pwc-archive/papers-with-abstracts huggingface dataset:
MAP-CSI: Single-site Map-Assisted Localization Using Massive MIMO CSI
Authors: Katarina Vuckovic, Farzam Hejazi, Nazanin Rahnavard
Date: 2021-10-01 00:00:00

This paper presents a new map-assisted localization approach utilizing Chanel State Information (CSI) in Massive Multiple-Input Multiple-Output (MIMO) systems. Map-assisted localization is an environment-aware approach in which the communication system has information regarding the surrounding environment. By combining radio frequency ray tracing parameters of the multipath components (MPC) with the environment map, it is possible to accomplish localization. Unfortunately, in real-world scenarios, ray tracing parameters are typically not explicitly available. Thus, additional complexity is added at a base station to obtain this information. On the other hand, CSI is a common communication parameter, usually estimated for any communication channel. In this work, we leverage the already available CSI data to propose a novel map-assisted CSI localization approach, referred to as MAP-CSI. We show that Angle-of-Departure (AoD) and Time-of-Arrival (ToA) can be extracted from CSI and then be used in combination with the environment map to localize the user. We perform simulations on a public MIMO dataset and show that our method works for both line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios. We compare our method to the state-of-the-art (SoA) method that uses the ray tracing data. Using MAP-CSI, we accomplish an average localization error of 1.8 m in LOS and 2.8 m in mixed (combination of LOS and NLOS samples) scenarios. On the other hand, SoA ray tracing has an average error of 1.0 m and 2.2 m, respectively, but requires explicit AoD and ToA information to perform the localization task.
Wideband Signal Localization with Spectral Segmentation
Authors: Nathan West, Tamoghna Roy, Timothy O'Shea
Date: 2021-10-01 00:00:00
Tasks: Segmentation

Signal localization is a spectrum sensing problem that jointly detects the presence of a signal and estimates a center frequency and bandwidth. This is a step beyond most spectrum sensing work which estimates "present" or "not present" detections for either a single channel or fixed sized channels. We define the signal localization task, present the metrics of precision and recall, and establish baselines for traditional energy detection on this task. We introduce a new dataset that is useful for training neural networks to perform this task and show a training framework to train signal detectors to achieve the task and present precision and recall curves over SNR. This neural network based approach shows an 8 dB improvement in recall over the traditional energy detection approach with minor improvements in precision.
Passivity-Based Decentralized Criteria for Small-Signal Stability of Power Systems with Converter-Interfaced Generation
Authors: Kaustav Dey, Anil Kulkarni
Date: 2021-10-04 00:00:00

With the increasing penetration of converter-interfaced distributed generation systems, it would be advantageous to specify local compliance criteria for these devices to ensure the small-signal stability of the interconnected system. Passivity of the device admittance, which is an example of a local criterion, has been used previously to avoid resonances between these devices and the lightly damped oscillatory modes of the network. Typical active and reactive power control strategies like droop control and virtual synchronous generator control inherently violate the passivity constraints on admittance at low frequencies, although this does not necessarily mean that the interconnected system will be unstable. Therefore, passivity of the admittance is unsuitable as a stability criterion for devices that are represented by their wide-band models. To overcome this problem, this paper proposes the use of criteria based on admittance at higher frequencies and an alternative transfer function at lower frequencies. The alternative representation uses active and reactive power and the derivatives of the polar components of voltage as interface variables. To allow for the separate analysis at low and high frequencies, the device dynamics should exhibit a slow-fast separation; this is proposed as an additional constraint. Adherence to the proposed criteria is not onerous and is easily verifiable through frequency response analysis.
A Game Theoretic Approach for Demand Response Allocation among Strategic Prosumers in Regulated Distribution Utilities
Authors: Sayyid Mohssen Sajjadi, Meng Wu
Date: 2021-10-04 00:00:00

