pwc-archive/papers-with-abstracts huggingface dataset:
A Synthetic Texas Power System with Time-Series Weather-Dependent Spatiotemporal Profiles
Authors: Jin Lu, Xingpeng Li, Hongyi Li, Taher Chegini, Carlos Gamarra, Y. C. Ethan Yang, Margaret Cook, Gavin Dillingham
Date: 2023-02-26 00:00:00
Tasks: Time Series, Time Series Analysis

We developed a synthetic Texas 123-bus backbone transmission system (TX-123BT) with spatio-temporally correlated grid profiles of solar power, wind power, dynamic line ratings and loads at one-hour resolution for five continuous years, which demonstrates unique advantages compared to conventional test cases that offer single static system profile snapshots. Three weather-dependent models are used to create the hourly wind power productions, solar power productions, and dynamic line ratings respectively. The actual historical weather information is also provided along with this dataset, which is suitable for machine learning models. Security-constrained unit commitment is conducted on TX-123BT daily grid profiles and numerical results are compared with the actual Texas system for validation. The created hourly DLR profiles can cut operating cost from USD 8.09 M to USD 7.95 M (-1.7 %), raises renewable dispatch by 1.3 %, and lowers average LMPs from USD 18.66 to USD 17.98 /MWh (-3.6 %). Two hydrogen options -- a 200 MW dual hub and a 500 MW hydrogen-energy transmission and conversion system -- reduce high-load Q3 daily costs by 13.9 % and 14.1 %, respectively. Sensitivity tests show that suppressing the high-resolution weather-driven profiles can push system cost up by as much as 15 %, demonstrating the economic weight of temporal detail.
Visual Privacy: Current and Emerging Regulations Around Unconsented Video Analytics in Retail
Authors: Scott Pletcher
Date: 2023-02-24 00:00:00

Video analytics is the practice of combining digital video data with machine learning models to infer various characteristics from that video. This capability has been used for years to detect objects, movement, and the number of customers in physical retail stores, but more complex machine learning models combined with more powerful computing power has unlocked new levels of possibility. Researchers claim it is now possible to infer a whole host of characteristics about an individual using video analytics, such as specific age, ethnicity, health status and emotional state. Moreover, an individuals visual identity can be augmented with information from other data providers to build out a detailed profile, all with the individual unknowingly contributing their physical presence in front of a retail store camera. Some retailers have begun to experiment with this new technology as a way to better know their customers. However, those same early adopters are caught in an evolving legal landscape around privacy and data ownership. This research looks into the current legal landscape and legislation currently in progress around the use of video analytics, specifically in the retail store setting. Because the ethical and legal norms around individualized video analytics are still heavily in flux, retailers are urged to adopt a wait and see approach or potentially incur costly legal expenses and risk damage to their brand.
Automatic Classification of Symmetry of Hemithoraces in Canine and Feline Radiographs
Authors: Peyman Tahghighi, Nicole Norena, Eran Ukwatta, Ryan B Appleby, Amin Komeili
Date: 2023-02-24 00:00:00
Tasks: Segmentation

Purpose: Thoracic radiographs are commonly used to evaluate patients with confirmed or suspected thoracic pathology. Proper patient positioning is more challenging in canine and feline radiography than in humans due to less patient cooperation and body shape variation. Improper patient positioning during radiograph acquisition has the potential to lead to a misdiagnosis. Asymmetrical hemithoraces are one of the indications of obliquity for which we propose an automatic classification method. Approach: We propose a hemithoraces segmentation method based on Convolutional Neural Networks (CNNs) and active contours. We utilized the U-Net model to segment the ribs and spine and then utilized active contours to find left and right hemithoraces. We then extracted features from the left and right hemithoraces to train an ensemble classifier which includes Support Vector Machine, Gradient Boosting and Multi-Layer Perceptron. Five-fold cross-validation was used, thorax segmentation was evaluated by Intersection over Union (IoU), and symmetry classification was evaluated using Precision, Recall, Area under Curve and F1 score. Results: Classification of symmetry for 900 radiographs reported an F1 score of 82.8% . To test the robustness of the proposed thorax segmentation method to underexposure and overexposure, we synthetically corrupted properly exposed radiographs and evaluated results using IoU. The results showed that the models IoU for underexposure and overexposure dropped by 2.1% and 1.2%, respectively. Conclusions: Our results indicate that the proposed thorax segmentation method is robust to poor exposure radiographs. The proposed thorax segmentation method can be applied to human radiography with minimal changes.
Improving Energy Management of Hybrid Electric Vehicles by Considering Battery Electric-Thermal Model
Authors: Arash Mousaei
Date: 2023-02-25 00:00:00
Tasks: energy management, Management

