities. In addition, it was found that the characteristics related to the topology of the commercial charge controller that is used to form the DC distribution nanogrid benefits the power quality for the developed grid size and topology.
A Specification-Guided Framework for Temporal Logic Control of Nonlinear Systems
Authors: Yinan Li, Zhibing Sun, Jun Liu
Date: 2021-04-03 00:00:00
Tasks: Motion Planning

This paper proposes a specification-guided framework for control of nonlinear systems with linear temporal logic (LTL) specifications. In contrast with well-known abstraction-based methods, the proposed framework directly characterizes the winning set, i.e., the set of initial conditions from which a given LTL formula can be realized, over the continuous state space of the system via a monotonic operator. Following this characterization, an algorithm is proposed to practically approximate the operator via an adaptive interval subdivision scheme, which yields a finite-memory control strategy. We show that the proposed algorithm is sound for full LTL specifications, and robustly complete for specifications recognizable by deterministic B\"uchi automata (DBA), the latter in the sense that control strategies can be found whenever the given specification can be satisfied with additional bounded disturbances. Without having to compute and store the abstraction and the resulting product system with the DBA, the proposed method is more memory efficient, which is demonstrated by complexity analysis and performance tests. A pre-processing stage is also devised to reduce computational cost via a decomposition of the specification. We show that the proposed method can effectively solve real-world control problems such as jet engine compressor control and motion planning for manipulators and mobile robots.
On the Dynamism of User Rejections in Mobility-on-Demand Systems
Authors: Florian Dandl, Roman Engelhardt, Klaus Bogenberger
Date: 2021-04-03 00:00:00

Mobility-on-demand (MoD) systems, especially ride-hailing systems, have seen tremendous growth in recent years. These systems provide user-centric mobility services, whose users expect a high level of convenience. Waiting for a response after an app request and eventually learning after a long period of time that no vehicle is available is hardly acceptable. This study investigates the use-case where users should be served within a certain maximum waiting time. Under certain assumptions, which are reasonable for an attractive MoD business model, it can be shown that an operator using dynamic state optimization can communicate a rejection to users after the first iteration, thereby eliminating unnecessary waiting time before these users would leave the system. Furthermore, early operator rejections reduce the dimension of subsequent customer-vehicle assignment problems, thereby saving computational resources and solving the problems faster. In turn, this allows shorter re-optimization cycles and once again faster responses, i.e. a better user experience.
A GPU Implementation of a Look-Ahead Optimal Controller for Eco-Driving Based on Dynamic Programming
Authors: Zhaoxuan Zhu, Shobhit Gupta, Nicola Pivaro, Shreshta Rajakumar Deshpande, Marcello Canova
Date: 2021-04-03 00:00:00
Tasks: energy management, GPU, Management

Predictive energy management of Connected and Automated Vehicles (CAVs), in particular those with multiple power sources, has the potential to significantly improve energy savings in real-world driving conditions. In particular, the eco-driving problem seeks to design optimal speed and power usage profiles based upon available information from connectivity and advanced mapping features to minimize the fuel consumption between two designated locations. In this work, the eco-driving problem is formulated as a three-state receding horizon optimal control problem and solved via Dynamic Programming (DP). The optimal solution, in terms of vehicle speed and battery State of Charge (SoC) trajectories, allows a connected and automated hybrid electric vehicle to intelligently pass the signalized intersections and minimize fuel consumption over a prescribed route. To enable real-time implementation, a parallel architecture of DP is proposed for an NVIDIA GPU with CUDA programming. Simulation results indicate that the proposed optimal controller delivers more than 15% fuel economy benefits compared to a baseline control strategy and that the solver time can be reduced by more than 90% by the parallel implementation when compared to a serial implementation.
Geometric Brownian Motion under Stochastic Resetting: A Stationary yet Non-ergodic Process
Authors: Viktor Stojkoski, Trifce Sandev, Ljupco Kocarev, Arnab Pal
Date: 2021-04-04 00:00:00

We study the effects of stochastic resetting on geometric Brownian motion (GBM), a canonical stochastic multiplicative process for non-stationary and non-ergodic dynamics. Resetting is a sudden interruption of a process, which consecutively renews its dynamics. We show that, although resetting renders GBM stationary, the resulting process remains non-ergodic. Quite surprisingly, the effect of resetting is pivotal in manifesting the non-ergodic behavior. In particular, we observe three different long-time regimes: a quenched state, an unstable and a stable annealed state depending on the resetting strength. Notably, in the last regime, the system is self-averaging and thus the sample average will always mimic ergodic behavior establishing a stand alone feature for GBM under resetting. Crucially, the above-mentioned regimes are well separated by a self-averaging time period which can be minimized by an optimal resetting rate. Our results can be useful to interpret data emanating from stock market collapse or reconstitution of investment portfolios.
Effective Resource-Competition Model for Species Coexistence
Authors: Deepak Gupta, Stefano Garlaschi, Samir Suweis, Sandro Azaele, Amos Maritan
Date: 2021-04-02 00:00:00
Tasks: model