This paper studies an optimal allocation of demand response (DR) provisions among strategic photovoltaic (PV) prosumers, through third-party DR providers which operate within the territory of the regulated distribution utility. A game theoretic model, consisting of a sequential game coupled with a simultaneous game, is proposed to capture the competition and interaction among PV prosumers, the third-party DR providers, and the utility company. An iterative approach is proposed to solve for the Nash equilibrium of the game. With this game theoretic model, the DR provision quantities for strategic PV prosumers are optimally determined, considering the profit maximizations of the PV prosumers, the DR providers, and the utility company. Case studies on an IEEE test system verifies the proposed model and the solution approach.
From Control to Mathematics-Part II: Observability-Based Design for Iterative Methods in Solving Linear Equations
Authors: Deyuan Meng
Date: 2021-10-04 00:00:00

The control approaches generally resort to the tools from the mathematics, but whether and how the mathematics can benefit from the control approaches is unclear. This paper aims to bring the "control design" idea into the mathematics by providing an observer-based iterative method that focuses on solving linear algebraic equations (LAEs). An inherent relationship is revealed between the problem-solving of LAEs and the design of observer-based control systems, with which the iterative method for solving LAEs is exploited based on the design of the basic state observers. It is shown that all (least squares) solutions for any (un)solvable LAEs can be determined exponentially fast or monotonically with different selections of initial conditions. By integrating the design idea of the deadbeat control, the solving of LAEs can be achieved within only finite iterations. In particular, our proposed iterative method can be leveraged to develop a new observer-based design algorithm to realize the perfect tracking objective of conventional two-dimensional iterative learning control (ILC) systems, where the gap between classical ILC design and popular feedback-based control design is narrowed.
Decentralized Safe Reinforcement Learning for Voltage Control
Authors: Wenqi Cui, Jiayi Li, Baosen Zhang
Date: 2021-10-03 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Safe Reinforcement Learning

Inverter-based distributed energy resources provide the possibility for fast time-scale voltage control by quickly adjusting their reactive power. The power-electronic interfaces allow these resources to realize almost arbitrary control law, but designing these decentralized controllers is nontrivial. Reinforcement learning (RL) approaches are becoming increasingly popular to search for policy parameterized by neural networks. It is difficult, however, to enforce that the learned controllers are safe, in the sense that they may introduce instabilities into the system. This paper proposes a safe learning approach for voltage control. We prove that the system is guaranteed to be exponentially stable if each controller satisfies certain Lipschitz constraints. The set of Lipschitz bound is optimized to enlarge the search space for neural network controllers. We explicitly engineer the structure of neural network controllers such that they satisfy the Lipschitz constraints by design. A decentralized RL framework is constructed to train local neural network controller at each bus in a model-free setting.
Quadrotor Control on $SU(2)\times R^3$ with SLAM Integration
Authors: Marcus Greiff, Patrik Persson, Zhiyong Sun, Karl Åström, Anders Robertsson
Date: 2021-10-03 00:00:00
Tasks: Simultaneous Localization and Mapping

We present a trajectory tracking controller for a quadrotor unmanned aerial vehicle (UAV) configured on $SU(2)\times R^3$, and relate this result to a family of geometric tracking controllers on $SO(3)\times R^3$. The theoretical results are complemented by simulation examples, and the controller is subsequently implemented in practice and integrated with a simultaneous localization and mapping (SLAM) system through an extended Kalman filter (EKF). This facilitates the operation of the UAV without external motion capture systems, and we demonstrate that the proposed control system can be used for inventorying tasks in a supermarket environment without external positioning systems.
Assessing Distribution Network Flexibility via Reliability-based P-Q Area Segmentation
Authors: Andrey Churkin, Wangwei Kong, Jose N. Melchor Gutierrez, Pierluigi Mancarella, Eduardo A. Martinez Cesena
Date: 2021-10-03 00:00:00
Tasks: Combinatorial Optimization

