pwc-archive/papers-with-abstracts huggingface dataset:
Decentralized Singular Value Decomposition for Large-scale Distributed Sensor Networks
Authors: Yufan Fan, Marius Pesavento
Date: 2024-08-26 00:00:00
Tasks: Low-Rank Matrix Completion, Matrix Completion

This article studies the problem of decentralized Singular Value Decomposition (d-SVD), which is fundamental in various signal processing applications. Two scenarios are considered depending on the availability of the data matrix under consideration. In the first scenario, the matrix of interest is row-wisely available in each local node in the network. In the second scenario, the matrix of interest implicitly forms an outer product from two different series of measurements. By combining the lightweight local rational function approximation approach with parallel averaging consensus algorithms, two d-SVD algorithms are proposed to cope with the two aforementioned scenarios. We evaluate the proposed algorithms using two application examples: decentralized sensor localization via low-rank matrix completion and decentralized passive radar detection. Moreover, a novel and non-trivial truncation technique, which employs a representative vector that is orthonormal to the principal signal subspace, is proposed to further reduce the communication cost associated with the d-SVD algorithms. Simulation results show that the proposed d-SVD algorithms converge to the centralized solution with reduced communication cost compared to those facilitated with the state-of-the-art decentralized power method.
Applying digital twins for the management of information in turnaround event operations in commercial airports
Authors: Javier Conde, Andres Munoz-Arcentales, Mario Romero, Javier Rojo, Joaquín Salvachúa, Gabriel Huecas, Álvaro Alonso
Date: 2024-08-26 00:00:00
Tasks: Management

The aerospace sector is one of the many sectors in which large amounts of data are generated. Thanks to the evolution of technology, these data can be exploited in several ways to improve the operation and management of industrial processes. However, to achieve this goal, it is necessary to define architectures and data models that allow to manage and homogenise the heterogeneous data collected. In this paper, we present an Airport Digital Twin Reference Conceptualisation's and data model based on FIWARE Generic Enablers and the Next Generation Service Interfaces-Linked Data standard. Concretely, we particularise the Airport Digital Twin to improve the efficiency of flight turnaround events. The architecture proposed is validated in the Aberdeen International Airport with the aim of reducing delays in commercial flights. The implementation includes an application that shows the real state of the airport, combining two-dimensional and three-dimensional virtual reality representations of the stands, and a mobile application that helps ground operators to schedule departure and arrival flights.
Hierarchical-type Model Predictive Control and Experimental Evaluation for a Water-Hydraulic Artificial Muscle with Direct Data-Driven Adaptive Model Matching
Authors: Satoshi Tsuruhara, Kazuhisa Ito
Date: 2024-08-26 00:00:00
Tasks: Model Predictive Control

High-precision displacement control for water-hydraulic artificial muscles is a challenging issue due to its strong hysteresis characteristics that is hard to be modelled precisely, and many control methods have been proposed. Recently, data-driven control methods have attracted much attention because they do not explicitly use mathematical models, making design much easier. In our previous work, we proposed fictitious reference iterative tuning (FRIT)-based model predictive control (FMPC), which combines data-driven and model-based methods for the muscle and showed its effectiveness because it can consider input constraints as well. However, the problem in which control performance strongly depends on prior input-output data remains still unsolved. Adaptive FRIT based on directional forgetting has also been proposed; however, it is difficult to achieve the desired transient performance because it cannot consider input constraints and there are no design parameters that directly determine the control performance, such as MPC. In this study, we propose a novel data-driven adaptive model matching-based controller that combines these methods. Experimental results show that the proposed method could significantly improve the control performance and achieve high robustness against inappropriate initial experimental data , while considering the input constraints in the design phase.
Miniaturized Patch Rectenna Using 3-Turn Complementary Spiral Resonator for Wireless Power Transfer
Authors: Ali Raza, Rasool Keshavarz, Negin Shariati
Date: 2024-08-26 00:00:00

