pwc-archive/papers-with-abstracts huggingface dataset:
An Investigation on Non-Invasive Brain-Computer Interfaces: Emotiv Epoc+ Neuroheadset and Its Effectiveness
Authors: Md Jobair Hossain Faruk, Maria Valero, Hossain Shahriar
Date: 2022-06-24 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

In this study, we illustrate the progress of BCI research and present scores of unveiled contemporary approaches. First, we explore a decoding natural speech approach that is designed to decode human speech directly from the human brain onto a digital screen introduced by Facebook Reality Lab and University of California San Francisco. Then, we study a recently presented visionary project to control the human brain using Brain-Machine Interfaces (BMI) approach. We also investigate well-known electroencephalography (EEG) based Emotiv Epoc+ Neuroheadset to identify six emotional parameters including engagement, excitement, focus, stress, relaxation, and interest using brain signals by experimenting the neuroheadset among three human subjects where we utilize two supervised learning classifiers, Naive Bayes and Linear Regression to show the accuracy and competency of the Epoc+ device and its associated applications in neurotechnological research. We present experimental studies and the demonstration indicates 69% and 62% improved accuracy for the aforementioned classifiers respectively in reading the performance matrices of the participants. We envision that non-invasive, insertable, and low-cost BCI approaches shall be the focal point for not only an alternative for patients with physical paralysis but also understanding the brain that would pave us to access and control the memories and brain somewhere very near.
Online Bayesian Meta-Learning for Cognitive Tracking Radar
Authors: Charles E. Thornton, R. Michael Buehrer, Anthony F. Martone
Date: 2022-07-07 00:00:00
Tasks: Meta-Learning

A key component of cognitive radar is the ability to generalize, or achieve consistent performance across a range of sensing environments, since aspects of the physical scene may vary over time. This presents a challenge for learning-based waveform selection approaches, since transmission policies which are effective in one scene may be highly suboptimal in another. We address this problem by strategically biasing a learning algorithm by exploiting high-level structure across tracking instances, referred to as meta-learning. In this work, we develop an online meta-learning approach for waveform-agile tracking. This approach uses information gained from previous target tracks to speed up and enhance learning in new tracking instances. This results in sample-efficient learning across a class of finite state target channels by exploiting inherent similarity across tracking scenes, attributed to common physical elements such as target type or clutter statistics. We formulate the online waveform selection problem within the framework of Bayesian learning, and provide prior-dependent performance bounds for the meta-learning problem using Probability Approximately Correct (PAC)-Bayes theory. We present a computationally feasible meta-posterior sampling algorithm and study the performance in a simulation study consisting of diverse scenes. Finally, we examine the potential performance benefits and practical challenges associated with online meta-learning for waveform-agile tracking.
Robot Swarms as Hybrid Systems: Modelling and Verification
Authors: Stefan Schupp, Francesco Leofante, Leander Behr, Erika Ábrahám, Armando Taccella
Date: 2022-07-14 00:00:00

A swarm robotic system consists of a team of robots performing cooperative tasks without any centralized coordination. In principle, swarms enable flexible and scalable solutions; however, designing individual control algorithms that can guarantee a required global behavior is difficult. Formal methods have been suggested by several researchers as a mean to increase confidence in the behavior of the swarm. In this work, we propose to model swarms as hybrid systems and use reachability analysis to verify their properties. We discuss challenges and report on the experience gained from applying hybrid formalisms to the verification of a swarm robotic system.
Toward cm-Level Accuracy: Carrier Phase Positioning for IIoT in 5G-Advanced NR Networks
Authors: Abdurrahman Fouda, Ryan Keating, Hyun-Su Cha
Date: 2022-07-14 00:00:00