This article proposes an offline Energy Management System (EMS) for Parallel Hybrid Electric Vehicles (PHEVs). Dividing the torque between the Electric Motor (EM) and the Internal Combustion Engine (ICE) requires a suitable EMS. Batteries are vital to HEVs and significantly impact overall vehicle cost and performance. High temperature and high battery State of Charge (SOC) are the main factors that accelerate battery aging. SOC is the most critical state variable in EMS and was usually considered the only dynamic variable in previous studies. For simplicity, the battery temperature was often assumed to be constant, and the effect of EMS on temperature change was neglected. In this paper, we first apply Dynamic Programming (DP) to a PHEV without considering battery temperature variations. Then, the battery model is improved by modeling the cooling system to take into account temperature variations and show how neglecting the thermal dynamics of the battery in EMS is impractical. Finally, by integrating battery temperature as a state variable in the optimization problem, a new EMS is proposed to control battery temperature and SOC variation. Simulation results of the tested vehicle show that the proposed method controls battery charge and temperature. The proposed EMS method prevents uncontrolled fluctuations in battery temperature and reduces its deterioration rate.
On Bellman's principle of optimality and Reinforcement learning for safety-constrained Markov decision process
Authors: Rahul Misra, Rafał Wisniewski, Carsten Skovmose Kallesøe
Date: 2023-02-25 00:00:00
Tasks: Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Safe Reinforcement Learning

We study optimality for the safety-constrained Markov decision process which is the underlying framework for safe reinforcement learning. Specifically, we consider a constrained Markov decision process (with finite states and finite actions) where the goal of the decision maker is to reach a target set while avoiding an unsafe set(s) with certain probabilistic guarantees. Therefore the underlying Markov chain for any control policy will be multichain since by definition there exists a target set and an unsafe set. The decision maker also has to be optimal (with respect to a cost function) while navigating to the target set. This gives rise to a multi-objective optimization problem. We highlight the fact that Bellman's principle of optimality may not hold for constrained Markov decision problems with an underlying multichain structure (as shown by the counterexample due to Haviv. We resolve the counterexample by formulating the aforementioned multi-objective optimization problem as a zero-sum game and thereafter construct an asynchronous value iteration scheme for the Lagrangian (similar to Shapley's algorithm). Finally, we consider the reinforcement learning problem for the same and construct a modified $Q$-learning algorithm for learning the Lagrangian from data. We also provide a lower bound on the number of iterations required for learning the Lagrangian and corresponding error bounds.
3D Surface Reconstruction in the Wild by Deforming Shape Priors from Synthetic Data
Authors: Nicolai Häni, Jun-Jee Chao, Volkan Isler
Date: 2023-02-24 00:00:00
Tasks: 3D Reconstruction, Object, Pose Estimation, Surface Reconstruction

Reconstructing the underlying 3D surface of an object from a single image is a challenging problem that has received extensive attention from the computer vision community. Many learning-based approaches tackle this problem by learning a 3D shape prior from either ground truth 3D data or multi-view observations. To achieve state-of-the-art results, these methods assume that the objects are specified with respect to a fixed canonical coordinate frame, where instances of the same category are perfectly aligned. In this work, we present a new method for joint category-specific 3D reconstruction and object pose estimation from a single image. We show that one can leverage shape priors learned on purely synthetic 3D data together with a point cloud pose canonicalization method to achieve high-quality 3D reconstruction in the wild. Given a single depth image at test time, we first transform this partial point cloud into a learned canonical frame. Then, we use a neural deformation field to reconstruct the 3D surface of the object. Finally, we jointly optimize object pose and 3D shape to fit the partial depth observation. Our approach achieves state-of-the-art reconstruction performance across several real-world datasets, even when trained only on synthetic data. We further show that our method generalizes to different input modalities, from dense depth images to sparse and noisy LIDAR scans.
A Semi-Intelligence Algorithm For Charging Electric Vehicles
Authors: Arash Mousaei, Hesamodin allahyari
Date: 2023-02-25 00:00:00