Local coexistence of species in large ecosystems is traditionally explained within the broad framework of niche theory. However, its rationale hardly justifies rich biodiversity observed in nearly homogeneous environments. Here we consider a consumer-resource model in which a coarse-graining procedure accounts for a variety of ecological mechanisms and leads to effective spatial effects which favour species coexistence. Herein, we provide conditions for several species to live in an environment with very few resources. In fact, the model displays two different phases depending on whether the number of surviving species is larger or smaller than the number of resources. We obtain conditions whereby a species can successfully colonize a pool of coexisting species. Finally, we analytically compute the distribution of the population sizes of coexisting species. Numerical simulations as well as empirical distributions of population sizes support our analytical findings.
Qubit Routing using Graph Neural Network aided Monte Carlo Tree Search
Authors: Animesh Sinha, Utkarsh Azad, Harjinder Singh
Date: 2021-04-01 00:00:00
Tasks: Graph Neural Network

Near-term quantum hardware can support two-qubit operations only on the qubits that can interact with each other. Therefore, to execute an arbitrary quantum circuit on the hardware, compilers have to first perform the task of qubit routing, i.e., to transform the quantum circuit either by inserting additional SWAP gates or by reversing existing CNOT gates to satisfy the connectivity constraints of the target topology. We propose a procedure for qubit routing that is architecture agnostic and that outperforms other available routing implementations on various circuit benchmarks. The depth of the transformed quantum circuits is minimised by utilizing the Monte Carlo tree search to perform qubit routing, aided by a Graph neural network that evaluates the value function and action probabilities for each state.
Identification and Estimation in Many-to-one Two-sided Matching without Transfers
Authors: YingHua He, Shruti Sinha, Xiaoting Sun
Date: 2021-04-05 00:00:00
Tasks: counterfactual

In a setting of many-to-one two-sided matching with non-transferable utilities, e.g., college admissions, we study conditions under which preferences of both sides are identified with data on one single market. Regardless of whether the market is centralized or decentralized, assuming that the observed matching is stable, we show nonparametric identification of preferences of both sides under certain exclusion restrictions. To take our results to the data, we use Monte Carlo simulations to evaluate different estimators, including the ones that are directly constructed from the identification. We find that a parametric Bayesian approach with a Gibbs sampler works well in realistically sized problems. Finally, we illustrate our methodology in decentralized admissions to public and private schools in Chile and conduct a counterfactual analysis of an affirmative action policy.
Economics in Nouns and Verbs
Authors: W. Brian Arthur
Date: 2021-04-05 00:00:00

Standard economic theory uses mathematics as its main means of understanding, and this brings clarity of reasoning and logical power. But there is a drawback: algebraic mathematics restricts economic modeling to what can be expressed only in quantitative nouns, and this forces theory to leave out matters to do with process, formation, adjustment, creation and nonequilibrium. For these we need a different means of understanding, one that allows verbs as well as nouns. Algorithmic expression is such a means. It allows verbs (processes) as well as nouns (objects and quantities). It allows fuller description in economics, and can include heterogeneity of agents, actions as well as objects, and realistic models of behavior in ill-defined situations. The world that algorithms reveal is action-based as well as object-based, organic, possibly ever-changing, and not fully knowable. But it is strangely and wonderfully alive.
Spatial parking planning design with mixed conventional and autonomous vehicles
Authors: Qida Su, David Z. W. Wang
Date: 2021-04-05 00:00:00
Tasks: Autonomous Vehicles

Travellers in autonomous vehicles (AVs) need not to walk to the destination any more after parking like those in conventional human-driven vehicles (HVs). Instead, they can drop off directly at the destination and AVs can cruise for parking autonomously. It is a revolutionary change that such parking autonomy of AVs may increase the potential parking span substantially and affect the spatial parking equilibrium. Given this, from urban planners' perspective, it is of great necessity to reconsider the planning of parking supply along the city. To this end, this paper is the first to examine the spatial parking equilibrium considering the mix of AVs and HVs with parking cruising effect. It is found that the equilibrium solution of travellers' parking location choices can be biased due to the ignorance of cruising effects. On top of that, the optimal parking span of AVs at given parking supply should be no less than that at equilibrium. Besides, the optimal parking planning to minimize the total parking cost is also explored in a bi-level parking planning design problem (PPDP). While the optimal differentiated pricing allows the system to achieve optimal parking distribution, this study suggests that it is beneficial to encourage AVs to cruise further to park by reserving less than enough parking areas for AVs.
Optimal parking provision in multi-modal morning commute problem considering ride-sourcing service
Authors: Qida Su
Date: 2021-04-05 00:00:00
Tasks: Management

Managing morning commute traffic through parking provision management has been well studied in the literature. However, most previous studies made the assumption that all road users require parking spaces at CBD area. However, in recent years, due to technological advancements and low market entry barrier, more and more e-dispatch FHVs (eFHVs) are provided in service. The rapidly growing eFHVs, on one hand, supply substantial trip services and complete the trips requiring no parking demand; on the other hand, imposes congestion effects to all road users. In this study, we investigate the multi-modal morning commute problem with bottleneck congestion and parking space constraints in the presence of ride-sourcing and transit service. Meanwhile, we derive the optimal number of parking spaces to best manage the commute traffic. One interesting finding is that, in the presence of ride-sourcing, excessive supply of parking spaces could incur higher system commute costs in the multi-modal case.