High-precision positioning accuracy is among the key features of the future fifth-generation (5G-advanced) cellular networks to enable a wide variety of commercial, critical, and consumer use cases. 5G new radio (NR) systems have relied on (1) cellular temporal/angular-based positioning methods to provide the indoor environments with a moderate positioning accuracy that is well below the positioning requirements of these future use cases and (2) highly precise satellite carrier phase/code-based positioning methods for the outdoor deployments that are limited by the availability of the satellite coverage. This paper defines the relevant standard mechanisms and algorithms to use the carrier phase cellular-based measurements as a potential solution to achieve a high-precision positioning estimation accuracy in 5G-advanced NR networks. The presented positioning technique is evaluated using high-fidelity system-level simulations for indoor factory (InF) deployment scenarios. The numerical results demonstrate that the presented technique can significantly improve the positioning accuracy compared with the state-of-the-art NR positioning methods. Our findings in this paper also show that the carrier phase method not only provides an indoor complement to the outdoor satellite positioning but also provides an outdoor alternative to the high-precision satellite methods.
Detecting People Interested in Non-Suicidal Self-Injury on Social Media
Authors: Zaihan Yang, Dmitry Zinoviev
Date: 2022-07-10 00:00:00
Tasks: Binary Classification

We propose a supervised learning approach to detect people interested in Non-Suicidal Self-Injury (NSSI). We treat the task as a binary classification problem, and build classifiers based upon features extracted from people self-declared interests. Experimental evaluation on a real-world dataset, the LiveJournal social blogging networking platform, demonstrates the effectiveness of our proposed model.
Inverse Resource Rational Based Stochastic Driver Behavior Model
Authors: Mehmet Ozkan, Yao Ma
Date: 2022-07-14 00:00:00
Tasks: model, Model Predictive Control

Human drivers have limited and time-varying cognitive resources when making decisions in real-world traffic scenarios, which often leads to unique and stochastic behaviors that can not be explained by perfect rationality assumption, a widely accepted premise in modeling driving behaviors that presume drivers rationally make decisions to maximize their own rewards under all circumstances. To explicitly address this disadvantage, this study presents a novel driver behavior model that aims to capture the resource rationality and stochasticity of the human driver's behaviors in realistic longitudinal driving scenarios. The resource rationality principle can provide a theoretic framework to better understand the human cognition processes by modeling human's internal cognitive mechanisms as utility maximization subject to cognitive resource limitations, which can be represented as finite and time-varying preview horizons in the context of driving. An inverse resource rational-based stochastic inverse reinforcement learning approach (IRR-SIRL) is proposed to learn a distribution of the planning horizon and cost function of the human driver with a given series of human demonstrations. A nonlinear model predictive control (NMPC) with a time-varying horizon approach is used to generate driver-specific trajectories by using the learned distributions of the planning horizon and the cost function of the driver. The simulation experiments are carried out using human demonstrations gathered from the driver-in-the-loop driving simulator. The results reveal that the proposed inverse resource rational-based stochastic driver model can address the resource rationality and stochasticity of human driving behaviors in a variety of realistic longitudinal driving scenarios.
An Efficient Method for Quantifying the Aggregate Flexibility of Plug-in Electric Vehicle Populations
Authors: Feras Al Taha, Tyrone Vincent, Eilyan Bitar
Date: 2022-07-14 00:00:00

Plug-in electric vehicles (EVs) are widely recognized as being highly flexible electric loads that can be pooled and controlled via aggregators to provide low-cost energy and ancillary services to wholesale electricity markets. To participate in these markets, an aggregator must encode the aggregate flexibility of the population of EVs under their command as a single polytope that is compliant with existing market rules. To this end, we investigate the problem of characterizing the aggregate flexibility set of a heterogeneous population of EVs whose individual flexibility sets are given as convex polytopes in half-space representation. As the exact computation of the aggregate flexibility set -- the Minkowski sum of the individual flexibility sets -- is known to be intractable, we study the problem of computing maximum-volume inner approximations to the aggregate flexibility set by optimizing over affine transformations of a given convex polytope in half-space representation. We show how to conservatively approximate these set containment problems as linear programs that scale polynomially with the number and dimension of the individual flexibility sets. The inner approximation methods provided in this paper generalize and improve upon existing methods from the literature. We illustrate the improvement in approximation accuracy and performance achievable by our methods with numerical experiments.
Multi-Agent Deep Reinforcement Learning-Driven Mitigation of Adverse Effects of Cyber-Attacks on Electric Vehicle Charging Station
Authors: M. Basnet, MH Ali
Date: 2022-07-14 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

