pwc-archive/papers-with-abstracts huggingface dataset:
Decision Making of Connected Automated Vehicles at An Unsignalized Roundabout Considering Personalized Driving Behaviours
Authors: Peng Hang, Chao Huang, Zhongxu Hu, Yang Xing, Chen Lv
Date: 2021-03-14 00:00:00
Tasks: Decision Making, Model Predictive Control, motion prediction

To improve the safety and efficiency of the intelligent transportation system, particularly in complex urban scenarios, in this paper a game theoretic decision-making framework is designed for connected automated vehicles (CAVs) at unsignalized roundabouts considering their personalized driving behaviours. Within the decision-making framework, a motion prediction module is designed and optimized using model predictive control (MPC) to enhance the effectiveness and accuracy of the decision-making algorithm. Besides, the payoff function of decision making is defined with the consideration of vehicle safety, ride comfort and travel efficiency. Additionally, the constraints of the decision-making problem are constructed. Based on the established decision-making model, Stackelberg game and grand coalition game approaches are adopted to address the decision making of CAVs at an unsignalized roundabout. Three testing cases considering personalized driving behaviours are carried out to verify the performance of the developed decision-making algorithms. The testing results show that the proposed game theoretic decision-making framework is able to make safe and reasonable decisions for CAVs in the complex urban scenarios, validating its feasibility and effectiveness. Stackelberg game approach shows its advantage in guaranteeing personalized driving objectives of individuals, while the grand coalition game approach is advantageous regarding the efficiency improvement of the transportation system.
Periodic Load Rejection for Floating Offshore Wind Turbines via Constrained Subspace Predictive Repetitive Control
Authors: Yichao Liu, Riccardo M. G. Ferrari, Jan-Willem van Wingerden
Date: 2021-04-09 00:00:00
Tasks: Model Predictive Control, Pitch control

Individual Pitch Control (IPC) is an effective control strategy to mitigate the blade loads on large-scale wind turbines. Since IPC usually requires high pitch actuation, the safety constraints of the pitch actuator should be taken into account when designing the controller. This paper introduces a constrained Subspace Predictive Repetitive Control (SPRC) approach, which considers the limitation of blade pitch angle and pitch rate. To fulfill this goal, a model predictive control scheme is implemented in the fully data-driven SPRC approach to incorporate the physical limitations of the pitch actuator in the control problem formulation. An optimal control law subjected to constraints is then formulated so that future constraint violations are anticipated and prevented. Case studies show that the developed constrained SPRC reduces the pitch activities necessary to mitigate the blade loads when experiencing wind turbulence and abrupt wind gusts. More importantly, the approach allows the wind farm operator to design conservative bounds for the pitch actuator constraints that satisfies safety limitations, design specifications and physical restrictions. This will help to alleviate the cyclic fatigue loads on the actuators, increase the structural reliability and extend the lifespan of the pitch control system.
Application of blockchain for secure data transmission in distributed state estimation
Authors: Sajjad Asefi, Yash Madhwal, Yury Yanovich, Elena Gryazina
Date: 2021-04-09 00:00:00
Tasks: State Estimation

The application of renewable energy sources in the power grid increases the necessity of tracking the system's state, especially in smart grids, where there is a bidirectional transfer of data and power. The complexity of coupling between communication and the electrical infrastructure in a smart grid will create a higher chance for security breach. Increasing the state estimation accuracy will help the smart grid operator efficiently manage the system. The paper proposes an integration of distributed state estimation with a blockchain designed communication platform. Additionally, the asynchronous manner for data transmission, which is more likely to happen in the real world, has been considered as the second task of this research. Finally, a detailed analysis of the blockchain-based application in distributed state estimation is provided. The numerical analysis shows that the proposed method meets real-world performance requirements and brings high security and reliability to the distributed state estimation process.
Improving Power Quality and Power Sharing in Unbalanced Multi-Microgrids Using Energy Storage System
Authors: Mehmet Emin Akdogan, Sara Ahmed
Date: 2021-04-09 00:00:00

The integration of single-phase distributed generations (DG) and unbalanced loads to three-phase DGs brings challenges to the operating system of the microgrids (MGs). These challenges include unbalanced voltage and unequal power flow at the point of common coupling (PCC) of the MGs. The aim of this paper is to develop an Energy Storage System (ESS) with multi-function control for islanded multi-microgrids (MMG) consisting of single and three PV-DGs to enhance its power quality and power-sharing of the MMGs. The proposed multi-function control consists of a reactive power compensation (RPC), a PCC power balance regulator (PBR), and a reactive power sharing algorithm (RPSA). The RPC and the PBR improve the three-phase PCC voltage and power unbalance of the MMG and the RPSA regulates the reactive power-sharing among the MMGs. The proposed strategy is experimentally validated on the Opal-RT OP5600 real-time simulator. The voltage unbalance factor (VUF) at the PCC is reduced from 4.3 percent to 0.03 percent, while the three-phase and the single-phase reactive powers are shared proportionally between different rated DGs.
Energy Storage System (ESS) for Compensating Unbalanced Multi-microgrids Using Modified Reverse Droop Control
Authors: Mehmet Emin Akdogan, Sara Ahmed
Date: 2021-04-09 00:00:00

The connection of single-phase microgrids (MG) and loads to three-phase MGs creates power quality problems such as unbalanced voltage and voltage rise at the point of common coupling (PCC) of the MGs. In this paper, a modified reverse droop control (MRDC) scheme in the Energy Storage System (ESS) is proposed to improve the three-phase PCC voltage quality in multi-microgrids (MMG). The MRDC consists of a reactive power compensator (RPC) and a voltage compensator. The controller regulates the reactive power and voltage unbalance of the MMG by using the reactive power produced by the ESS. The effectiveness of this proposed scheme is verified in real-time simulation using the Opal-RT OP5600 real-time simulator. The voltage unbalance factor (VUF) at the PCC is decreased from 3.6 percent to 0.25 percent, while the reactive power is reduced significantly at the single-phase load.
Faster than Real-Time Simulation: Methods, Tools, and Applications
Authors: Xiaorui Liu, Juan Ospina, Ioannis Zografopoulos, Alonzo Russell, Charalambos Konstantinou
Date: 2021-04-09 00:00:00
Tasks: Management, Scheduling

Real-time simulation enables the understanding of system operating conditions by evaluating simulation models of physical components running synchronized at the real-time wall clock. Leveraging the real-time measurements of comprehensive system models, faster than real-time (FTRT) simulation allows the evaluation of system architectures at speeds faster than real-time. FTRT simulation can assist in predicting the system's behavior efficiently, thus assisting the operation of system processes. Namely, the provided acceleration can be used for improving system scheduling, assessing system vulnerabilities, and predicting system disruptions in real-time systems. The acceleration of simulation times can be achieved by utilizing digital real-time simulators (RTS) and high-performance computing (HPC) architectures. FTRT simulation has been widely used, among others, for the operation, design, and investigation of power system events, building emergency management plans, wildfire prediction, etc. In this paper, we review the existing literature on FTRT simulation and its applications in different disciplines, with a particular focus on power systems. We present existing system modeling approaches, simulation tools and computing frameworks, and stress the importance of FTRT accuracy.
A Cardinality Minimization Approach to Security-Constrained Economic Dispatch
Authors: David Troxell, Miju Ahn, Harsha Gangammanavar
Date: 2021-04-08 00:00:00

We present a threshold-based cardinality minimization formulation to model the security-constrained economic dispatch problem. The model aims to minimize the operating cost of the system while simultaneously reducing the number of lines operating in emergency operating zones during contingency events. The model allows the system operator to monitor the duration for which lines operate in emergency zones and ensure that they are within the acceptable reliability standards determined by the system operators. We develop a continuous difference-of-convex approximation of the cardinality minimization problem and a solution method to solve the problem. Our numerical experiments demonstrate that the cardinality minimization approach reduces the overall system operating cost as well as avoids prolonged periods of high electricity prices during contingency events.
Synchronization in Electric Power Networks with Inherent Heterogeneity Up to 100% Inverter-Based Renewable Generation
Authors: Amirhossein Sajadi, Rick Wallace Kenyon, Bri-Mathias Hodge
Date: 2021-04-08 00:00:00

The synchronized operation of power generators is the foundation of electric power network stability and a key to the prevention of undesired power outages and blackouts. Here, we derive the conditions that guarantee synchronization in power networks with inherent generator heterogeneity when subjected to small perturbations, and perform a parametric sensitivity analysis to understand synchronization with varied types of generators. As inverter-based resources, which are the primary interfacing technology for many renewable sources of energy, have supplanted synchronous generators in ever growing numbers, the center of attention on associated integration challenges have resided primarily on the role of declining system inertia. Our results instead highlight the critical role of generator damping in achieving a stable state of synchronization. Additionally, we report the feasibility of operating interconnected electric grids with up to 100% power contribution from inverter-based renewable generation technologies. Our study has important implications as it sets the basis for the development of advanced control architectures and grid optimization methods that ensure synchronization and further pave the path towards the decarbonization of the electric power sector.
Design and implementation of an environment for Learning to Run a Power Network (L2RPN)
Authors: Marvin Lerousseau
Date: 2021-04-06 00:00:00
Tasks: Math, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), RTE

This report summarizes work performed as part of an internship at INRIA, in partial requirement for the completion of a master degree in math and informatics. The goal of the internship was to develop a software environment to simulate electricity transmission in a power grid and actions performed by operators to maintain this grid in security. Our environment lends itself to automate the control of the power grid with reinforcement learning agents, assisting human operators. It is amenable to organizing benchmarks, including a challenge in machine learning planned by INRIA and RTE for 2019. Our framework, built on top of open-source libraries, is available at https://github.com/MarvinLer/pypownet. In this report we present intermediary results and its usage in the context of a reinforcement learning game.
Progressive extension of reinforcement learning action dimension for asymmetric assembly tasks
Authors: Yuhang Gai, Jiuming Guo, Dan Wu, Ken Chen
Date: 2021-04-06 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement learning (RL) is always the preferred embodiment to construct the control strategy of complex tasks, like asymmetric assembly tasks. However, the convergence speed of reinforcement learning severely restricts its practical application. In this paper, the convergence is first accelerated by combining RL and compliance control. Then a completely innovative progressive extension of action dimension (PEAD) mechanism is proposed to optimize the convergence of RL algorithms. The PEAD method is verified in DDPG and PPO. The results demonstrate the PEAD method will enhance the data-efficiency and time-efficiency of RL algorithms as well as increase the stable reward, which provides more potential for the application of RL.
Population genetics in microchannels
Authors: Anzhelika Koldaeva, Hsieh-Fu Tsai, Amy Q. Shen, Simone Pigolotti
Date: 2021-04-09 00:00:00
Tasks: Diversity

Spatial constraints such as rigid barriers affect the dynamics of cell populations, potentially altering the course of natural evolution. In this paper, we investigate the population genetics of Escherichia coli proliferating in microchannels with open ends. Our analysis is based on a population model in which reproducing cells shift entire lanes of cells towards the open ends of the channel. The model predicts that diversity is lost very rapidly within lanes, but at a much slower pace among lanes. As a consequence, two mixed, neutral E. coli strains competing in a microchannel must organize into an ordered regular stripe pattern in the course of a few generations. These predictions are in quantitative agreement with our experiments. We also demonstrate that random mutations appearing in the middle of the channel are much more likely to reach fixation than those occurring elsewhere. Our results illustrate fundamental mechanisms of microbial evolution in spatially confined space.
Ammonia-induced Calcium Phosphate Nanostructure: A Potential Assay for Studying Osteoporosis and Bone Metastasis
Authors: Sijia Chen, Qiong Wang, Felipe Eltit, Yubin Guo, Michael Cox, Rizhi Wang
Date: 2021-04-09 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

Osteoclastic resorption of bone plays a central role in both osteoporosis and bone metastasis. A reliable in vitro assay that simulates osteoclastic resorption in vivo would significantly speed up the process of devel-oping effective therapeutic solutions for those diseases. Here we reported the development of a novel and robust nano-structured calcium phosphate coating with unique functions on the track-etched porous mem-brane by using an ammonia-induced mineralization (AiM) technique. The calcium phosphate coating uni-formly covers one side of the PET membrane enabling testing for osteoclastic resorption. The track-etched pores in the PET membrane allow calcium phosphate mineral pins to grow inside, which, on one hand, enhances coating integration with membrane substrate, and on the other hand provides diffusion channels for delivering drugs from the lower chamber of a double-chamber cell culture system. The applications of the processed calcium phosphate coating was first demonstrated as a drug screening device by using alen-dronate, a widely used drug for osteoporosis. It was confirmed that the delivery of alendronate significant-ly decreased both the number of monocyte-differentiated osteoclasts and coating resorption. To demon-strate the application in studying bone metastasis, we delivered PC3 prostate cancer conditioned medium and confirmed that both the differentiation of monocytes into osteoclasts and the osteoclastic resorption of the calcium phosphate coating were significantly enhanced. This novel assay thus provides a new platform for studying osteoclastic activities and assessing drug efficacy in vitro.
Impact of pandemic fatigue on the spread of COVID-19: a mathematical modelling study
Authors: Disheng Tang, Wei Cao, Jiang Bian, Tie-Yan Liu, Zhifeng Gao, Shun Zheng, Jue Liu
Date: 2021-04-09 00:00:00

In late-2020, many countries around the world faced another surge in number of confirmed cases of COVID-19, including United Kingdom, Canada, Brazil, United States, etc., which resulted in a large nationwide and even worldwide wave. While there have been indications that precaution fatigue could be a key factor, no scientific evidence has been provided so far. We used a stochastic metapopulation model with a hierarchical structure and fitted the model to the positive cases in the US from the start of outbreak to the end of 2020. We incorporated non-pharmaceutical interventions (NPIs) into this model by assuming that the precaution strength grows with positive cases and studied two types of pandemic fatigue. We found that people in most states and in the whole US respond to the outbreak in a sublinear manner (with exponent k=0.5), while only three states (Massachusetts, New York and New Jersey) have linear reaction (k=1). Case fatigue (decline in people's vigilance to positive cases) is responsible for 58% of cases, while precaution fatigue (decay of maximal fraction of vigilant group) accounts for 26% cases. If there were no pandemic fatigue (no case fatigue and no precaution fatigue), total positive cases would have reduced by 68% on average. Our study shows that pandemic fatigue is the major cause of the worsening situation of COVID-19 in United States. Reduced vigilance is responsible for most positive cases, and higher mortality rate tends to push local people to react to the outbreak faster and maintain vigilant for longer time.
Optimal Epidemic Control in Equilibrium with Imperfect Testing and Enforcement
Authors: Thomas Phelan, Alexis Akira Toda
Date: 2021-04-09 00:00:00

We analyze equilibrium behavior and optimal policy within a Susceptible-Infected-Recovered epidemic model augmented with potentially undiagnosed agents who infer their health status and a social planner with imperfect enforcement of social distancing. We define and prove the existence of a perfect Bayesian Markov competitive equilibrium and contrast it with the efficient allocation subject to the same informational constraints. We identify two externalities, static (individual actions affect current risk of infection) and dynamic (individual actions affect future disease prevalence), and study how they are affected by limitations on testing and enforcement. We prove that a planner with imperfect enforcement will always wish to curtail activity, but that its incentives to do so vanish as testing becomes perfect. When a vaccine arrives far into the future, the planner with perfect enforcement may encourage activity before herd immunity. We find that lockdown policies have modest welfare gains, whereas quarantine policies are effective even with imperfect testing.
Projection Bias in Effort Choices
Authors: Marc Kaufmann
Date: 2021-04-09 00:00:00

Working becomes harder as we grow tired or bored. I model individuals who underestimate these changes in marginal disutility -- as implied by "projection bias" -- when deciding whether or not to continue working. This bias causes people's plans to change: early in the day when they are rested, they plan to work more than late in the day when they are rested. Despite initially overestimating how much they will work, people facing a single task with decreasing returns to effort work optimally. However, when facing multiple tasks, they misprioritize urgent but unimportant over important but non-urgent tasks. And when they face a single task with all-or-nothing rewards (such as being promoted) they start, and repeatedly work on, some overly ambitious tasks that they later abandon. Each day they stop working once they have grown tired, which can lead to large daily welfare losses. Finally, when they have either increasing or decreasing productivity, people work less each day than previously planned. This moves people closer to optimal effort for decreasing, and further away from optimal effort for increasing productivity.
Speaker-conditioned Target Speaker Extraction based on Customized LSTM Cells
Authors: Ragini Sinha, Marvin Tammen, Christian Rollwage, Simon Doclo
Date: 2021-04-09 00:00:00
Tasks: Target Speaker Extraction

Speaker-conditioned target speaker extraction systems rely on auxiliary information about the target speaker to extract the target speaker signal from a mixture of multiple speakers. Typically, a deep neural network is applied to isolate the relevant target speaker characteristics. In this paper, we focus on a single-channel target speaker extraction system based on a CNN-LSTM separator network and a speaker embedder network requiring reference speech of the target speaker. In the LSTM layer of the separator network, we propose to customize the LSTM cells in order to only remember the specific voice patterns corresponding to the target speaker by modifying the information processing in the forget gate. Experimental results for two-speaker mixtures using the Librispeech dataset show that this customization significantly improves the target speaker extraction performance compared to using standard LSTM cells.
The NTNU Taiwanese ASR System for Formosa Speech Recognition Challenge 2020
Authors: Fu-An Chao, Tien-Hong Lo, