pwc-archive/papers-with-abstracts huggingface dataset:
A Family of Interleaved High Step-Up DC-DC Converters by Integrating a Voltage Multiplier and an Active Clamp Circuits
Authors: R. Beiranvand, S. H. Sangani
Date: 2023-05-29 00:00:00

A family of interleaved current-fed high step-up dc-dc converters are introduced and analyzed here by combining a voltage multiplier (VM) and an active clamp circuit for high-voltage high-power applications. Low input currents and output voltages ripples values and high voltage-gains characteristics of these converters make them suitable for lots of dc-dc applications. All power devices operate entirely under soft switching conditions, even when wide load and input voltage variations are applied. Thus, they can be designed at high switching frequencies to reduce passive components sizes to achieve high-power density, one of the main targets of the power electronics researches. Also, their input and output ports common ground simplifies the gate-drives and control circuits. To verify the given analyses and simulations, a 120-320 V to 1 kV, 50-1300 W three-stage two-leg prototype converter has been implemented at 100 kHz. Based on the experimental results, maximum efficiency of 96.5% is achieved.
Design of Rim-Located Reconfigurable Reflectarrays for Interference Mitigation in Reflector Antennas
Authors: Jordan Budhu, Sean V. Hum, Steven Ellingson, R. Michael Buehrer
Date: 2023-05-29 00:00:00

Radio telescopes are susceptible to interference arriving through its sidelobes. If a reflector antenna could be retrofitted with an adaptive null steering system, it could potentially mitigate this interference. The design of a reflectarray which can be used to reconfigure a radio telescopes radiation pattern by driving a null to the angle of incoming interference is presented. The reflectarray occupies only a portion of the rim of the original reflector and lays conformal to the paraboloid within this region. The conformal reflectarray contains unit cells with 1-bit reconfigurability stemming from two symmetrically placed PIN diodes. It is found that the dielectric and switch losses introduced by the reflectarray do not significantly affect the radio telescopes efficiency since the reflectarray is placed only along the outer rim of the reflector which is weakly illuminated. Simulation results of an L-band reconfigurable reflectarray for an 18m prime focus fed parabola are presented.
Development of a Power Quality Based Digital Energy Meter Educational Platform
Authors: Mislav Bui, Marko Jurčević
Date: 2023-05-29 00:00:00

Phasor Measurement Units (PMUs) are being used extensively for electrical grid monitoring and control. However, their cost prohibits further adoption on the distribution grid and easy access for educational purposes. This paper proposes that simple and fundamental functions of a PMU can be achieved using an energy metering IC and integrated into smart electricity meters, providing a lower cost and more widely available method of monitoring and control of distribution grids, and presents a proof-of-concept platform with aforementioned functionality. The described platform's construction and flexibility emphasizes its educational capabilities PMUs and electricity meters as well.
Radiotherapy Dosimetry: A Review on Open-Source Optimizer
Authors: Paul Dubois
Date: 2023-05-29 00:00:00

Radiotherapy dosimetry plays a crucial role in optimizing treatment plans for cancer patients. In this study, we investigate the performance of a dozen standard state-of-the-art open-source optimizers for radiotherapy dosimetry. Our evaluation includes the use of TGG119 benchmark cases as well as one real case obtained from the Institute du Cancer de Montpellier (ICM). Among the tested optimizers, Newton CG demonstrates the fastest convergence in terms of the number of iterations. However, when considering the computation time per iteration, LBFGS emerges as the most efficient optimizer. These findings shed light on the performance of open-source optimizers for radiotherapy dosimetry, aiding practitioners in selecting suitable optimization tools for efficient treatment planning.
A Learning-based Nonlinear Model Predictive Controller for a Real Go-Kart based on Black-box Dynamics Modeling through Gaussian Processes
Authors: Enrico Picotti, Enrico Mion, Alberto Dalla Libera, Josip Pavlovic, Andrea Censi, Emilio Frazzoli, Alessandro Beghi, Mattia Bruschetta
Date: 2023-05-29 00:00:00
Tasks: Autonomous Driving, Gaussian Processes, Model Predictive Control

