pwc-archive/papers-with-abstracts huggingface dataset:
An Online Data-Driven Method to Locate Forced Oscillation Sources from Power Plants Based on Sparse Identification of Nonlinear Dynamics (SINDy)
Authors: Yaojie Cai, Xiaozhe Wang, Geza Joos, Innocent Kamwa
Date: 2022-07-12 00:00:00

Forced oscillations may jeopardize the secure operation of power systems. To mitigate forced oscillations, locating the sources is critical. In this paper, leveraging on Sparse Identification of Nonlinear Dynamics (SINDy), an online purely data-driven method to locate the forced oscillation is developed. Validations in all simulated cases (in the WECC 179-bus system) and actual oscillation events (in ISO New England system) in the IEEE Task Force test cases library are carried out, which demonstrate that the proposed algorithm, requiring no model information, can accurately locate sources in most cases, even under resonance condition and when the natural modes are poorly damped. The little tuning requirement and low computational cost make the proposed method viable for online application.
Distributed Control of Multi-agent Systems with Unknown Time-varying Gains: A Novel Indirect Framework for Prescribed Performance
Authors: Zeqiang Li, Yujuan Wang, Xiucai Huang
Date: 2022-07-12 00:00:00
Tasks: LEMMA

In this paper, a new yet indirect performance guaranteed framework is established to address the distributed tracking control problem for networked uncertain nonlinear strict-feedback systems with unknown time-varying gains under a directed interaction topology. The proposed framework involves two steps: In the first one, a fully distributed robust filter is constructed to estimate the desired trajectory for each agent with guaranteed observation performance that allows the directions among the agents to be non-identical. In the second one, by establishing a novel lemma regarding Nussbaum function, a new adaptive control protocol is developed for each agent based on backstepping technique, which not only steers the output to asymptotically track the corresponding estimated signal with arbitrarily prescribed transient performance, but also largely extends the scope of application since the unknown control gains are allowed to be time-varying and even state-dependent. In such an indirect way, the underlying problem is tackled with the output tracking error converging into an arbitrarily pre-assigned residual set exhibiting an arbitrarily pre-defined convergence rate. Besides, all the internal signals are ensured to be semi-globally ultimately uniformly bounded (SGUUB). Finally, simulation results are provided to illustrate the effectiveness of the co-designed scheme.
High-Order Leader-Follower Tracking Control under Limited Information Availability
Authors: Chuan Yan, Tao Yang, Huazhen Fang
Date: 2022-07-12 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Limited information availability represents a fundamental challenge for control of multi-agent systems, since an agent often lacks sensing capabilities to measure certain states of its own and can exchange data only with its neighbors. The challenge becomes even greater when agents are governed by high-order dynamics. The present work is motivated to conduct control design for linear and nonlinear high-order leader-follower multi-agent systems in a context where only the first state of an agent is measured. To address this open challenge, we develop novel distributed observers to enable followers to reconstruct unmeasured or unknown quantities about themselves and the leader and on such a basis, build observer-based tracking control approaches. We analyze the convergence properties of the proposed approaches and validate their performance through simulation.
Exploring the feasibility of electric vehicle travel for remote communities in Australia
Authors: Keigan Demaria, Björn C. P. Sturmberg, Brad Riley, Francis Markham
Date: 2022-07-12 00:00:00