The penetration of Electric Vehicles (EVs) in Distribution Networks (DNs) has many challenges for distribution companies, such as voltage drop, network losses, etc. To overcome these problems, it is recommended to use coordinated charging methods. However, these methods require high-cost telecommunication, measurement, and processing infrastructure and can only be implemented in smart grids. This article introduces a Semi-Smart Method (SSM) to charge EVs that does not require complex infrastructure. This method, using a simple and inexpensive local automation system, charges EVs during off-peak hours of the DN, thus improving its parameters. Since EVs are charged at low-traffic time intervals, the proposed method benefits EV owners. To get real results, 4- wire DN is considered to model the effect of the neutral wire. To confirm the effectiveness of the proposed method, it is compared to various uncontrolled loading methods. A standard 19-bus test system is used for simulations.
Hierarchical Fuel-Cell Airpath Control: an Efficiency-Aware MIMO Control Approach Combined with a Novel Constraint-Enforcing Reference Governor
Authors: Eli Bacher-Chong, Mostafa Ali Ayubirad, Zeng Qiu, Hao Wang, Alireza Goshtasbi, Hamid R. Ossareh
Date: 2023-02-25 00:00:00

This paper presents a hierarchical multivariable control and constraint management approach for an air supply system for a proton exchange membrane fuel cell (PEMFC) system. The control objectives are to track desired compressor mass airflow and cathode inlet pressure, maintain a minimum oxygen excess ratio (OER), and run the system at maximum net efficiency. A multi-input multi-output (MIMO) internal model controller (IMC) is designed and simulated to track flow and pressure set-points, which showed high performance despite strongly coupled plant dynamics. A new set-point map is generated to compute the most efficient cathode inlet pressure from the stack current load. To enforce OER constraints, a novel reference governor (RG) with the ability to govern multiple references (the cascade RG) and the ability to speed up as well as slow down a reference signal (the cross-section RG) is developed and tested. Compared with a single-input single-output (SISO) air-flow control approach, the proposed MIMO control approach shows up to 7.36 percent lower hydrogen fuel consumption. Compared to a traditional load governor, the novel cascaded cross-section RG (CC-RG) shows up to 3.68 percent less mean absolute percent error (MAPE) on net power tracking and greatly improved worst-case OER on realistic drive-cycle simulations. Control development and validations were conducted on two fuel cell system (FCS) models, a nonlinear open-source model and a proprietary Ford high-fidelity model
A Generalized Frequency Domain Embedded State Space Approach for Controller Synthesis
Authors: Ahmad A. Masoud
Date: 2023-02-25 00:00:00

This paper develops a novel control synthesis approach for a wide class of practical systems. The control action is derived by inserting a compensator device in the forward path of the system that is to be controlled. The compensator design method is based on a state space system that is embedded in the frequency domain of the plant. It uses a nonlinear subspace that is the image of the compensator being used to determine the parameters of the compensator and modify system behavior. The coordinates of the state space system are taken as the compensator parameters. The method is capable of designing a compensator of arbitrary order to make the system comply with given stability performance requirements provided that these requirements can be geometrically interpreted in the frequency domain. The approach is developed and a proof of its ability to converge, if a solution exists, to the compensator tuning parameter set that satisfy the desired performance conditions is provided. A set of design examples are supplied to demonstrate the applicability of the approach to different types of linear, nonlinear, SISO and MIMO systems and system with delays.
Impact of Thermal Variability on SOC Estimation Algorithms
Authors: Wasiue Ahmed, Mokhi Maan Siddiqui, Dr. Faheemullah Shaikh
Date: 2023-02-25 00:00:00
Tasks: Management