An electric vehicle charging station (EVCS) infrastructure is the backbone of transportation electrification. However, the EVCS has myriads of exploitable vulnerabilities in software, hardware, supply chain, and incumbent legacy technologies such as network, communication, and control. These standalone or networked EVCS open up large attack surfaces for the local or state-funded adversaries. The state-of-the-art approaches are not agile and intelligent enough to defend against and mitigate advanced persistent threats (APT). We propose the data-driven model-free distributed intelligence based on multiagent Deep Reinforcement Learning (MADRL)-- Twin Delayed Deep Deterministic Policy Gradient (TD3) -- that efficiently learns the control policy to mitigate the cyberattacks on the controllers of EVCS. Also, we have proposed two additional mitigation methods: the manual/Bruteforce mitigation and the controller clone-based mitigation. The attack model considers the APT designed to malfunction the duty cycles of the EVCS controllers with Type-I low-frequency attack and Type-II constant attack. The proposed model restores the EVCS operation under threat incidence in any/all controllers by correcting the control signals generated by the legacy controllers. Also, the TD3 algorithm provides higher granularity by learning nonlinear control policies as compared to the other two mitigation methods. Index Terms: Cyberattack, Deep Reinforcement Learning(DRL), Electric Vehicle Charging Station, Mitigation.
Precision Attitude Stabilization with Intermittent External Torque
Authors: S. P. Arjun Ram, Maruthi R Akella
Date: 2022-07-14 00:00:00

The attitude stabilization of a micro-satellite employing a variable-amplitude cold gas thruster which reflects as a time varying gain on the control input is considered. Existing literature uses a persistence filter based approach that typically leads to large control gains and torque inputs during specific time intervals corresponding to the 'on' phase of the external actuation. This work aims at reducing the transient spikes placed upon the torque commands by the judicious introduction of an additional time varying scaling signal as part of the control law. The time update mechanism for the new scaling factor and overall closed-loop stability are established through a Lyapunov-like analysis. Numerical simulations highlight the various features of this new control algorithm for spacecraft attitude stabilization subject to torque intermittence.
Adversarial Examples for Model-Based Control: A Sensitivity Analysis
Authors: Po-han Li, Ufuk Topcu, Sandeep P. Chinchali
Date: 2022-07-14 00:00:00
Tasks: Adversarial Attack, Sensitivity

We propose a method to attack controllers that rely on external timeseries forecasts as task parameters. An adversary can manipulate the costs, states, and actions of the controllers by forging the timeseries, in this case perturbing the real timeseries. Since the controllers often encode safety requirements or energy limits in their costs and constraints, we refer to such manipulation as an adversarial attack. We show that different attacks on model-based controllers can increase control costs, activate constraints, or even make the control optimization problem infeasible. We use the linear quadratic regulator and convex model predictive controllers as examples of how adversarial attacks succeed and demonstrate the impact of adversarial attacks on a battery storage control task for power grid operators. As a result, our method increases control cost by $8500\%$ and energy constraints by $13\%$ on real electricity demand timeseries.
Power Injection Measurements are more Vulnerable to Data Integrity Attacks than Power Flow Measurements
Authors: Xiuzhen Ye, Iñaki Esnaola, Samir M. Perlaza, Robert F. Harrison
Date: 2022-07-14 00:00:00

