pwc-archive/papers-with-abstracts huggingface dataset:
Predictive Vehicle Repositioning for On-Demand Ride-Pooling Services
Authors: Roman Engelhardt, Hani S. Mahmassani, Klaus Bogenberger
Date: 2023-08-10 00:00:00

On-Demand Ride-Pooling services have the potential to increase traffic efficiency compared to private vehicle trips by decreasing parking space needed and increasing vehicle occupancy due to higher vehicle utilization and shared trips, respectively. Thereby, an operator controls a fleet of vehicles that serve requested trips on-demand while trips can be shared. In this highly dynamic and stochastic setting, asymmetric spatio-temporal request distributions can drive the system towards an imbalance between demand and supply when vehicles end up in regions with low demand. This imbalance would lead to low fleet utilization and high customer waiting times. This study proposes a novel rebalancing algorithm to predictively reposition idle fleet vehicles to reduce this imbalance. The algorithm first samples artificial requests from a predicted demand distribution and simulates future fleet states to identify supply shortages. An assignment problem is formulated that assigns repositioning trips considering multiple samples and forecast horizons. The algorithm is implemented in an agent-based simulation framework and compared to multiple state-of-the-art rebalancing algorithms. A case study for Chicago, Illinois shows the benefits of applying the repositioning strategy by increasing service rate and vehicle revenue hours by roughly 50% compared to a service without repositioning. It additionally outperforms all comparison algorithms by serving more customers, increasing the pooling efficiency and decreasing customer waiting time regardless of the forecasting method applied. As a trade-off, the computational time increases, but with a termination within a couple of seconds it is still applicable for large-scale real world instances.
Bayesian Meta-Learning on Control Barrier Functions with Data from On-Board Sensors
Authors: Wataru Hashimoto, Kazumune Hashimoto, Akifumi Wachi, Xun Shen, Masako Kishida, Shigemasa Takai
Date: 2023-08-10 00:00:00
Tasks: Meta-Learning, Navigate

In this paper, we consider a way to safely navigate the robots in unknown environments using measurement data from sensory devices. The control barrier function (CBF) is one of the promising approaches to encode safety requirements of the system and the recent progress on learning-based approaches for CBF realizes online synthesis of CBF-based safe controllers with sensor measurements. However, the existing methods are inefficient in the sense that the trained CBF cannot be generalized to different environments and the re-synthesis of the controller is necessary when changes in the environment occur. Thus, this paper considers a way to learn CBF that can quickly adapt to a new environment with few amount of data by utilizing the currently developed Bayesian meta-learning framework. The proposed scheme realizes efficient online synthesis of the controller as shown in the simulation study and provides probabilistic safety guarantees on the resulting controller.
Output-feedback adaptive model predictive control for ramp metering: a set-membership approach
Authors: Zhexian Li, Ketan Savla
Date: 2023-08-10 00:00:00
Tasks: Model Predictive Control, State Estimation

Ramp metering, which regulates the flow entering the freeway, is one of the most effective freeway traffic control methods. This paper introduces an output-feedback adaptive approach to ramp metering that combines model predictive control (MPC) with set-membership parameter and state estimation. The set-membership estimator is based on a mixed-monotone embedding of underlying traffic dynamics. The embedding is also used as the modeling basis for MPC optimization. For a freeway stretch with unknown parameters and partial measurement on the freeway mainline, we provide sufficient conditions on the control horizon, cost functions, terminal sets of MPC, and inflow demand at the ramps such that the queue lengths in the closed-loop system remain bounded. The sufficient condition on the demand matches the necessary condition, thereby proving maximal throughput under the proposed controller. The result is strengthened to input-to-state stability when model parameters and demand are known. The stability analysis is conducted for the case of constant demand and unbounded on-ramps. The closed-loop trajectory data generated by the proposed controller is shown to facilitate finite time estimation of free-flow model parameters, i.e., free-flow speed and turning ratios. Simulation results illustrate stability of the closed-loop system under the proposed controller with time-varying demand and few mainline measurements, for which the system becomes unstable under a well-known approach from the literature. This indicates that the proposed controller renders higher throughput than the well-known approach, possibly using more computing resources.
An Electrical Grid with Discrete Energy Levels
Authors: H. Grebel
Date: 2023-08-09 00:00:00

Minimizing both power fluctuations and energy waste in an electrical grid is a central challenge to energy policy. Any discrepancy between power production and loads may lead to inefficiencies and instability in the system. Right now, the electrical grid is an analog system that only retroactively reacts to power demands. The balancing act becomes even harder with the penetration of sustainable resources (e.g., wind turbines). Here, we consider the effect of random perturbations to the grid's steady states operation. A model is constructed and analyzed within the framework of a randomly perturbed Markovian chain. Instead of balancing continuous values for supply and demand, the model assumes that both the generators and the users adhere to discrete pattern energy levels which is supplemented by local, short-term energy storage units (STESU). Under reasonable assumptions, we show that this grid maintains stability (meaning, a constant difference between supply and demand) over long periods of time while subjected to randomly fluctuating energy conditions.
AlgDiff: An open source toolbox for the design, analysis and discretisation of algebraic differentiators
Authors: Amine Othmane, Joachim Rudolph
Date: 2023-08-09 00:00:00
Tasks: Fault Diagnosis

Algebraic differentiators have attracted much interest in recent years. Their simple implementation as classical finite impulse response digital filters and systematic tuning guidelines may help to solve challenging problems, including, but not limited to, nonlinear feedback control, model-free control, and fault diagnosis. This contribution introduces the open source toolbox AlgDiff for the design, analysis and discretisation of algebraic differentiators.
Aggregated demand flexibility prediction of residential thermostatically controlled loads and participation in electricity balance markets
Authors: Alejandro Martín-Crespo, Enrique Baeyens, Sergio Saludes-Rodil, Fernando Frechoso-Escudero
Date: 2023-08-09 00:00:00

The aggregate demand flexibility of a set of thermostatically controlled residential loads (TCLs) can be represented by a virtual battery (VB) in order to manage their participation in the electricity markets. For this purpose, it is necessary to know in advance and with a high level of reliability the maximum power that can be supplied by the aggregation of TCLs. A probability function of the power that can be supplied by a VB is introduced. This probability function is used to predict the demand flexibility using a new experimental probabilistic method based on a combination of Monte Carlo simulation and extremum search by bisection algorithm (MC&ESB). As a result, the maximum flexibility power that a VB can provide with a certain guaranteed probability is obtained. The performance and validity of the proposed method are demonstrated in three different case studies where a VB bids its aggregate power in the Spanish electricity balancing markets (SEBM).
A High-efficient Battery Charging System for Electric Vehicle
Authors: Zexuan Li
Date: 2023-08-09 00:00:00

Nowadays, automobile is facing the trend of electrification. Lithium-ion batteries is widely used as their power supplies. Lithium-ion battery has complex characteristics, as a result, Lithium-ion battery needs optimal charging strategies to make sure it is charged safely and efficiently. This paper focuses on development of a high-efficient charging method for lithium-ion battery. To test different charging strategies, the electric vehicle charging system consisting of a dual active bridge DC-DC converter and a Thevenin battery model is implemented. Multistage constant current charging (MSCC) and multistage constant current reflex charging (MSCC with reflex charging) were proposed. Compared with the traditional constant voltage constant current (CC-CV) charging method, MSCC can reduce 12% of the charging time and 1.1% of the battery loss; MSCC with reflex charging has a 10.45% and a 1.54% reduction of charging time and battery loss separately.
Fully Decentralized Peer-to-Peer Community Grid with Dynamic and Congestion Pricing
Authors: Hien Thanh Doan, Truong Hoang Bao Huy, Daehee Kim, HongSeok Kim
Date: 2023-08-09 00:00:00