Lately, Nonlinear Model Predictive Control (NMPC)has been successfully applied to (semi-) autonomous driving problems and has proven to be a very promising technique. However, accurate control models for real vehicles could require costly and time-demanding specific measurements. To address this problem, the exploitation of system data to complement or derive the prediction model of the NMPC has been explored, employing learning dynamics approaches within Learning-based NMPC (LbNMPC). Its application to the automotive field has focused on discrete grey-box modeling, in which a nominal dynamics model is enhanced by the data-driven component. In this manuscript, we present an LbNMPC controller for a real go-kart based on a continuous black-box model of the accelerations obtained by Gaussian Processes. We show the effectiveness of the proposed approach by testing the controller on a real go-kart vehicle, highlighting the approximation steps required to get an exploitable GP model on a real-time application.
Robust Queue Length Estimation for Ramp Metering in a Connected Vehicle Environment
Authors: Yu Tang, Kaan Ozbay, Li Jin
Date: 2023-05-29 00:00:00
Tasks: State Estimation

Connected vehicles (CVs) can provide numerous new data via vehicle-to-vehicle or vehicle-to-infrastructure communication. These data can in turn be used to facilitate real-time traffic state estimation. In this paper, we focus on ramp queue length estimation in a connected vehicle environment, which improves control design and implementation of ramp metering algorithms. One major challenge of the estimation problem is that the CV data only represent partial traffic observations and could introduce new uncertainties if real-time CV penetration rates are unknown. To address this, we build our estimation approach on both traditional freeway sensors and new CV data. We first formulate a ramp queue model that considers i) variations in the penetration rate and ii) noise in measurements. Then we develop a robust filter that minimizes the impacts of these two kinds of uncertainties on queue estimation. More importantly, we show that the designed filter has guaranteed long-term estimation accuracy. It allows us to quantify in a theoretical way the relationship between estimation error and fluctuation of CV penetration rates. We also provide a series of simulation results to verify our approach.
Maximizing Safety and Efficiency for Cooperative Lane-Changing: A Minimally Disruptive Approach
Authors: Andres S. Chavez Armijos, Anni Li, Christos G. Cassandras
Date: 2023-05-29 00:00:00

This paper addresses cooperative lane-changing maneuvers in mixed traffic, aiming to minimize traffic flow disruptions while accounting for uncooperative vehicles. The proposed approach adopts controllers combining Optimal control with Control Barrier Functions (OCBF controllers) which guarantee spatio-temporal constraints through the use of fixed-time convergence. Additionally, we introduce robustness to disturbances by deriving a method for handling worst-case disturbances using the dual of a linear programming problem. We present a near-optimal solution that ensures safety, optimality, and robustness to changing behavior of uncooperative vehicles. Simulations demonstrate the effectiveness of the proposed approach in enhancing efficiency and safety.
An Efficient Safety-oriented Car-following Model for Connected Automated Vehicles Considering Discrete Signals
Authors: DianChao Lin, Li Li
Date: 2023-05-29 00:00:00
Tasks: Decision Making

With the rapid development of Connected and Automated Vehicle (CAV) technology, limited self-driving vehicles have been commercially available in certain leading intelligent transportation system countries. When formulating the car-following model for CAVs, safety is usually the basic constraint. Safety-oriented car-following models seek to specify a safe following distance that can guarantee safety if the preceding vehicle were to brake hard suddenly. The discrete signals of CAVs bring a series of phenomena, including discrete decision-making, phase difference, and discretely distributed communication delay. The influences of these phenomena on the car-following safety of CAVs are rarely considered in the literature. This paper proposes an efficient safety-oriented car-following model for CAVs considering the impact of discrete signals. The safety constraints during both normal driving and a sudden hard brake are incorporated into one integrated model to eliminate possible collisions during the whole driving process. The mechanical delay information of the preceding vehicle is used to improve car-following efficiency. Four modules are designed to enhance driving comfort and string stability in case of heavy packet losses. Simulations of a platoon with diversified vehicle types demonstrate the safety, efficiency, and string stability of the proposed model. Tests with different packet loss rates imply that the model could guarantee safety and driving comfort in even poor communication environments.
An Approximate Feasibility Assessment of Electric Vehicles Adoption in Nigeria: Forecast 2030
Authors: Qasim Ajao, Lanre Sadeeq
Date: 2023-05-29 00:00:00