Remote communities in Australia face unique mobility challenges that stand to be further complicated by the transition from Internal Combustion Engine (ICE) vehicles to Electric Vehicles (EVs). EVs offer a range of advantages that include lower maintenance requirements and independence from costly, dangerous and polluting petroleum imports that have long been burdensome for remote communities. Yet the adoption of electric vehicles in Australia has been slow by international standards, and what policy strategies do exist tend to focus on incentivizing uptake among urban residents with the means to afford new technologies, potentially leaving remote communities in the 'too hard basket'. In this study we assess the feasibility of EVs for a sample of communities in remote Australia using Geographic Information System analysis of travel distances between communities and service hub towns utilizing present-day EV specifications and charging technologies. We show that while EV travel is often not currently feasible for trips to large service hub towns using low-range vehicles, over 99% of communities and residents considered would be able to travel to their nearest small service hub town with existing long-range EVs. This finding suggests that while the barriers to the electrification of transport in remote communities are significant, they are not insurmountable and are deserving of consideration in national and state policy developments in the deployment of charging infrastructure.
Numerical Comparisons of Linear Power Flow Approximations: Optimality, Feasibility, and Computation Time
Authors: Meiyi Li, Yuhan Du, Javad Mohammadi, Constance Crozier, Kyri Baker, Soummya Kar
Date: 2022-07-11 00:00:00
Tasks: Computational Efficiency

Linear approximations of the AC power flow equations are of great significance for the computational efficiency of large-scale optimal power flow (OPF) problems. Put differently, the feasibility of the obtained solution is essential for practical use cases of OPF. However, most studies focus on approximation error and come short of comprehensively studying the AC feasibility of different linear approximations of power flow. This paper discusses the merits of widely-used linear approximations of active power in OPF problems. The advantages and disadvantages of the linearized models are discussed with respect to four criteria; accuracy of the linear approximation, optimality, feasibility, and computation time. Each method is tested on five different systems.
An Online Data-Driven Method for Microgrid Secondary Voltage and Frequency Control with Ensemble Koopman Modeling
Authors: Xun Gong, Xiaozhe Wang, Geza Joos
Date: 2022-07-11 00:00:00
Tasks: Event Detection

Low inertia, nonlinearity and a high level of uncertainty (varying topologies and operating conditions) pose challenges to microgrid (MG) systemwide operation. This paper proposes an online adaptive Koopman operator optimal control (AKOOC) method for MG secondary voltage and frequency control. Unlike typical data-driven methods that are data-hungry and lack guaranteed stability, the proposed AKOOC requires no warm-up training yet with guaranteed bounded-input-bounded-output (BIBO) stability and even asymptotical stability under some mild conditions. The proposed AKOOC is developed based on an ensemble Koopman state space modeling with full basis functions that combines both linear and nonlinear bases without the need of event detection or switching. An iterative learning method is also developed to exploit model parameters, ensuring the effectiveness and the adaptiveness of the designed control. Simulation studies in the 4-bus (with detailed inner-loop control) MG system and the 34-bus MG system showed improved modeling accuracy and control, verifying the effectiveness of the proposed method subject to various changes of operating conditions even with time delay, measurement noise, and missing measurements.
LQG Reference Tracking with Safety and Reachability Guarantees under Unknown False Data Injection Attacks
Authors: Zhouchi Li, Luyao Niu, Andrew Clark
Date: 2021-02-28 00:00:00

We investigate a linear quadratic Gaussian (LQG) tracking problem with safety and reachability constraints in the presence of an adversary who mounts an FDI attack on an unknown set of sensors. For each possible set of compromised sensors, we maintain a state estimator disregarding the sensors in that set, and calculate the optimal LQG control input at each time based on this estimate. We propose a control policy which constrains the control input to lie within a fixed distance of the optimal control input corresponding to each state estimate. The control input is obtained at each time step by solving a quadratically constrained quadratic program (QCQP). We prove that our policy can achieve a desired probability of safety and reachability using the barrier certificate method. Our control policy is evaluated via a numerical case study.
Safe Control for Nonlinear Systems under Faults and Attacks via Control Barrier Functions
Authors: Hongchao Zhang, Zhouchi Li, Andrew Clark
Date: 2022-07-11 00:00:00
Tasks: Philosophy