A novel metric that describes the vulnerability of the measurements in power system to data integrity attacks is proposed. The new metric, coined vulnerability index (VuIx), leverages information theoretic measures to assess the attack effect on the fundamental limits of the disruption and detection tradeoff. The result of computing the VuIx of the measurements in the system yields an ordering of the measurements vulnerability based on the level of exposure to data integrity attacks. This new framework is used to assess the measurements vulnerability of IEEE test systems and it is observed that power injection measurements are overwhelmingly more vulnerable to data integrity attacks than power flow measurements. A detailed numerical evaluation of the VuIx values for IEEE test systems is provided.
Approximate synchronization of coupled multi-valued logical networks
Authors: Rong Zhao, Jun-e Feng, Biao Wang
Date: 2022-07-14 00:00:00

This article deals with the approximate synchronization of two coupled multi-valued logical networks. According to the initial state set from which both systems start, two kinds of approximate synchronization problem, local approximate synchronization and global approximate synchronization, are proposed for the first time. Three new notions: approximate synchronization state set (ASSS), the maximum approximate synchronization basin (MASB) and the shortest approximate synchronization time (SAST) are introduced and analyzed. Based on ASSS, several necessary and sufficient conditions are obtained for approximate synchronization. MASB, the set of all possible initial states, from which the systems are approximately synchronous, is investigated combining with the maximum invariant subset. And the calculation method of the SAST, associated with transient period, is presented. By virtue of MASB, pinning control scheme is investigated to make two coupled systems achieve global approximate synchronization. Furthermore, the related theories are also applied to the complete synchronization problem of $k$-valued ($k\geq2$) logical networks. Finally, four examples are given to illustrate the obtained results.
Dynamic State Estimation for Load Bus Protection on Inverter-Interfaced Microgrids
Authors: Arthur K. Barnes, Adam Mate, Jean Marie V. Bikorimana, Ricardo J. Castillo
Date: 2022-07-13 00:00:00
Tasks: State Estimation

Inverter-interfaced microgrids results in challenges when designing protection systems. Traditional time-overcurrent, admittance, and differential protection methods are unsuitable on account of lack of fault current, excessively short lines, or a prohibitive number of protective devices needing to be installed. Current practice is to force all inverters to shut down during fault conditions, weakening resilience and reducing reliability. Dynamic state estimation (DSE), which has been explored for both line protection and load bus protection before, is a potential solution to these challenges to create widely utilizable, highly reliable protection systems. However, it has only been tested for load protection with ideal voltage sources, which do not capture the short-circuit behavior of inverter-interfaced generation, notably low fault current and unbalanced output voltage. This paper aims to extend the state-of-the-art on DSE load protection: the performance of DSE during short-circuit conditions with a grid-forming inverter with current-limiting behavior during fault conditions is investigated.
Construction of Lyapunov Functions Using Vector Field Decomposition
Authors: Yuanyuan Liu
Date: 2022-07-13 00:00:00

In the present paper, a novel vector field decomposition based approach for constructing Lyapunov functions is proposed. For a given dynamical system, if the defining vector field admits a decomposition into two mutually orthogonal vector fields, one of which is curl-free and the other is divergence-free, then the potential function of the former can serve as a Lyapunov function candidate, since its positive definiteness will reflect the stability of the system. Moreover, under some additional conditions, its sublevel sets will give the exact attraction domain of the system. A sufficient condition for the existence of the proposed vector field decomposition is first obtained for $2$-dimensional systems by solving a partial differential equation and then generalized to $n$-dimensional systems. Furthermore, the proposed vector field decomposition always exists for linear systems and can be obtained by solving a specific algebraic Riccati equation.
Optimization of rule-based energy management strategies for hybrid vehicles using dynamic programming
Authors: Di Zhu, Ewan Pritchard, Sumanth Reddy Dadam, Vivek Kumar, Yang Xu
Date: 2022-07-08 00:00:00
Tasks: energy management, Management