Peer-to-peer (P2P) electricity markets enable prosumers to minimize their costs, which has been extensively studied in recent research. However, there are several challenges with P2P trading when physical network constraints are also included. Moreover, most studies use fixed prices for grid power prices without considering dynamic grid pricing, and equity for all participants. This policy may negatively affect the long-term development of the market if prosumers with low demand are not treated fairly. An initial step towards addressing these problems is the design of a new decentralized P2P electricity market with two dynamic grid pricing schemes that are determined by consumer demand. Futhermore, we consider a decentralized system with physical constraints for optimizing power flow in networks without compromising privacy. We propose a dynamic congestion price to effectively address congestion and then prove the convergence and global optimality of the proposed method. Our experiments show that P2P energy trade decreases generation cost of main grid by 56.9% compared with previous works. Consumers reduce grid trading by 57.3% while the social welfare of consumers is barely affected by the increase of grid price.
Real-time FPGA Implementation of CNN-based Distributed Fiber Optic Vibration Event Recognition Method
Authors: Zhongyao Luo, Zhao Ge, Hao Wu, Ming Tang
Date: 2023-08-09 00:00:00
Tasks: CPU, Edge-computing, GPU

Utilizing optical fibers to detect and pinpoint vibrations, Distributed Optical Fiber Vibration Sensing (DVS) technology provides real-time monitoring and surveillance of wide-reaching areas. This field has been leveraging Convolutional Neural Networks (CNN). Recently, a study has accomplished end-to-end vibration event recognition, enabling utilization of CNN-based DVS algorithms as real-time embedded system for edge computing in practical application situations. Considering the power consumption of central processing unit (CPU) and graphics processing unit (GPU), and the inflexibility of application-specific integrated circuit (ASIC), field-Programmable gate array (FPGA) is the optimal computing platform for the system. This paper proposes to compress pre-trained network and adopt a novel hardware structure, to design a fully on-chip, pipelined inference accelerator for CNN-based DVS algorithm, without fine tuning or re-training. This design allows for real-time processing with low power consumption and system requirement.An examination has been executed on an existing DVS algorithm based on a 40-layer CNN model comprising 2.7 million parameters. It is completely implemented on-chip, pipelined, with no reduction in accuracy.
An Approach for Optimizing Acceleration in Connected and Automated Vehicles
Authors: Filippos N. Tzortzoglou, Dionysios Theodosis, Andreas Malikopoulos
Date: 2023-08-08 00:00:00

Vehicle automation technology has made significant progress, laying the groundwork for a future of fully automated vehicles. This paper delves into the operation of connected and automated vehicles (CAVs). In prior work, we developed a controller that includes a tunable gain whose value significantly influences CAV performance and, in particular, its acceleration. By varying this gain, CAV acceleration is associated with different values depending on some initial conditions. Thus, our goal in this paper is to identify the optimal value of this gain in terms of acceleration for any group of initial conditions. To this end, we formulate an optimization problem where the decision variable is the gain value, and the objective function includes the acceleration of the vehicles. The complexity of this problem prohibits real-time solutions. To address this challenge, we train a neural network to map different initial conditions to the optimal gain values efficiently. We showcase the proposed approach to deriving the optimal gains in a merging scenario with an on-ramp.
Generalized Power Iteration with Application to Distributed Connectivity Estimation of Asymmetric Networks
Authors: M. Mehdi Asadi, Mohammad Khosravi, Hesam Mosalli, Stephane Blouin, Amir G. Aghdam
Date: 2023-08-08 00:00:00
Tasks: Connectivity Estimation

The problem of connectivity assessment in an asymmetric network represented by a weighted directed graph is investigated in this article. A power iteration algorithm in a centralized implementation is developed first to compute the generalized algebraic connectivity of asymmetric networks. After properly transforming the Laplacian matrix of the network, two sequences of one-dimensional and two-dimensional subspaces are generated iteratively, one of which converges to the desired subspace spanned by the eigenvector(s) associated with the eigenvalue(s) representing the network's generalized algebraic connectivity. A distributed implementation of the proposed power iteration algorithm is then developed to compute the generalized algebraic connectivity from the viewpoint of each node, which is scalable to any asymmetric network of any size with a fixed message length per node. The convergence analysis of these algorithms is subsequently provided under some weak assumptions. The efficiency of the developed algorithms in computing the network connectivity is then demonstrated by simulations.
Universal scaling relation and criticality in metabolism and growth of Escherichia coli
Authors: Shaohua Guan, Zhichao Zhang, Zihan Zhang, Hualin Shi
Date: 2023-08-09 00:00:00
Tasks: Relation