While the efficiency of renewable energy components like inverters and PV panels is at an all-time high, there are still research gaps for batteries. Lithium-ion batteries have a lot of potential, but there are still some problems that need fixing, such as thermal management. Because of this, the battery management system accomplishes its goal. In order for a battery management system (BMS) to function properly, it must make accurate estimates of all relevant parameters, including state of health, state of charge, and temperature; however, for the purposes of this article, we will only discuss SOC. The goal of this article is to estimate the SOC of a lithium-ion battery at different temperatures. Comparing the Extended Kalam filter algorithm to coulomb counting at various temperatures concludes this exhaustive investigation. The graphene battery has the highest SOC when operated at the optimal temperature, as determined by extensive analysis and correlation between SOC and temperature is not linear
Diagonally Square Root Integrable Kernels in System Identification
Authors: Mohammad Khosravi, Roy S. Smith
Date: 2023-02-24 00:00:00

In recent years, the reproducing kernel Hilbert space (RKHS) theory has played a crucial role in linear system identification. The core of a RKHS is the associated kernel characterizing its properties. Accordingly, this work studies the class of diagonally square root integrable (DSRI) kernels. We demonstrate that various well-known stable kernels introduced in system identification belong to this category. Moreover, it is shown that any DSRI kernel is also stable and integrable. We look into certain topological features of the RKHSs associated with DSRI kernels, particularly the continuity of linear operators defined on the respective RKHSs. For the stability of a Gaussian process centered at a stable impulse response, we show that the necessary and sufficient condition is the diagonally square root integrability of the corresponding kernel. Furthermore, we elaborate on this result by providing proper interpretations.
Implicit neural representations for unsupervised super-resolution and denoising of 4D flow MRI
Authors: Simone Saitta, Marcello Carioni, Subhadip Mukherjee, Carola-Bibiane Schönlieb, Alberto Redaelli
Date: 2023-02-24 00:00:00
Tasks: Denoising, Super-Resolution

4D flow MRI is a non-invasive imaging method that can measure blood flow velocities over time. However, the velocity fields detected by this technique have limitations due to low resolution and measurement noise. Coordinate-based neural networks have been researched to improve accuracy, with SIRENs being suitable for super-resolution tasks. Our study investigates SIRENs for time-varying 3-directional velocity fields measured in the aorta by 4D flow MRI, achieving denoising and super-resolution. We trained our method on voxel coordinates and benchmarked our approach using synthetic measurements and a real 4D flow MRI scan. Our optimized SIREN architecture outperformed state-of-the-art techniques, producing denoised and super-resolved velocity fields from clinical data. Our approach is quick to execute and straightforward to implement for novel cases, achieving 4D super-resolution.
A Data-Driven Polynomial Chaos Expansion-Based Method for Microgrid Ramping Support Capability Assessment and Enhancement
Authors: Mohan Du, Xiaozhe Wang
Date: 2023-02-24 00:00:00
Tasks: Scheduling

Microgrids (MGs) are regarded as effective solutions to provide ramping support to the main grid during heavy-load periods. Nevertheless, the uncertain renewable energy sources (RES) and electric vehicles (EVs) integrated into an MG may affect the ramping support capability (RSC) of an MG. To address the challenge, this paper develops a data-driven sparse polynomial chaos expansion (DDSPCE)-based method to accurately and efficiently evaluate the hour-by-hour RSC of an MG. The DDSPCE model is further exploited to identify the most influential random inputs, based on which a scheduling method of BESS is developed to enhance the RSC of an MG. Simulation results in the modified IEEE 33-bus MG shows that the proposed method takes less than 3 minutes for evaluating and enhancing the hourly RSC.
Electric Vehicle E-hailing Fleet Dispatching and Charge Scheduling
Authors: Linji Chen, Homayoun Hamedmoghadam, Mahdi Jalili, Mohsen Ramezani
Date: 2023-02-24 00:00:00
Tasks: Autonomous Vehicles, Scheduling