A miniaturized linearly-polarized patch antenna is presented for Wireless Power Transfer (WPT) at 1. 8 GHz. The proposed antenna consists of a patch element and a 3-turn Complementary Spiral Resonator (3-CSR) with antenna dimension of 50 mm x 50 mm. 3-CSR is inserted in the ground plane to reduce the antenna size. This modification also increased the impedance bandwidth from 43 MHz (1.78-1.83 GHz) to 310 MHz (1.69-2.0 GHz) . Moreover, antenna is fabricated and simulated and measured results are in good agreement. Additionally, a rectifier and matching circuits are designed at -10 dBm to realize a rectenna (rectifying antenna) for WPT application. Rectenna efficiency of 53.6 % is achieved at a low input power of -10 dBm.
Speeding Ticket: Unveiling the Energy and Emission Burden of AI-Accelerated Distributed and Decentralized Power Dispatch Models
Authors: Meiyi Li, Javad Mohammadi
Date: 2024-08-26 00:00:00
Tasks: Decision Making, Management

As the modern electrical grid shifts towards distributed systems, there is an increasing need for rapid decision-making tools. Artificial Intelligence (AI) and Machine Learning (ML) technologies are now pivotal in enhancing the efficiency of power dispatch operations, effectively overcoming the constraints of traditional optimization solvers with long computation times. However, this increased efficiency comes at a high environmental cost, escalating energy consumption and carbon emissions from computationally intensive AI/ML models. Despite their potential to transform power systems management, the environmental impact of these technologies often remains an overlooked aspect. This paper introduces the first comparison of energy demands across centralized, distributed, and decentralized ML-driven power dispatch models. We provide a detailed analysis of the energy and carbon footprint required for continuous operations on an IEEE 33 bus system, highlighting the critical trade-offs between operational efficiency and environmental sustainability. This study aims to guide future AI implementations in energy systems, ensuring they enhance not only efficiency but also prioritize ecological integrity.
On output consensus of heterogeneous dynamical networks
Authors: Yongkang Su, Lanlan Su, Sei Zhen Khong
Date: 2024-08-25 00:00:00

This work is concerned with interconnected networks with non-identical subsystems. We investigate the output consensus of the network where the dynamics are subject to external disturbance and/or reference input. For a network of output-feedback passive subsystems, we first introduce an index that characterises the gap between a pair of adjacent subsystems by the difference of their input-output trajectories. The set of these indices quantifies the level of heterogeneity of the networks. We then provide a condition in terms of the level of heterogeneity and the connectivity of the networks for ensuring the output consensus of the interconnected network.
Data-driven harmonic output regulation of a class of nonlinear systems
Authors: Zhongjie Hu, Claudio De Persis, John W. Simpson-Porco, Pietro Tesi
Date: 2024-08-25 00:00:00

The paper deals with the data-based design of state-feedback controllers that solve the output regulation problem for a class of nonlinear systems. Inspired by recent developments in model-based output regulation design techniques and in data-driven control design for nonlinear systems, we derive a data-dependent semidefinite program that, when solved, directly returns a controller that steers the regulation error to a periodic signal whose Fourier series has identically zero coefficients up to a certain order set by the controller. When specialized to the case of linear systems, the result appears to improve upon existing work. Numerical results illustrate the findings
Watercraft as Overwater Ambulance Exchange Points to Enhance Aeromedical Evacuation
Authors: Mahdi Al-Husseini, Kyle H. Wray, Mykel J. Kochenderfer
Date: 2024-08-25 00:00:00