Efforts toward building a sustainable future have underscored the importance of collective responsibility among state and non-state actors, corporations, and individuals to achieve climate goals. International initiatives, including the Sustainable Development Goals and the Paris Agreement, emphasize the need for immediate action from all stakeholders. This paper presents a feasibility assessment focused on the opportunities within Nigeria's Electric Vehicle Value Chain, aiming to enhance public understanding of the country's renewable energy sector. As petroleum currently fulfills over 95% of global transportation needs, energy companies must diversify their portfolios and integrate various renewable energy sources to transition toward a sustainable future. The shifting investor sentiment away from traditional fossil fuel industries further highlights the imperative of incorporating renewables. To facilitate significant progress in the renewable energy sector, it is vital to establish platforms that support the growth and diversification of industry players, with knowledge sharing playing a pivotal role. This feasibility assessment serves as an initial reference for individuals and businesses seeking technically and economically viable opportunities within the sector.
Large Banks and Systemic Risk: Insights from a Mean-Field Game Model
Authors: Yuanyuan Chang, Dena Firoozi, David Benatia
Date: 2023-05-28 00:00:00
Tasks: Management

This paper presents a dynamic game framework to analyze the role of large banks in interbank markets. By extending existing models, we incorporate a large bank as a dynamic decision-maker interacting with multiple small banks. Using the mean-field game methodology and convex analysis, best-response trading strategies are derived, leading to an approximate equilibrium for the interbank market. We investigate the influence of the large bank on the market stability by examining individual default probabilities and systemic risk, through the use of Monte Carlo simulations. Our findings reveal that, when the size of the major bank is not excessively large, it can positively contribute to market stability. However, there is also the potential for negative spillover effects in the event of default, leading to an increase in systemic risk. The magnitude of this impact is further influenced by the size and trading rate of the major bank. Overall, this study provides valuable insights into the management of systemic risk in interbank markets.
Systems Development of a Two-Axis Stabilised Platform to Facilitate Astronomical Observations from a Moving Base
Authors: James H Hepworth, Hendrik D Mouton
Date: 2023-05-28 00:00:00

This project aimed to design, simulate, and implement a two-axis inertially stabilised platform (ISP) for use in astronomical applications. It aimed to approximate the stabilisation of a Meade ETX-90 3.5" compound telescope at low-cost using a mechanical assembly designed to geometrically and inertially model the telescope. A set of system specifications was developed to guide design decisions and to provide an analysis framework against which the performance of the implemented system was compared. The electro-mechanical structure of the ISP was designed and manufactured, the associated electrical systems were specified and configured, an image processing script capable of detecting and locating the centre of the Moon in a camera field-of-view was written, a complete simulation model for the system was developed and used to design various classical controllers for the ISP control system. These controllers were implemented on a STM32F051 microcontroller and a user interface was written in LabVIEW to facilitate intuitive user control of the system and perform datalogging of the system runtime data.
New Cycle-based Formulation, Cost Function, and Heuristics for DC OPF Based Controlled Islanding
Authors: Ilya Tyuryukanov, Marjan Popov, Jorrit A. Bos, Mart A. M. M. van der Meijden, Vladimir Terzija
Date: 2023-05-28 00:00:00

This paper presents a new formulation for intentional controlled islanding (ICI) of power transmission grids based on mixed-integer linear programming (MILP) DC optimal power flow (OPF) model. We highlight several deficiencies of the most well-known formulation for this problem and propose new enhancements for their improvement. In particular, we propose a new alternative optimization objective that may be more suitable for ICI than the minimization of load shedding, a new set of island connectivity constraints, and a new set of constraints for DC OPF with switching, and a new MILP heuristic to find initial feasible solutions for ICI. It is shown that the proposed improvements help to reduce the final optimality gaps as compared to the benchmark model on several test instances.
Fully Automatic Gym Exercises Recording: An IoT Solution
Authors: Sizhen Bian, Alexander Rupp, Michele Magno
Date: 2023-05-27 00:00:00

