pwc-archive/papers-with-abstracts huggingface dataset:
Power Loss Minimization of Distribution Network using Different Grid Strategies
Authors: Umar Jamil
Date: 2023-07-12 00:00:00

Power losses in electrical power systems especially, distribution systems, occur due to several environmental and technical factors. Transmission & Distribution line losses are normally 17% and 50% respectively. These losses are due to the inappropriate size of the conductor, long distribution lines, low power factor, overloading of lines etc. The power losses cause economic loss and reduce the system's reliability. The reliability of electrical power systems can be improved by decreasing network power loss and by improving the voltage profile. In radial distribution systems, power loss can also be minimized through Distributed Generation (DG) system placement. In this thesis, three different grid strategies including real power sharing, reactive power injection and transformer tap changing are discussed and used to minimize line losses. These three proposed grid strategies have been implemented using a power flow study based on Newton-Raphson (NR) and Genetic Algorithm (GA). To minimize line losses, both methods have been used for each grid strategy. The used test system in this research work is the IEEE-30 bus radial distribution system. Results obtained after simulation of each grid strategy using NR and GA shows that real load sharing is reliable with respect to minimization of line loss as compared to reactive power injection and transformer tap changing grid strategy. Comparative analysis has been performed between GA and NR for each grid strategy, results show that Genetic Algorithm is more reliable and efficient for loss minimization as compared to Newton-Raphson. In the base case for optimum power flow solution using genetic algorithm and Newton-Raphson, real line losses are 9.481475MW and 17.557MW respectively. So, GA is preferable for each proposed grid strategy to minimize line losses than NR.
Cooperative Localization for Autonomous Underwater Vehicles -- a comprehensive review
Authors: Milind Fernandes, Soumya Ranjan Sahoo, Mangal Kothari
Date: 2023-07-12 00:00:00

Cooperative localization is an important technique in environments devoid of GPS-based localization, more so in underwater scenarios, where none of the terrestrial localization techniques based on radio frequency or optics are suitable due to severe attenuation. Given the large swaths of oceans and seas where autonomous underwater vehicles (AUVs) operate, traditional acoustic positioning systems fall short on many counts. Cooperative localization (CL), which involves sharing mutual information amongst the vehicles, has thus emerged as a viable option in the past decade. This paper assimilates the research carried out in AUV cooperative localization and presents a qualitative overview. The cooperative localization approaches are categorized by their cooperation and localization strategies, while the algorithms employed are reviewed on the various challenges posed by the underwater acoustic channel and environment. Furthermore, existing problems and future scope in the domain of underwater cooperative localization are discussed.
Model-Free Control Design for Feedback-Linearizable SISO Systems
Authors: Karthik Shenoy, Akshit Saradagi, Ramkrishna Pasumarthy, Vijaysekhar Chellaboina
Date: 2023-07-12 00:00:00

Data-driven control has gained significant attention in recent years, particularly regarding feedback linearization of nonlinear systems. However, existing approaches face limitations when it comes to implementing them on hardware. The main challenges include the need for very small sampling times, which strain hardware capabilities, and the requirement of an initial open-loop data set, which can be impractical for stabilizing unstable equilibrium points. To address these issues, we propose a two-stage model-free approach that combines a high-gain observer and a dynamic controller. This eliminates the hardware implementation difficulties mentioned earlier. The high-gain observer acts as a robust state estimator, offering superior noise attenuation and lower computational costs, crucial factors for digital hardware implementation. Unlike data-driven methods, our design's stability and performance depend on a tunable software parameter, simplifying digital implementation without overburdening hardware resources. Experimental results on a Twin Rotor system demonstrate the effectiveness of our approach compared to the state-of-the-art data-driven method.
On the Design of Nonlinear MPC and LPVMPC for Obstacle Avoidance in Autonomous Driving
Authors: Maryam Nezami, Dimitrios S. Karachalios, Georg Schildbach, Hossam S. Abbas
Date: 2023-07-12 00:00:00
Tasks: Autonomous Driving, Model Predictive Control, Scheduling