The metabolic network plays a crucial role in regulating bacterial metabolism and growth, but it is subject to inherent molecular stochasticity. Previous studies have utilized flux balance analysis and the maximum entropy method to predict metabolic fluxes and growth rates, while the underlying principles governing bacterial metabolism and growth, especially the criticality hypothesis, remain unclear. In this study, we employ a maximum entropy approach to investigate the universality in various constraint-based metabolic networks of Escherichia coli. Our findings reveal the existence of universal scaling relations across different nutritional environments and metabolic network models, similar to the universality observed in physics. By analyzing single-cell data, we confirm that metabolism of Escherichia coli operates close to the state with maximum Fisher information, which serves as a signature of criticality. This critical state provides functional advantages such as high sensitivity and long-range correlation. Moreover, we demonstrate that a metabolic system operating at criticality takes a compromise solution between growth and adaptation, thereby serving as a survival strategy in fluctuating environments.
A low-cost, user-friendly rheo-optical compression assay to measure mechanical properties of cell spheroids in standard cell culture plates
Authors: Rosalia Ferraro, Sergio Caserta, Stefano Guido
Date: 2023-08-09 00:00:00

The mechanical characterization of cell spheroids, one of the most widely used 3D biology models in vitro, is a hotspot of current research on the role played by the mechanical response of cells and tissues. The techniques proposed so far in the literature, while providing important scientific insights, require a specialized equipment and technical skills which are not usually available in cell biology facilities. Here, we present an innovative rheo-optical compression assay based on microscopy glass coverslips as the load applied to cell spheroids in standard cell culture plates and on image acquisition with an optical microscope or even a smartphone equipped with adequate magnification lenses. Mechanical properties can be simply obtained by correlating the applied load to the deformation of cell spheroids measured by image analysis. The low-cost, user-friendly features of the proposed technique can boost mechanobiology research making it easily affordable to any biomedical lab equipped with cell culture facilities.
High-Accuracy Prediction of Metal-Insulator-Metal Metasurface with Deep Learning
Authors: Kaizhu Liu, Hsiang-Chen Chui, Changsen Sun, Xue Han
Date: 2023-08-04 00:00:00
Tasks: Prediction

Deep learning prediction of electromagnetic software calculation results has been a widely discussed issue in recent years. But the prediction accuracy was still one of the challenges to be solved. In this work, we proposed that the ResNets-10 model was used for predicting plasmonic metasurface S11 parameters. The two-stage training was performed by the k-fold cross-validation and small learning rate. After the training was completed, the prediction loss for aluminum, gold, and silver metal-insulator-metal metasurfaces was -48.45, -46.47, and -35.54, respectively. Due to the ultralow error value, the proposed network can replace the traditional electromagnetic computing method for calculation within a certain structural range. Besides, this network can finish the training process less than 1,100 epochs. This means that the network training process can effectively lower the design process time. The ResNets-10 model we proposed can also be used to design meta-diffractive devices and biosensors, thereby reducing the time required for the calculation process. The ultralow error of the network indicates that this work contributes to the development of future artificial intelligence electromagnetic computing software.
HSD-PAM: High Speed Super Resolution Deep Penetration Photoacoustic Microscopy Imaging Boosted by Dual Branch Fusion Network
Authors: Zhengyuan Zhang, Haoran Jin, Zesheng Zheng, Wenwen Zhang, Wenhao Lu, Feng Qin, Arunima Sharma, Manojit Pramanik, Yuanjin Zheng
Date: 2023-08-09 00:00:00
Tasks: Super-Resolution