Ambulance exchange points are preidentified sites where patients are transferred between evacuation platforms while en route to enhanced medical care. We propose a new capability for maritime medical evacuation, which involves co-opting underway watercraft as overwater ambulance exchange points to transfer patients between medical evacuation aircraft. We partner with the United States Army's 25th Combat Aviation Brigade to demonstrate the use of an Army watercraft as an overwater ambulance exchange point. A manikin is transferred between two HH-60 Medical Evacuation Black Hawk helicopters conducting hoist operations over Army Logistics Support Vessel 3, which is traveling south of Honolulu, Hawaii. The demonstration is enabled by a decision support system for dispatching aircraft, hoist stabilization technology, commercial satellite internet, military geospatial infrastructure applications, and digital medical documentation tools, the benefits of which are all discussed. Three extensions of the overwater ambulance exchange point are introduced and civilian applications are considered.
Sensing-aided Near-Field Secure Communications with Mobile Eavesdroppers
Authors: Yiming Xu, Mingxuan Zheng, Dongfang Xu, Shenghui Song, Daniel Benevides da Costa
Date: 2024-08-25 00:00:00
Tasks: Integrated sensing and communication, ISAC

The additional degree of freedom (DoF) in the distance domain of near-field communication offers new opportunities for physical layer security (PLS) design. However, existing works mainly consider static eavesdroppers, and the related study with mobile eavesdroppers is still in its infancy due to the difficulty in obtaining the channel state information (CSI) of the eavesdropper. To this end, we propose to leverage the sensing capability of integrated sensing and communication (ISAC) systems to assist PLS design. To comprehensively study the dynamic behaviors of the system, we propose a Pareto optimization framework, where a multi-objective optimization problem (MOOP) is formulated to simultaneously optimize three key performance metrics: power consumption, number of securely served users, and tracking performance, while guaranteeing the achievable rate of the users with a given leakage rate constraint. A globally optimal design based on the generalized Benders decomposition (GBD) method is proposed to achieve the Pareto optimal solutions. To reduce the computational complexity, we further design a low-complexity algorithm based on zero-forcing (ZF) beamforming and successive convex approximation (SCA). Simulation results validate the effectiveness of the proposed algorithms and reveal the intrinsic trade-offs between the three performance metrics. It is observed that near-field communication offers a favorable beam diffraction effect for PLS, where the energy of the information signal is nulled around the eavesdropper and focused on the users.
Integrated Modeling and Forecasting of Electric Vehicles Charging Profiles Based on Real Data
Authors: Octavio Ramos-Leaños, Hussein Suprême, Mouhamadou Makthar Dione, Daniel Chabot, Vincent Beaulieu
Date: 2024-08-25 00:00:00
Tasks: Time Series

In the context of energy transition and decarbonization of the economy, several governments will ban the sale of new combustion vehicles by 2050. Thus, growing penetration of electric vehicles (EVs) in distribution networks (DN) is predicted. Impact analyzes must be performed to determine if mitigation means are needed to accommodate a large quantity of EVs in the DN. Furthermore, the habits of the local population resulting in different EVs charging patterns needs to be realistically considered. This article proposed an individual residential EVs multi-charging algorithm based on the observed charging behavior of 500 measured residential EVs located in a large North American utility. Probability functions are derived from the analysis of these charging patterns. These can be used to model daily charging profiles of individual EV to assess, in a quasi-static time-series perspective, their impact either on a single costumer or a whole DN. An impact evaluation study is also presented.
Receding-Horizon Games with Tullock-Based Profit Functions for Electric Ride-Hailing Markets
Authors: Marko Maljkovic, Gustav Nilsson, Nikolas Geroliminis
Date: 2024-08-24 00:00:00

This paper proposes a receding-horizon, game-theoretic charging planning mechanism for electric ride-hailing markets. As the demand for ride-hailing services continues to surge and governments advocate for stricter environmental regulations, integrating electric vehicles into these markets becomes inevitable. The proposed framework addresses the challenges posed by dynamic demand patterns, fluctuating energy costs, and competitive dynamics inherent in such markets. Leveraging the concept of receding-horizon games, we propose a method to optimize proactive dispatching of vehicles for recharging over a predefined time horizon. We integrate a modified Tullock contest that accounts for customer abandonment due to long waiting times to model the expected market share, and by factoring in the demand-based electricity charging, we construct a game capturing interactions between two companies over the time horizon. For this game, we first establish the existence and uniqueness of the Nash equilibrium and then present a semi-decentralized, iterative method to compute it. Finally, the method is evaluated in an open-loop and a closed-loop manner in a simulated numerical case study.
Enabling Wireless Communications, Energy Harvesting, and Energy Saving by Using a Multimode Smart Nonlinear Circuit (MSNC)
Authors: Rasool Keshavarz, Dimitrios L. Sounas, Saeed Keshavarz, Negin Shariati
Date: 2024-08-24 00:00:00