Reducing energy consumption is a key focus for hybrid electric vehicle (HEV) development. The popular vehicle dynamic model used in many energy management optimization studies does not capture the vehicle dynamics that the in-vehicle measurement system does. However, feedback from the measurement system is what the vehicle controller actually uses to manage energy consumption. Therefore, the optimization solely using the model does not represent what the vehicle controller sees in the vehicle. This paper reports the utility factor-weighted energy consumption using a rule-based strategy under a real-world representative drive cycle. In addition, the vehicle test data was used to perform the optimization approach. By comparing results from both rule-based and optimization-based strategies, the areas for further improving rule-based strategy are discussed. Furthermore, recent development of OBD raises a concern about the increase of energy consumption. This paper investigates the energy consumption increase with extensive OBD usage.
Changepoint Detection for Real-Time Spectrum Sharing Radar
Authors: Samuel Haug, Austin Egbert, Robert J. Marks II, Charles Baylis, Anthony Martone
Date: 2022-06-30 00:00:00

Radar must adapt to changing environments, and we propose changepoint detection as a method to do so. In the world of increasingly congested radio frequencies, radars must adapt to avoid interference. Many radar systems employ the prediction action cycle to proactively determine transmission mode while spectrum sharing. This method constructs and implements a model of the environment to predict unused frequencies, and then transmits in this predicted availability. For these selection strategies, performance is directly reliant on the quality of the underlying environmental models. In order to keep up with a changing environment, these models can employ changepoint detection. Changepoint detection is the identification of sudden changes, or changepoints, in the distribution from which data is drawn. This information allows the models to discard "garbage" data from a previous distribution, which has no relation to the current state of the environment. In this work, bayesian online changepoint detection (BOCD) is applied to the sense and predict algorithm to increase the accuracy of its models and improve its performance. In the context of spectrum sharing, these changepoints represent interferers leaving and entering the spectral environment. The addition of changepoint detection allows for dynamic and robust spectrum sharing even as interference patterns change dramatically. BOCD is especially advantageous because it enables online changepoint detection, allowing models to be updated continuously as data are collected. This strategy can also be applied to many other predictive algorithms that create models in a changing environment.
Solving Bilevel AC OPF Problems by Smoothing the Complementary Conditions -- Part I: Model Description and the Algorithm
Authors: Karlo Šepetanc, Hrvoje Pandžić, Tomislav Capuder
Date: 2022-06-28 00:00:00

The existing research on market price-affecting agents, i.e. price makers, neglects or simplifies the nature of AC power flows in the power system as it predominantly relies on DC power flows. This paper proposes a novel bilevel formulation based on the smoothing technique, where any price-affecting strategic player can be modelled in the upper level, while the market clearing problem in the lower level uses convex quadratic transmission AC optimal power flow (AC OPF), with the goal of achieving accuracy close to the one of the exact nonlinear formulations. Achieving convexity in the lower level is the foundation for bilevel modeling since traditional single-level reduction techniques do not hold for nonconvex models. The bilevel market participation problem with the AC OPF formulation in the lower level is transformed into a single-level problem and solved using multiple techniques such as the primal-dual counterpart, the strong duality theorem, the McCormick envelopes, the complementary slackness, the penalty factor, the interaction discretization as well as the proposed smoothing techniques. Due to an extensive amount of information and descriptions, the overall work is presented as a two-part paper. This first part provides a literature overview, positions the work and presents the model and the solution algorithm, while the solution techniques and case studies are provided in the accompanying paper.
AutoML-Based Drought Forecast with Meteorological Variables
Authors: Shiheng Duan, Xiurui Zhang
Date: 2022-06-09 00:00:00
Tasks: AutoML, Deep Learning, Management

A precise forecast for droughts is of considerable value to scientific research, agriculture, and water resource management. With emerging developments of data-driven approaches for hydro-climate modeling, this paper investigates an AutoML-based framework to forecast droughts in the U.S. Compared with commonly-used temporal deep learning models, the AutoML model can achieve comparable performance with less training data and time. As deep learning models are becoming popular for Earth system modeling, this paper aims to bring more efforts to AutoML-based methods, and the use of them as benchmark baselines for more complex deep learning models.