This paper proposes a framework to assess the flexibility of active distribution networks (ADNs) via P-Q area segmentation, considering the reliability of flexible units (FUs). A mixed-integer quadratically constrained programming (MIQCP) model is formulated to analyse flexible active and reactive power support at the interface with transmission networks, explicitly capturing the contributions and reliability of FUs that provide flexibility services within an ADN. The numerical simulations performed for a real 124-bus UK distribution network demonstrate the optimal flexibility provision by different FUs, as well as the corresponding reliability and the impact of network reconfiguration. Distribution system operators (DSOs) can use the proposed framework to identify critical units, select an adequate combination of flexibility volumes, and manage its reliability.
Adaptive Real-Time Grid Operation via Online Feedback Optimization with Sensitivity Estimation
Authors: Miguel Picallo, Lukas Ortmann, Saverio Bolognani, Florian Dörfler
Date: 2021-10-03 00:00:00
Tasks: Sensitivity

In this paper we propose an approach based on an Online Feedback Optimization (OFO) controller with grid input-output sensitivity estimation for real-time grid operation, e.g., at subsecond time scales. The OFO controller uses grid measurements as feedback to update the value of the controllable elements in the grid, and track the solution of a time-varying AC Optimal Power Flow (AC-OPF). Instead of relying on a full grid model, e.g., grid admittance matrix, OFO only requires the steady-state sensitivity relating a change in the controllable inputs, e.g., power injections set-points, to a change in the measured outputs, e.g., voltage magnitudes. Since an inaccurate sensitivity may lead to a model-mismatch and jeopardize the performance, we propose a recursive least-squares estimation that enables OFO to learn the sensitivity from measurements during real-time operation, turning OFO into a model-free approach. We analytically certify the convergence of the proposed OFO with sensitivity estimation, and validate its performance on a simulation using the IEEE 123-bus test feeder, and comparing it against a state-of-the-art OFO with constant sensitivity.
Application of Artificial Neural Networks for Catalysis
Authors: Zhiqiang Liu, Wentao Zhou
Date: 2021-10-03 00:00:00
Tasks: Self-Learning

Catalyst, as an important material, plays a crucial role in the development of chemical industry. By improving the performance of the catalyst, the economic benefit can be greatly improved. Artificial neural network (ANN), as one of the most popular machine learning algorithms, relies on its good ability of nonlinear transformation, parallel processing, self-learning, self-adaptation and good associative memory, has been widely applied to various areas. Through the optimization of catalyst by ANN, the consumption of time and resources can be greatly reduced and greater economic benefits can be obtained. In this review, we show how this powerful technique helps people address the highly complicated problems and accelerate the progress of the catalysis community.
Dynamic-Programming-Based Failure-Tolerant Control for Satellite with Thrusters in 6-DOF Motion
Authors: Abdolreza Taheri, Nima Assadian
Date: 2021-10-02 00:00:00

In this paper, a dynamic-programming approach to the coupled translational and rotational control of thruster-driven spacecraft is studied. To reduce the complexity of the problem, dynamic-programming-based optimal policies are calculated using decoupled position and attitude dynamics with generalized forces and torques as controls. A quadratic-programming-based control allocation is then used to map the controls to actuator commands. To control the spacecraft in the event of thruster failure, both the dynamic programming policies and control allocation are reconfigured to cope with the losses in controls. The control allocation parameters are adjusted dynamically to ensure the satellite always approaches the target from the side with two operative thrusters to achieve a stable control. The effectiveness of the proposed dynamic programming control is compared with a Lyapunov-stable control method, which shows that the proposed method is more fuel-efficient in tracking the same path.
Learning Region of Attraction for Nonlinear Systems
Authors: Shaoru Chen, Mahyar Fazlyab, Manfred Morari, George J. Pappas, Victor M. Preciado
Date: 2021-10-02 00:00:00