In recent years, working out in the gym has gotten increasingly more data-focused and many gym enthusiasts are recording their exercises to have a better overview of their historical gym activities and to make a better exercise plan for the future. As a side effect, this recording process has led to a lot of time spent painstakingly operating these apps by plugging in used types of equipment and repetitions. This project aims to automate this process using an Internet of Things (IoT) approach. Specifically, beacons with embedded ultra-low-power inertial measurement units (IMUs) are attached to the types of equipment to recognize the usage and transmit the information to gym-goers and managers. We have created a small ecosystem composed of beacons, a gateway, smartwatches, android/iPhone applications, a firebase cloud server, and a dashboard, all communicating over a mixture of Bluetooth and Wifi to distribute collected data from machines to users and gym managers in a compact and meaningful way. The system we have implemented is a working prototype of a bigger end goal and is supposed to initialize progress toward a smarter, more efficient, and still privacy-respect gym environment in the future. A small-scale real-life test shows 94.6\% accuracy in user gym session recording, which can reach up to 100\% easily with a more suitable assembling of the beacons. This promising result shows the potential of a fully automatic exercise recording system, which enables comprehensive monitoring and analysis of the exercise sessions and frees the user from manual recording. The estimated battery life of the beacon is 400 days with a 210 mAh coin battery. We also discussed the shortcoming of the current demonstration system and the future work for a reliable and ready-to-deploy automatic gym workout recording system.
Mobile Safety Application for Pedestrians
Authors: Sukru Yaren Gelbal, Mustafa Ridvan Cantas, Bilin Aksun Guvenc, Levent Guvenc, Gopichandra Surnilla, Hao Zhang
Date: 2023-05-27 00:00:00

Vulnerable Road User (VRU) safety has been an important issue throughout the years as corresponding fatality numbers in traffic have been increasing each year. With the developments in connected vehicle technology, there are new and easier ways of implementing Vehicle to Everything (V2X) communication which can be utilized to provide safety and early warning benefits for VRUs. Mobile phones are one important point of interest with their sensors being increased in quantity and quality and improved in terms of accuracy. Bluetooth and extended Bluetooth technology in mobile phones has enhanced support to carry larger chunks of information to longer distances. The work we discuss in this paper is related to a mobile application that utilizes the mobile phone sensors and Bluetooth communication to implement Personal Safety Message (PSM) broadcast using the SAE J2735 standard to create a Pedestrian to Vehicle (P2V) based safety warning structure. This implementation allows the drivers to receive a warning on their mobile phones and be more careful about the pedestrian intending to cross the street. As a result, the driver has much more time to safely slow down and stop at the intersection. Most importantly, thanks to the wireless nature of Bluetooth connection and long-range mode in Bluetooth 5.0, most dangerous cases such as reduced visibility or No-Line-of-Sight (NLOS) conditions can be remedied.
Resilience in Platoons of Cooperative Heterogeneous Vehicles: Self-organization Strategies and Provably-correct Design
Authors: Di Liu, Sebastian Mair, Kang Yang, Simone Baldi, Paolo Frasca, Matthias Althoff
Date: 2023-05-27 00:00:00

This work proposes provably-correct self-organizing strategies for platoons of heterogeneous vehicles. We refer to self-organization as the capability of a platoon to autonomously homogenize to a common group behavior. We show that self-organization promotes resilience to acceleration limits and communication failures, i.e., homogenizing to a common group behavior makes the platoon recover from these causes of impairments. In the presence of acceleration limits, resilience is achieved by self-organizing to a common constrained group behavior that prevents the vehicles from hitting their acceleration limits. In the presence of communication failures, resilience is achieved by self-organizing to a common group observer to estimate the missing information. Stability of the self-organization mechanism is studied analytically, and correctness with respect to traffic actions (e.g. emergency braking, cut-in, merging) is realized through a provably-correct safety layer. Numerical validations via the platooning toolbox OpenCDA in CARLA and via the CommonRoad platform confirm improved performance through self-organization and the provably-correct safety layer.
Hierarchical Game for Coupled Power System with Energy Sharing and Transportation System
Authors: Dongxiang Yan, Tongxin Li, Changhong Zhao, Han Wang, Yue Chen
Date: 2023-05-27 00:00:00