With recent developments in vehicle and battery technologies, electric vehicles (EVs) are rapidly getting established as a sustainable alternative to traditional fossil-fuel vehicles. This has made the large-scale electrification of ride-sourcing operations a practical viability, providing an opportunity for a leap toward urban sustainability goals. Despite having a similar driving range to fossil-fuel vehicles, EVs are disadvantaged by their long charging times which compromises the total fleet service time. To efficiently manage an EV fleet, the operator needs to address the charge scheduling problem as part of the dispatch strategy. This paper introduces a probabilistic matching method which evaluates the optimal trip and charging decisions for a fully electrified e-hailing fleet, with the goal of maximising the operator's expected market profit. In the midst of the technological transition towards autonomous vehicles, it is also critical to include stochastic driver behaviours in transport models as presented in this paper. Since drivers may either comply with trip dispatching or choose to reject a charging trip order considering the additional fees, contrary to the commonly assumed fleet autonomy, the proposed method designs an incentivisation scheme (charging discounts) to encourage driver compliance so that the planned charging trips and the associated profit can be realised.
Efficient Point Mass Predictor for Continuous and Discrete Models with Linear Dynamics
Authors: Jakub Matousek, Jindrich Dunik, Marek Brandner, Chan Gook Park, Yeongkwon Choe
Date: 2023-02-24 00:00:00
Tasks: State Estimation

This paper deals with state estimation of stochastic models with linear state dynamics, continuous or discrete in time. The emphasis is laid on a numerical solution to the state prediction by the time-update step of the grid-point-based point-mass filter (PMF), which is the most computationally demanding part of the PMF algorithm. A novel way of manipulating the grid, leading to the time-update in form of a convolution, is proposed. This reduces the PMF time complexity from quadratic to log-linear with respect to the number of grid points. Furthermore, the number of unique transition probability values is greatly reduced causing a significant reduction of the data storage needed. The proposed PMF prediction step is verified in a numerical study.
A holistically 3D-printed flexible millimeter-wave Doppler radar: Towards fully printed high-frequency multilayer flexible hybrid electronics systems
Authors: Hong Tang, Yingjie Zhang, Bowen Zheng, Sensong An, Mohammad Haerinia, Yunxi Dong, Yi Huang, Wei Guo, Hualiang Zhang
Date: 2023-02-24 00:00:00

Flexible hybrid electronics (FHE) is an emerging technology enabled through the integration of advanced semiconductor devices and 3D printing technology. It unlocks tremendous market potential by realizing low-cost flexible circuits and systems that can be conformally integrated into various applications. However, the operating frequencies of most reported FHE systems are relatively low. It is also worth to note that reported FHE systems have been limited to relatively simple design concept (since complex systems will impose challenges in aspects such as multilayer interconnections, printing materials, and bonding layers). Here, we report a fully 3D-printed flexible four-layer millimeter-wave Doppler radar (i.e., a millimeter-wave FHE system). The sensing performance and flexibility of the 3D-printed radar are characterized and validated by general field tests and bending tests, respectively. Our results demonstrate the feasibility of developing fully 3D-printed high-frequency multilayer FHE, which can be conformally integrated into irregular surfaces (e.g., vehicle bumpers) for applications such as vehicle radars and wearable electronics.
Power System Recovery Coordinated with (Non-)Black-Start Generators
Authors: Meng Zhao, Patrick R. Maloney, Xinda Ke, Juan Carlos Bedoya Ceballos, Xiaoyuan Fan, Marcelo A. Elizondo
Date: 2023-02-23 00:00:00

Power restoration is an urgent task after a black-out, and recovery efficiency is critical when quantifying system resilience. Multiple elements should be considered to restore the power system quickly and safely. This paper proposes a recovery model to solve a direct-current optimal power flow (DCOPF) based on mixed-integer linear programming (MILP). Since most of the generators cannot start independently, the interaction between black-start (BS) and non-black-start (NBS) generators must be modeled appropriately. The energization status of the NBS is coordinated with the recovery status of transmission lines, and both of them are modeled as binary variables. Also, only after an NBS unit receives cranking power through connected transmission lines, will it be allowed to participate in the following system dispatch. The amount of cranking power is estimated as a fixed proportion of the maximum generation capacity. The proposed model is validated on several test systems, as well as a 1393-bus representation system of the Puerto Rican electric power grid. Test results demonstrate how the recovery of NBS units and damaged transmission lines can be optimized, resulting in an efficient and well-coordinated recovery procedure.