Estimating the region of attraction (ROA) of general nonlinear autonomous systems remains a challenging problem and requires a case-by-case analysis. Leveraging the universal approximation property of neural networks, in this paper, we propose a counterexample-guided method to estimate the ROA of general nonlinear dynamical systems provided that they can be approximated by piecewise linear neural networks and that the approximation error can be bounded. Specifically, our method searches for robust Lyapunov functions using counterexamples, i.e., the states at which the Lyapunov conditions fail. We generate the counterexamples using Mixed-Integer Quadratic Programming. Our method is guaranteed to find a robust Lyapunov function in the parameterized function class, if exists, after collecting a finite number of counterexamples. We illustrate our method through numerical examples.
Joint Estimation of System Inertia and Load Relief
Authors: Julius Susanto, Alireza Fereidouni, Dean Sharafi
Date: 2021-10-02 00:00:00

Modern power systems with high share of renewable generation are at the risk of rapid changes in frequency and inertia resulting from contingencies. The importance of an accurate assessment of system and load relief, as well as frequency changes in the critical timescales is more pronounced in these power systems. This knowledge serves as an insight which will guide the solutions required for enhanced security and resilience. A novel procedure for simultaneously assessing system inertia and load relief using a closed-form system frequency response (SFR) model to fit measured disturbance data is presented. Results of applying the proposed approach on generator contingencies in the South West Interconnected System (SWIS) in Western Australia demonstrate the validity of the method and indicate that it can overcome some of the limitations observed in conventional inertia estimation methods based on the linearised swing equation, such as the sliding window and polynomial fit methods.
Online Distribution System State Estimation via Stochastic Gradient Algorithm
Authors: Jianqiao Huang, Xinyang Zhou, Bai Cui
Date: 2021-10-01 00:00:00
Tasks: State Estimation

Distribution network operation is becoming more challenging because of the growing integration of intermittent and volatile distributed energy resources (DERs). This motivates the development of new distribution system state estimation (DSSE) paradigms that can operate at fast timescale based on real-time data stream of asynchronous measurements enabled by modern information and communications technology. To solve the real-time DSSE with asynchronous measurements effectively and accurately, this paper formulates a weighted least squares DSSE problem and proposes an online stochastic gradient algorithm to solve it. The performance of the proposed scheme is analytically guaranteed and is numerically corroborated with realistic data on IEEE-123 bus feeder.
The Proportional Integral Notch and Coleman Blade Effective Wind Speed Estimators and Their Similarities
Authors: Yichao Liu, Atindriyo K. Pamososuryo, Sebastiaan P. Mulders, Riccardo M. G. Ferrari, Jan-Willem van Wingerden
Date: 2021-10-01 00:00:00

The estimation of the rotor effective wind speed is used in modern wind turbines to provide advanced power and load control capabilities. However, with the ever increasing rotor sizes, the wind field over the rotor surface shows a higher degree of spatial variation. A single effective wind speed estimation therefore limits the attainable levels of load mitigation, and the estimation of the Blade Effective Wind Speed (BEWS) might present opportunities for improved load control. This letter introduces two novel BEWS estimator approaches: A Proportional Integral Notch (PIN) estimator based on individual blade load measurements, and a Coleman estimator targeting the estimation in the non-rotating frame. Given the seeming disparities between these two estimators, the objective of this letter is to analyze the similarities between the approaches. It is shown that the PIN estimator, which is equivalent to the diagonal form of the Coleman estimator, is a simple but effective method to estimate the BEWS. The Coleman estimator, which takes the coupling effects between individual blades into account, shows a more well behaved transient response than the PIN estimator.
Subsystem decomposition and state estimation of nonlinear processes with implicit time-scale multiplicity
Authors: Sarupa Debnath, Soumya Ranjan Sahoo, Benjamin Decardi-Nelson, Jinfeng Liu
Date: 2021-10-01 00:00:00
Tasks: Chemical Process, State Estimation

In this work, we propose a subsystem decomposition approach and a distributed estimation scheme for a class of implicit two-time-scale nonlinear systems. Taking the advantage of the two-time-scale separation, these processes are decomposed into smaller subsystems such as fast subsystem and slow subsystem. In the proposed method, an approach, composite solution, combines the approximate solutions obtained from both fast and slow subsystems. Based on the fast and slow subsystems, a distributed state estimation scheme is proposed to handle the implicit time-scale multiplicity. In the proposed design, an extended Kalman filter (EKF) is designed for the fast subsystem and a moving horizon estimator (MHE) is designed for the slow subsystem. There is a communication between the estimators: the slow subsystem is only required to send information to the fast subsystem one-directionally. The fast subsystem estimator does not send out any information. The estimators use different sampling of the state measurements, i.e., fast sampling of the fast state variables is considered in the fast EKF and slow sampling of the slow state variables is considered in the slow MHE. Extensive simulations based on a chemical process are performed to illustrate the effectiveness and applicability of the proposed subsystem decomposition and composite state estimation architecture.
RecA and RecB: probing complexes of DNA repair proteins with mitomycin C in live Escherichia coli with single-molecule sensitivity
Authors: Alex L. Payne-Dwyer, Aisha H. Syeda, Jack W. Shepherd, Lewis Frame, Mark. C. Leake
Date: 2021-10-04 00:00:00

The RecA protein and RecBCD complex are key bacterial components for the maintenance and repair of DNA, RecBCD a helicase-nuclease that uses homologous recombination to resolve double-stranded DNA breaks and also facilitating decoration of single-stranded DNA with RecA to form RecA filaments, a vital step in the double-stranded break DNA repair pathway. However, questions remain about the mechanistic roles of RecA and RecBCD in live cells. Here, we use millisecond super-resolved fluorescence microscopy to pinpoint the spatial localization of fluorescent reporters of RecA and the RecB at physiological levels of expression in individual live Escherichia coli cells. By introducing the DNA crosslinker mitomycin C, we induce DNA damage and quantify the resulting changes in stoichiometry, copy number and molecular mobilities of RecA and RecB. We find that both proteins accumulate in molecular hotspots to effect repair, resulting in RecA filamental stoichiometries equivalent to several hundred molecules that act largely in RecA tetramers before DNA damage, but switch to approximately hexameric subunits when mature filaments are formed. Unexpectedly, we find that the physiologically predominant form of RecB is a dimer.
A new iterative algorithm for generating gradient directions to detect white matter fibers in brain from MRI data
Authors: Ashishi Puri, Sanjeev Kumar
Date: 2021-10-04 00:00:00
Tasks: Anatomy, Position

This paper proposes an iterative algorithm for choosing gradient directions use to reconstruct white matter fibers in the brain. The present study is not focusing on data acquisition where scanning is performed. The Adaptive Gradient Directions (AGD) approach is extended to refine the position and area of the grid, resulting in an admissible reduction in angular error. We begin with the gradient directions distributed uniformly inside a grid of bigger size and with larger spacing between the points. Both (size of the grid and spacing between the points) reduce iteratively. The proposed algorithm ensures that the actual position of fiber comes inside the grid at each iteration, unlike as in the AGD approach. As a result, the solution tends to actual orientation in each iteration followed by better estimation of fibers. The proposed algorithm is validated by associating it with mixture of Gaussian diffusion and mixture of non-central Wishart distribution models. The proposed approach significantly reduce the angular error for multiple computer-generated experiments on synthetic simulations and real data. Moreover, we have also performed simulations with fibers not residing in the XY-plane. For this set-up also, the proposed work outperforms, giving lesser angular error with both the models. Synthetic simulations have been performed with Rician distributed (R-D) noise of standard deviation ranging from 0.02-0.1. This work helps in better understanding of the anatomy of the brain using the MRI signal data.