The wide deployment of distributed renewable energy sources and electric vehicles can help mitigate climate crisis. This necessitates new business models in the power sector to hedge against uncertainties while imposing a strong coupling between the connected power and transportation networks. To address these challenges, this paper first proposes an energy sharing mechanism considering AC power network constraints to encourage local energy exchange in the power system. Under the proposed mechanism, all prosumers play a generalized Nash game. We prove that the energy sharing equilibrium exists and is socially optimal. Furthermore, a hierarchical game is built to characterize the interactions both inside and between the power and transportation systems. Externally, the two systems are engaged in a generalized Nash game because traffic flows serve as electric demands by charging behaviors, and each driver pays the energy sharing price for charging. The hierarchical game is then converted into a mixed-integer linear program (MILP) with the help of optimality conditions and linearization techniques. Numerical experiments validate the theoretical results and show the mutual impact between the two systems.
Reliability Evaluation of Phasor Measurement Unit Considering Failure of Hardware and Software Using Fuzzy Approach
Authors: Evan Carollo, Zikai Xu
Date: 2023-05-26 00:00:00

The wide-area measurement system (WAMS) consists of the future power system, increasing geographical sprawl which is linked by the Phasor measurement unit(PMU). Thus, the failure of PMU will cause severe results, such as a blackout of the power system. In this paper, the reliability model of PMU is considered both hardware and software, where it gives a characteristic of correlated failure of hardware and software. Markov process is applied to model PMU, and reliability parameters are given by using symmetrical triangular membership for Type-1 fuzzy reliability analysis. The paper gives insightful results revealing the effective approach for analyzing the reliability of PMU, under a circumstance which lack of sufficient field data.
Image Quality Is Not All You Want: Task-Driven Lens Design for Image Classification
Authors: Xinge Yang, Qiang Fu, Yunfeng Nie, Wolfgang Heidrich
Date: 2023-05-26 00:00:00
Tasks: All, image-classification, Image Classification

In computer vision, it has long been taken for granted that high-quality images obtained through well-designed camera lenses would lead to superior results. However, we find that this common perception is not a "one-size-fits-all" solution for diverse computer vision tasks. We demonstrate that task-driven and deep-learned simple optics can actually deliver better visual task performance. The Task-Driven lens design approach, which relies solely on a well-trained network model for supervision, is proven to be capable of designing lenses from scratch. Experimental results demonstrate the designed image classification lens (``TaskLens'') exhibits higher accuracy compared to conventional imaging-driven lenses, even with fewer lens elements. Furthermore, we show that our TaskLens is compatible with various network models while maintaining enhanced classification accuracy. We propose that TaskLens holds significant potential, particularly when physical dimensions and cost are severely constrained.
Nonlinear Impulse Response Functions and Local Projections
Authors: Christian Gourieroux, Quinlan Lee
Date: 2023-05-29 00:00:00

The goal of this paper is to extend the method of estimating Impluse Response Functions (IRFs) by means of Local Projection (LP) in a nonlinear dynamic framework. We discuss the existence of a nonlinear autoregressive representation for a Markov process, and explain how their Impulse Response Functions are directly linked to the nonlinear Local Projection, as in the case for the linear setting. We then present a nonparametric LP estimator, and compare its asymptotic properties to that of IRFs obtained through direct estimation. We also explore issues of identification for the nonlinear IRF in the multivariate framework, which remarkably differs in comparison to the Gaussian linear case. In particular, we show that identification is conditional on the uniqueness of deconvolution. Then, we consider IRF and LP in augmented Markov models.