Safety is one of the most important properties of control systems. Sensor faults and attacks and actuator failures may cause errors in the sensor measurements and system dynamics, which leads to erroneous control inputs and hence safety violations. In this paper, we improve the robustness against sensor faults and actuator failures by proposing a class of Fault-Tolerant Control Barrier Functions (FT-CBFs) for nonlinear systems. Our approach maintains a set of state estimators according to fault patterns and incorporates CBF-based linear constraints for each state estimator. We then propose a framework for joint safety and stability by integrating FT-CBFs with Control Lyapunov Functions. With a similar philosophy of utilizing redundancy, we proposed High order CBF-based approach to ensure safety when actuator failures occur. We propose a sum-of-squares (SOS) based approach to verify the feasibility of FT-CBFs for both sensor faults and actuator failures. We evaluate our approach via two case studies, namely, a wheeled mobile robot (WMR) system in the presence of a sensor attack and a Boeing 747 lateral control system under actuator failures.
Field Evaluation of Four Low-cost PM Sensors and Design, Development and Field Evaluation of A Wearable PM Exposure Monitoring System
Authors: Wei-Ying Yi, Yu Zhou, Ya-Fen Chan, Yee Leung, Kam-Sang Woo, Wen-Wei Che, Kai-Hon Lau, Jia-Min Chen, Kwong-Sak Leung
Date: 2022-07-11 00:00:00

To mitigate the significant biases/errors in research studying the associations between PM and health, which are introduced by the coarse/inadequate assessments of PM exposure from conventional PM monitoring paradigm, a personalized monitoring system consisting of a low-cost wearable PM device is proposed. However, due to the absence of a unifying evaluation protocol for low-cost PM sensors, the evaluation results/performance specifications from existing studies/datasheets are of limited reference values when attempting to determine the best candidate for the proposed system. In this regard, the authors appeal to the research community to develop a standardized evaluation protocol for low-cost PM sensors/devices, and a unifying attempt is established in this manuscript by adopting the definitive terminology from international documents and the evaluation metrics regarded as best practices. Collocated on the rooftop of the HKUST Supersite, four empirically selected PM sensors were compared against each other and calibrated against two reference monitors. They were then evaluated against the reference following the protocol. The PlanTower PMS-A003 sensor was selected for the wearable device as it outperformed the others in terms of affordability, portability, detection capability, data quality, as well as humidity and condensation insusceptibility. An automated approach was proposed to identify and remove the condensation associated abnormal measurements. The proposed device has better affordability and portability as well as similar usability and data accessibility compared to those existing devices recognized. The first 10 devices were also evaluated and calibrated at the Supersite. Additional 120 units were manufactured and delivered to the subjects to acquire their daily PM2.5 exposures for investigating the association with subclinical atherosclerosis.
Pulsed interaction signals as a route to biological pattern formation
Authors: Eduardo H. Colombo, Cristóbal López, Emilio Hernández-García
Date: 2022-07-11 00:00:00

We identify a mechanism for biological spatial pattern formation arising when the signals that mediate interactions between individuals in a population have pulsed character. Our general population-signal framework shows that while for a slow signal-dynamics limit no pattern formation is observed for any values of the model parameters, for a fast limit, on the contrary, pattern formation can occur. Furthermore, at these limits, our framework reduces, respectively, to reaction-diffusion and spatially nonlocal models, thus bridging these approaches.
Origin of power laws and their spatial fractal structure for city-size distributions
Authors: Tomoya Mori, Takashi Akamatsu, Yuki Takayama, Minoru Osawa
Date: 2022-07-12 00:00:00
Tasks: Diversity