In this paper, a multimode smart nonlinear circuit (MSNC) for wireless communications (Tx and Rx modes) as well as energy harvesting (EH) and power saving is presented. The proposed MSNC is designed at 680 MHz and has three ports, which are connected to an antenna, and T/R (transceiver) and power-saving modules. According to the input/output power level, the proposed MSNC has three modes of operations; Receiving (Rx), power saving and transmitting (Tx), for low (<-25 dBm), mid (>-25 dBm and <0 dBm) and high (>5 dBm) power ranges, respectively. In the power-saving mode, when the received power is greater than the sensitivity of the Rx module, the excess power is directed to the energy harvesting load (power storage), while the receiving direction is still in place. The fact that the proposed MSNC can manage the received power level smartly and without any external control, distinguishes the proposed MSNC from other EH circuits. The proposed MSNC operates within a power range from -50 dBm to +15 dBm, demonstrates an efficiency of more than 60% in the power-saving mode, and has acceptable matching over a large frequency range. The design procedure of the proposed MSNC along with the theoretical, simulation and measurement results are presented in this paper. Good agreement between theory, simulation and measurement results confirms the accuracy of design procedure.
Quantitative Evaluation of Full-Scale Ship Maneuvering Characteristics During Berthing and Unberthing
Authors: Agnes N. Mwange, Yoshiki Miyauchi, Taichi Kambara, Hiroaki Koike, Kazuyoshi Hosogaya, Atsuo Maki
Date: 2024-08-24 00:00:00
Tasks: Time Series Analysis

Leveraging empirical data is crucial in the development of accurate and reliable virtual models for the advancement of autonomous ship technologies and the optimization of port operations. This study presents an in-depth analysis of ship berthing and unberthing maneuvering characteristics by utilizing a comprehensive dataset encompassing the operation of a full-scale ship in diverse infrastructural and environmental conditions. Various statistical techniques and time-series analysis were employed to process and interpret the operational data. A systematic analysis was conducted on key performance variables, including approach speed, drift angles, turning motions, distance from obstacles, and actuator utilization. The results demonstrate significant discrepancies between the empirical data and the established maneuvering characteristics. These findings have the potential to significantly enhance the accuracy and reliability of conventional maneuvering models, such as the Mathematical Modeling Group (MMG) model, and improve the conditions used in captive model tests for the identification of maneuvering model parameters. Furthermore, these findings could inform the development of more robust autonomous berthing and unberthing algorithms and digital twins.
Extraction of Typical Operating Scenarios of New Power System Based on Deep Time Series Aggregation
Authors: Zhaoyang Qu, Zhenming Zhang, Nan Qu, Yuguang Zhou, Yang Li, Tao Jiang, Min Li, Chao Long
Date: 2024-08-23 00:00:00
Tasks: Scheduling, Time Series