In this study, we are concerned with autonomous driving missions when a static obstacle blocks a given reference trajectory. To provide a realistic control design, we employ a model predictive control (MPC) utilizing nonlinear state-space dynamic models of a car with linear tire forces, allowing for optimal path planning and tracking to overtake the obstacle. We provide solutions with two different methodologies. Firstly, we solve a nonlinear MPC (NMPC) problem with a nonlinear optimization framework, capable of considering the nonlinear constraints. Secondly, by introducing scheduling signals, we embed the nonlinear dynamics in a linear parameter varying (LPV) representation with adaptive linear constraints for realizing the nonlinear constraints associated with the obstacle. Consequently, an LPVMPC optimization problem can be solved efficiently as a quadratic programming (QP) that constitutes the main novelty of this work. We test the two methods for a challenging obstacle avoidance task and provide qualitative comparisons. The LPVMPC shows a significant reduction in terms of the computational burden at the expense of a slight loss of performance.
Robust internal model control approach for position control of systems with sandwiched backlash
Authors: Yoav Vered, Stephen J. Elliott
Date: 2023-07-12 00:00:00
Tasks: Position

This paper describes the design of a robust controller for position control in systems with sandwiched backlash. The backlash, which is nonsmooth and nonlinear, is inevitable in the operation of many systems, but it can have destructive effects on the stability and performance of feedback systems. In this work, a robust controller is designed using a modified linear internal model control framework. Different controller architectures are considered and compared based on an experimental case study. The experimental testbased is composed of a three-platform structure driven by a stepper motor. The backlash is introduced into the system in a non-destructive and controllable manner by closing an internal nonlinear feedback loop around the stepper motor. The robustness of the designed controller to a large amount of backlash is verified experimentally, and while the stability is maintained, some residual vibrations are observed. The effects on the residual vibration levels of including nonlinear elements in the controller and changing the controller's settling time are also examined experimentally. The robustness to changes and mismodelling of the linear system, with and without the backlash, is described, as is the tracking of a smooth sinusoidal command signal with a growing amount of backlash. Based on the case study, it is concluded that combining the linear internal model control design method with a small dead zone results in a highly robust controller both with respect to the backlash and to changes in the linear system, which ensure stability and good performance. The required robustness is achieved by tuning the controller's settling time and the dead zone width parameters.
Fuel-Optimal Collision Avoidance Maneuvers in Long-Term Encounters with Station-Keeping Constraints
Authors: Zeno Pavanello, Laura Pirovano, Roberto Armellin
Date: 2023-07-12 00:00:00
Tasks: Collision Avoidance

This work presents a sequential convex program method to compute fuel-optimal collision avoidance maneuvers for long-term encounters. The low-thrust acceleration model is used to account for the control, but the method can compute high-thrust maneuvers by increasing the maximum available acceleration. Dealing with the long-term conjunction poses additional challenges compared to the short-term problem because the encounter is not instantaneous. Thus, under the assumption of Gaussian statistics, the probability of collision is replaced by a simpler metric, the instantaneous probability of collision (IPoC) and a keep-out zone constraint is formulated as a continuous condition to be respected throughout the time frame of interest. The robustness of the solution is improved by introducing a constraint on the sensitivity of the IPoC. Furthermore, the collision avoidance problem is coupled with the classical station-keeping requirement for geostationary Earth orbit satellites and with a return to the nominal orbit condition for low Earth orbit satellites. Even though no guarantee is given for the recovery of the global optimum solution, numerical simulations in different orbital regimes show that the proposed approach can yield a local fuel-optimal solution with a run-time suitable for autonomous applications.
Implementation of a new authorization system from monolithic solution to microservice architecture
Authors: David Ahmad Abboud, Damien Jacob
Date: 2023-07-12 00:00:00
Tasks: Management