City-size distributions follow an approximate power law in various countries despite high volatility in relative city sizes over time. Our empirical evidence for the United States and Japan indicates that the scaling law stems from a spatial fractal structure owing to the coordination of industrial locations. While the locations of individual industries change considerably over time, there is a persistent pattern in that more localized industries at a given time are found only in larger cities. The spatial organization of cities exhibits a hierarchical structure in which larger cities are spaced apart to serve as centers for surrounding smaller cities, generating a recursive pattern across different spatial scales. In our theoretical replication of the observed regularities, diversity in scale economy among industries induces diversity in their location pattern, which translates into diversity in city size via spatial coordination of industries and population. The city-size power law is a generic feature of Monte-Carlo samples of stationary states resulting from the spontaneous spatial fractal structure in the hypothetical economy. The identified regularities reveal constraints on feasible urban planning at each regional scale. The success or failure of place-based policies designed to take advantage of individual cities' characteristics should depend on their spatial relationships with other cities, subject to the nationwide spatial fractal structure.
An algebraic characterization of self-generating chemical reaction networks using semigroup models
Authors: Dimitri Loutchko
Date: 2022-07-12 00:00:00

The ability of a chemical reaction network to generate itself by catalyzed reactions from constantly present environmental food sources is considered a fundamental property in origin-of-life research. Based on Kaufmann's autocatalytic sets, Hordijk and Steel have constructed the versatile formalism of catalytic reaction systems (CRS) to model and to analyze such self-generating networks, which they named reflexively autocatalytic and food generated (RAF). Previously, it was established that the subsequent and simultaenous catalytic functions of the chemicals of a CRS give rise to an algebraic structure, termed a semigroup model. The semigroup model allows to naturally consider the function of any subset of chemicals on the whole CRS. This gives rise to a generative dynamics by iteratively applying the function of a subset to the externally supplied food set. The fixed point of this dynamics yields the maximal self-generating set of chemicals. Moreover, the lattice of all functionally closed self-generating sets of chemicals is discussed and a structure theorem for this lattice is proven. It is also shown that a CRS which contains self-generating sets of chemicals cannot be nilpotent and thus a useful link to the combinatorial theory of finite semigroups is established. The main technical tool introduced and utilized in this work is the representation of the semigroup elements as decorated rooted trees, allowing to translate the generation of chemicals from a given set of resources into the semigroup language.
Detecting Anti-dumping Circumvention: A Network Approach
Authors: Luca Barbaglia, Christophe Croux, Ines Wilms
Date: 2022-07-12 00:00:00

Despite the increasing integration of the global economic system, anti-dumping measures are a common tool used by governments to protect their national economy. In this paper, we propose a methodology to detect cases of anti-dumping circumvention through re-routing trade via a third country. Based on the observed full network of trade flows, we propose a measure to proxy the evasion of an anti-dumping duty for a subset of trade flows directed to the European Union, and look for possible cases of circumvention of an active anti-dumping duty. Using panel regression, we are able correctly classify 86% of the trade flows, on which an investigation of anti-dumping circumvention has been opened by the European authorities.
Towards Hybrid-Optimization Video Coding
Authors: Shuai Huo, Dong Liu, Li Li, Siwei Ma, Feng Wu, Wen Gao
Date: 2022-07-12 00:00:00

Video coding is a mathematical optimization problem of rate and distortion essentially. To solve this complex optimization problem, two popular video coding frameworks have been developed: block-based hybrid video coding and end-to-end learned video coding. If we rethink video coding from the perspective of optimization, we find that the existing two frameworks represent two directions of optimization solutions. Block-based hybrid coding represents the discrete optimization solution because those irrelevant coding modes are discrete in mathematics. It searches for the best one among multiple starting points (i.e. modes). However, the search is not efficient enough. On the other hand, end-to-end learned coding represents the continuous optimization solution because the gradient descent is based on a continuous function. It optimizes a group of model parameters efficiently by the numerical algorithm. However, limited by only one starting point, it is easy to fall into the local optimum. To better solve the optimization problem, we propose to regard video coding as a hybrid of the discrete and continuous optimization problem, and use both search and numerical algorithm to solve it. Our idea is to provide multiple discrete starting points in the global space and optimize the local optimum around each point by numerical algorithm efficiently. Finally, we search for the global optimum among those local optimums. Guided by the hybrid optimization idea, we design a hybrid optimization video coding framework, which is built on continuous deep networks entirely and also contains some discrete modes. We conduct a comprehensive set of experiments. Compared to the continuous optimization framework, our method outperforms pure learned video coding methods. Meanwhile, compared to the discrete optimization framework, our method achieves comparable performance to HEVC reference software HM16.10 in PSNR.
Revisiting the Sample Adaptive Offset post-filter of VVC with Neural-Networks
Authors: Philippe Bordes, Franck Galpin, Thierry Dumas, Pavel Nikitin
Date: 2022-07-11 00:00:00
Tasks: Blocking, Video Compression