Extracting typical operational scenarios is essential for making flexible decisions in the dispatch of a new power system. This study proposed a novel deep time series aggregation scheme (DTSAs) to generate typical operational scenarios, considering the large amount of historical operational snapshot data. Specifically, DTSAs analyze the intrinsic mechanisms of different scheduling operational scenario switching to mathematically represent typical operational scenarios. A gramian angular summation field (GASF) based operational scenario image encoder was designed to convert operational scenario sequences into high-dimensional spaces. This enables DTSAs to fully capture the spatiotemporal characteristics of new power systems using deep feature iterative aggregation models. The encoder also facilitates the generation of typical operational scenarios that conform to historical data distributions while ensuring the integrity of grid operational snapshots. Case studies demonstrate that the proposed method extracted new fine-grained power system dispatch schemes and outperformed the latest high-dimensional featurescreening methods. In addition, experiments with different new energy access ratios were conducted to verify the robustness of the proposed method. DTSAs enables dispatchers to master the operation experience of the power system in advance, and actively respond to the dynamic changes of the operation scenarios under the high access rate of new energy.
Optimal Dispatch Strategy for a Multi-microgrid Cooperative Alliance Using a Two-Stage Pricing Mechanism
Authors: Yonghui Nie, Zhi Li, Jie Zhang, Lei Gao, Yang Li, Hengyu Zhou
Date: 2024-08-23 00:00:00
Tasks: Scheduling

To coordinate resources among multi-level stakeholders and enhance the integration of electric vehicles (EVs) into multi-microgrids, this study proposes an optimal dispatch strategy within a multi-microgrid cooperative alliance using a nuanced two-stage pricing mechanism. Initially, the strategy assesses electric energy interactions between microgrids and distribution networks to establish a foundation for collaborative scheduling. The two-stage pricing mechanism initiates with a leader-follower game, wherein the microgrid operator acts as the leader and users as followers. Subsequently, it adjusts EV tariffs based on the game's equilibrium, taking into account factors such as battery degradation and travel needs to optimize EVs' electricity consumption. Furthermore, a bi-level optimization model refines power interactions and pricing strategies across the network, significantly enhancing demand response capabilities and economic outcomes. Simulation results demonstrate that this strategy not only increases renewable energy consumption but also reduces energy costs, thereby improving the overall efficiency and sustainability of the system.
Survey Paper on Control Barrier Functions
Authors: Promit Panja
Date: 2024-08-17 00:00:00
Tasks: Survey

Control Barrier Functions (CBFs) have emerged as a powerful paradigm in control theory, providing a principled approach to enforcing safety-critical constraints in dynamic systems. This survey paper comprehensively explores the foundational principles of CBFs, delves into the complexities of High Order Control Barrier Functions (HOCBFs), and extends the discussion to the adaptive realm with adaptive Control Barrier Functions (aCBFs). Through a systematic examination of theoretical underpinnings, practical applications, and the evolving landscape of research, this survey highlights the versatility of CBFs in addressing safety and stability challenges.
The LiU-ICE Benchmark -- An Industrial Fault Diagnosis Case Study
Authors: Daniel Jung, Erik Frisk, Mattias Krysander
Date: 2024-08-16 00:00:00
Tasks: Fault Detection, Fault Diagnosis

This paper presents the LiU-ICE fault diagnosis benchmark. The purpose of the benchmark is to support fault diagnosis research by providing data and a model of an industrially relevant system. Data has been collected from an internal combustion engine test bench operated in both nominal and faulty modes. A state-of-the-art model of the air path through an internal combustion engine with unknown parameters is provided. This benchmark has previously been used in a competition at the 12th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes (Safe Process) 2024, Ferrara, Italy.
Gradient based reachability analysis for goal-oriented guidance. Application to Eros proximity operations
Authors: Antonio Rizza, Francesco Topputo
Date: 2024-08-12 00:00:00

The recent growing interest in small solar system bodies such as asteroids and comets for scientific inspection, exploitation of resources, and planetary defense reasons is pushing the development of innovative engineering solutions to better investigate these celestial bodies. Current approaches for autonomous proximity operations often relies on tracking a reference trajectory previously designed and optimized on-ground. An innovative concept developed in recent years is proposing a paradigm shift towards autonomous goal-oriented strategies, providing the probe with high-level tasks and enabling replanning capability on-board. A gradient base reachability exploration scheme is introduced in this work to efficiently compute high reward paths with limited computational resource. The methodology is tested in a case study involving proximity operations to asteroid (433) Eros, comparing the proposed approach with other exploration techniques and showing promising results.