Monolithic applications used to be considered the standard for software development. However, due to the rapid evolution of technology and the increasing demand for scalability and flexibility, these applications have become increasingly inadequate for contemporary environment. In response to these challenges, developers have begun to adopt a microservice (MS) architecture, which offers a modular approach to software creation. However, this transition requires to rethink the enablement system to meet the new requirements. Two MS architectures can be deployed: a centralized or decentralized architecture. Based on the requirements of the application's users, a centralized authorization management architecture was chosen. The purpose of this study is to explain the migration from a Role- Based Access Control (RBAC) authorization system to a centralized microservice authorization architecture. The migration is carried out in two stages: 1) Creation of an authorization microservice 2) Abandonment of RBAC
Knowledge-Driven Resource Allocation for D2D Networks: A WMMSE Unrolled Graph Neural Network Approach
Authors: Hao Yang, Nan Cheng, Ruijin Sun, Wei Quan, Rong Chai, Khalid Aldubaikhy, Abdullah Alqasir, Xuemin Shen
Date: 2023-07-12 00:00:00
Tasks: Graph Neural Network, Management

This paper proposes an novel knowledge-driven approach for resource allocation in device-to-device (D2D) networks using a graph neural network (GNN) architecture. To meet the millisecond-level timeliness and scalability required for the dynamic network environment, our proposed approach incorporates the deep unrolling of the weighted minimum mean square error (WMMSE) algorithm, referred to as domain knowledge, into GNN, thereby reducing computational delay and sample complexity while adapting to various data distributions. Specifically, the aggregation and update functions in the GNN architecture are designed by utilizing the summation and power calculation components of the WMMSE algorithm, which leads to improved model generalization and interpretabiliy. Theoretical analysis of the proposed approach reveals its capability to simplify intricate end-to-end mappings and diminish the model exploration space, resulting in increased network expressiveness and enhanced optimization performance. Simulation results demonstrate the robustness, scalability, and strong performance of the proposed knowledge-driven resource allocation approach across diverse communication topologies without retraining. Our findings contribute to the development of efficient and scalable wireless resource management solutions for distributed and dynamic networks with strict latency requirements.
Geometrical Structure of Bifurcations during Spatial Decision-Making
Authors: Dan Gorbonos, Nir S. Gov, Iain D. Couzin
Date: 2023-07-11 00:00:00
Tasks: Decision Making

Animals must constantly make decisions on the move, such as when choosing among multiple options, or "targets", in space. Recent evidence suggests that this results from a recursive feedback between the (vectorial) neural representation of the targets and the resulting motion defined by this consensus, which then changes the egocentric neural representation of the the options, and so on. Here we employ a simple model of this process to both explore how its dynamics account for the experimentally-observed abruptly-branching trajectories exhibited by animals during spatial decision-making, and to provide new insights into spatiotemporal computation. Essential neural dynamics, notably local excitation and long-range inhibition, are captured in our model via spin-system dynamics, with groups of Ising-spins representing neural "activity bumps" corresponding to target directions. Analysis, employing a novel "mean-field trajectory" approach, reveals the nature of the spontaneous symmetry breaking - bifurcations in the model that result in literal bifurcations in trajectory space and how it results in new geometric principles for spatiotemporal decision-making. We find that all bifurcation points, beyond the very first, fall on a small number of "bifurcation curves". It is the spatial organization of these curves that is shown to be key to determining the shape of the trajectories, such as self-similar or space filling, exhibited during decision-making, irrespective of the trajectory's starting point. Furthermore, we find that a non-Euclidean representation of space considerably reduces the number of bifurcation points in many geometrical configurations, preventing endless indecision and promoting effective spatial decision-making. This suggests that a non-Euclidean neural representation of space may be expected to have evolved across species in order to facilitate spatial decision-making.
Stationarity with Occasionally Binding Constraints
Authors: James A. Duffy, Sophocles Mavroeidis, Sam Wycherley
Date: 2023-07-12 00:00:00