The Sample Adaptive Offset (SAO) filter has been introduced in HEVC to reduce general coding and banding artefacts in the reconstructed pictures, in complement to the De-Blocking Filter (DBF) which reduces artifacts at block boundaries specifically. The new video compression standard Versatile Video Coding (VVC) reduces the BD-rate by about 36% at the same reconstruction quality compared to HEVC. It implements an additional new in-loop Adaptive Loop Filter (ALF) on top of the DBF and the SAO filter, the latter remaining unchanged compared to HEVC. However, the relative performance of SAO in VVC has been lowered significantly. In this paper, it is proposed to revisit the SAO filter using Neural Networks (NN). The general principles of the SAO are kept, but the a-priori classification of SAO is replaced with a set of neural networks that determine which reconstructed samples should be corrected and in which proportion. Similarly to the original SAO, some parameters are determined at the encoder side and encoded per CTU. The average BD-rate gain of the proposed SAO improves VVC by at least 2.3% in Random Access while the overall complexity is kept relatively small compared to other NN-based methods.
Data-Driven Thermal Modelling for Anomaly Detection in Electric Vehicle Charging Stations
Authors: Pere Izquierdo Gómez, Alberto Barragan Moreno, Jun Lin, Tomislav Dragičević
Date: 2022-07-12 00:00:00
Tasks: Anomaly Detection

The rapid growth of the electric vehicle (EV) sector is giving rise to many infrastructural challenges. One such challenge is its requirement for the widespread development of EV charging stations which must be able to provide large amounts of power in an on-demand basis. This can cause large stresses on the electrical and electronic components of the charging infrastructure - negatively affecting its reliability as well as leading to increased maintenance and operation costs. This paper proposes a human-interpretable data-driven method for anomaly detection in EV charging stations, aiming to provide information for the condition monitoring and predictive maintenance of power converters within such a station. To this end, a model of a high-efficiency EV charging station is used to simulate the thermal behaviour of EV charger power converter modules, creating a data set for the training of neural network models. These machine learning models are then employed for the identification of anomalous performance.
Simultaneous Indoor and Outdoor 3D Localization with STAR-RIS-Assisted Millimeter Wave Systems
Authors: Jiguang He, Aymen Fakhreddine, George C. Alexandropoulos
Date: 2022-07-12 00:00:00
Tasks: Outdoor Localization

Simultaneously transmitting (refracting) and reflecting reconfigurable intelligent surfaces (STAR-RISs) have been recently identified to improve the spectrum/energy efficiency and extend the communication range. However, their potential for enhanced concurrent indoor and outdoor localization has not yet been explored. In this paper, we study the fundamental limits, i.e., the Cram\'er Rao lower bounds (CRLBs) via Fisher information analyses, on the three-dimensional (3D) localization performance with a STAR-RIS at millimeter wave frequencies. The effect of the power splitting between refraction and reflection at the STAR-RIS as well as the power allocation between the two mobile stations (MSs) are investigated. By maximizing the principal angle between the two subspaces corresponding to the STAR-RIS reflection and refraction matrices, we are able to find the optimal solutions for these simultaneous operations. We verify that high-accuracy 3D localization can be achieved for both indoor and outdoor MSs when the system parameters are well optimized.