Photoacoustic microscopy (PAM) is a novel implementation of photoacoustic imaging (PAI) for visualizing the 3D bio-structure, which is realized by raster scanning of the tissue. However, as three involved critical imaging parameters, imaging speed, lateral resolution, and penetration depth have mutual effect to one the other. The improvement of one parameter results in the degradation of other two parameters, which constrains the overall performance of the PAM system. Here, we propose to break these limitations by hardware and software co-design. Starting with low lateral resolution, low sampling rate AR-PAM imaging which possesses the deep penetration capability, we aim to enhance the lateral resolution and up sampling the images, so that high speed, super resolution, and deep penetration for the PAM system (HSD-PAM) can be achieved. Data-driven based algorithm is a promising approach to solve this issue, thereby a dedicated novel dual branch fusion network is proposed, which includes a high resolution branch and a high speed branch. Since the availability of switchable AR-OR-PAM imaging system, the corresponding low resolution, undersample AR-PAM and high resolution, full sampled OR-PAM image pairs are utilized for training the network. Extensive simulation and in vivo experiments have been conducted to validate the trained model, enhancement results have proved the proposed algorithm achieved the best perceptual and quantitative image quality. As a result, the imaging speed is increased 16 times and the imaging lateral resolution is improved 5 times, while the deep penetration merit of AR-PAM modality is still reserved.
An automated pipeline for quantitative T2* fetal body MRI and segmentation at low field
Authors: Kelly Payette, Alena Uus, Jordina Aviles Verdera, Carla Avena Zampieri, Megan Hall, Lisa Story, Maria Deprez, Mary A. Rutherford, Joseph V. Hajnal, Sebastien Ourselin, Raphael Tomi-Tricot, Jana Hutter
Date: 2023-08-09 00:00:00
Tasks: Quantitative MRI

Fetal Magnetic Resonance Imaging at low field strengths is emerging as an exciting direction in perinatal health. Clinical low field (0.55T) scanners are beneficial for fetal imaging due to their reduced susceptibility-induced artefacts, increased T2* values, and wider bore (widening access for the increasingly obese pregnant population). However, the lack of standard automated image processing tools such as segmentation and reconstruction hampers wider clinical use. In this study, we introduce a semi-automatic pipeline using quantitative MRI for the fetal body at low field strength resulting in fast and detailed quantitative T2* relaxometry analysis of all major fetal body organs. Multi-echo dynamic sequences of the fetal body were acquired and reconstructed into a single high-resolution volume using deformable slice-to-volume reconstruction, generating both structural and quantitative T2* 3D volumes. A neural network trained using a semi-supervised approach was created to automatically segment these fetal body 3D volumes into ten different organs (resulting in dice values > 0.74 for 8 out of 10 organs). The T2* values revealed a strong relationship with GA in the lungs, liver, and kidney parenchyma (R^2>0.5). This pipeline was used successfully for a wide range of GAs (17-40 weeks), and is robust to motion artefacts. Low field fetal MRI can be used to perform advanced MRI analysis, and is a viable option for clinical scanning.
Physics-driven universal twin-image removal network for digital in-line holographic microscopy
Authors: Mikołaj Rogalski, Piotr Arcab, Luiza Stanaszek, Vicente Micó, Chao Zuo, Maciej Trusiak
Date: 2023-08-08 00:00:00

Digital in-line holographic microscopy (DIHM) enables efficient and cost-effective computational quantitative phase imaging with a large field of view, making it valuable for studying cell motility, migration, and bio-microfluidics. However, the quality of DIHM reconstructions is compromised by twin-image noise, posing a significant challenge. Conventional methods for mitigating this noise involve complex hardware setups or time-consuming algorithms with often limited effectiveness. In this work, we propose UTIRnet, a deep learning solution for fast, robust, and universally applicable twin-image suppression, trained exclusively on numerically generated datasets. The availability of open-source UTIRnet codes facilitates its implementation in various DIHM systems without the need for extensive experimental training data. Notably, our network ensures the consistency of reconstruction results with input holograms, imparting a physics-based foundation and enhancing reliability compared to conventional deep learning approaches. Experimental verification was conducted among others on live neural glial cell culture migration sensing, which is crucial for neurodegenerative disease research.