This paper studies a class of multivariate threshold autoregressive models, known as censored and kinked structural vector autoregressions (CKSVAR), which are notably able to accommodate series that are subject to occasionally binding constraints. We develop a set of sufficient conditions for the processes generated by a CKSVAR to be stationary, ergodic, and weakly dependent. Our conditions relate directly to the stability of the deterministic part of the model, and are therefore less conservative than those typically available for general vector threshold autoregressive (VTAR) models. Though our criteria refer to quantities, such as refinements of the joint spectral radius, that cannot feasibly be computed exactly, they can be approximated numerically to a high degree of precision.
Identification in Multiple Treatment Models under Discrete Variation
Authors: Vishal Kamat, Samuel Norris, Matthew Pecenco
Date: 2023-07-12 00:00:00

We develop a method to learn about treatment effects in multiple treatment models with discrete-valued instruments. We allow selection into treatment to be governed by a general class of threshold crossing models that permits multidimensional unobserved heterogeneity. Under a semi-parametric restriction on the distribution of unobserved heterogeneity, we show how a sequence of linear programs can be used to compute sharp bounds for a number of treatment effect parameters when the marginal treatment response functions underlying them remain nonparametric or are additionally parameterized.
Robust Impulse Responses using External Instruments: the Role of Information
Authors: Davide Brignone, Alessandro Franconi, Marco Mazzali
Date: 2023-07-12 00:00:00

External-instrument identification leads to biased responses when the shock is not invertible and the measurement error is present. We propose to use this identification strategy in a structural Dynamic Factor Model, which we call Proxy DFM. In a simulation analysis, we show that the Proxy DFM always successfully retrieves the true impulse responses, while the Proxy SVAR systematically fails to do so when the model is either misspecified, does not include all relevant information, or the measurement error is present. In an application to US monetary policy, the Proxy DFM shows that a tightening shock is unequivocally contractionary, with deteriorations in domestic demand, labor, credit, housing, exchange, and financial markets. This holds true for all raw instruments available in the literature. The variance decomposition analysis highlights the importance of monetary policy shocks in explaining economic fluctuations, albeit at different horizons.
What Does it Take to Control Global Temperatures? A toolbox for testing and estimating the impact of economic policies on climate
Authors: Guillaume Chevillon, Takamitsu Kurita
Date: 2023-07-11 00:00:00
Tasks: counterfactual

This paper tests the feasibility and estimates the cost of climate control through economic policies. It provides a toolbox for a statistical historical assessment of a Stochastic Integrated Model of Climate and the Economy, and its use in (possibly counterfactual) policy analysis. Recognizing that stabilization requires supressing a trend, we use an integrated-cointegrated Vector Autoregressive Model estimated using a newly compiled dataset ranging between years A.D. 1000-2008, extending previous results on Control Theory in nonstationary systems. We test statistically whether, and quantify to what extent, carbon abatement policies can effectively stabilize or reduce global temperatures. Our formal test of policy feasibility shows that carbon abatement can have a significant long run impact and policies can render temperatures stationary around a chosen long run mean. In a counterfactual empirical illustration of the possibilities of our modeling strategy, we study a retrospective policy aiming to keep global temperatures close to their 1900 historical level. Achieving this via carbon abatement may cost about 75% of the observed 2008 level of world GDP, a cost equivalent to reverting to levels of output historically observed in the mid 1960s. By contrast, investment in carbon neutral technology could achieve the policy objective and be self-sustainable as long as it costs less than 50% of 2008 global GDP and 75% of consumption.
Decentralized Decision-Making in Retail Chains: Evidence from Inventory Management
Authors: Victor Aguirregabiria, Francis Guiton
Date: 2023-07-09 00:00:00
Tasks: counterfactual, Decision Making, Management

This paper investigates the impact of decentralizing inventory decision-making in multi-establishment firms using data from a large retail chain. Analyzing two years of daily data, we find significant heterogeneity among the inventory decisions made by 634 store managers. By estimating a dynamic structural model, we reveal substantial heterogeneity in managers' perceived costs. Moreover, we observe a correlation between the variance of these perceptions and managers' education and experience. Counterfactual experiments show that centralized inventory management reduces costs by eliminating the impact of managers' skill heterogeneity. However, these benefits are offset by the negative impact of delayed demand information.
S Equilibrium: A Synthesis of (Behavioral) Game Theory
Authors: Jacob K Goeree, Bernardo Garcia-Pola
Date: 2023-07-12 00:00:00

$S$ equilibrium synthesizes a century of game-theoretic modeling. $S$-beliefs determine choices as in the refinement literature and level-$k$, without anchoring on Nash equilibrium or imposing ad hoc belief formation. $S$-choices allow for mistakes as in QRE, without imposing rational expectations. $S$ equilibrium is explicitly set-valued to avoid the common practice of selecting the best prediction from an implicitly defined set of unknown, and unaccounted for, size. $S$-equilibrium sets vary with a complexity parameter, offering a trade-off between accuracy and precision unlike in $M$ equilibrium. Simple "areametrics" determine the model's parameter and show that choice sets with a relative size of 5 percent capture 58 percent percent of the data. Goodness-of-fit tests applied to data from a broad array of experimental games confirm $S$ equilibrium's ability to predict behavior in and out of sample. In contrast, choice (belief) predictions of level-$k$ and QRE are rejected in most (all) games.
Spatially-Adaptive Learning-Based Image Compression with Hierarchical Multi-Scale Latent Spaces
Authors: Fabian Brand, Alexander Kopte, Kristian Fischer, André Kaup
Date: 2023-07-12 00:00:00
Tasks: Image Compression, Video Compression

Adaptive block partitioning is responsible for large gains in current image and video compression systems. This method is able to compress large stationary image areas with only a few symbols, while maintaining a high level of quality in more detailed areas. Current state-of-the-art neural-network-based image compression systems however use only one scale to transmit the latent space. In previous publications, we proposed RDONet, a scheme to transmit the latent space in multiple spatial resolutions. Following this principle, we extend a state-of-the-art compression network by a second hierarchical latent-space level to enable multi-scale processing. We extend the existing rate variability capabilities of RDONet by a gain unit. With that we are able to outperform an equivalent traditional autoencoder by 7% rate savings. Furthermore, we show that even though we add an additional latent space, the complexity only increases marginally and the decoding time can potentially even be decreased.
Kriging-Based 3-D Spectrum Awareness for Radio Dynamic Zones Using Aerial Spectrum Sensors
Authors: Sung Joon Maeng, Ozgur Ozdemir, Ismail Guvenc, Mihail L. Sichitiu
Date: 2023-07-12 00:00:00

Radio dynamic zones (RDZs) are geographical areas within which dedicated spectrum resources are monitored and controlled to enable the development and testing of new spectrum technologies. Real-time spectrum awareness within an RDZ is critical for preventing interference with nearby incumbent users of the spectrum. In this paper, we consider a 3D RDZ scenario and propose to use unmanned aerial vehicles (UAVs) equipped with spectrum sensors to create and maintain a 3D radio map of received signal power from different sources within the RDZ. In particular, we introduce a 3D Kriging interpolation technique that uses realistic 3D correlation models of the signal power extracted from extensive measurements carried out at the NSF AERPAW platform. Using C-Band signal measurements by a UAV at altitudes between 30 m-110 m, we first develop realistic propagation models on air-to-ground path loss, shadowing, spatial correlation, and semi-variogram, while taking into account the knowledge of antenna radiation patterns and ground reflection. Subsequently, we generate a 3D radio map of a signal source within the RDZ using the Kriging interpolation and evaluate its sensitivity to the number of measurements used and their spatial distribution. Our results show that the proposed 3D Kriging interpolation technique provides significantly better radio maps when compared with an approach that assumes perfect knowledge of path loss.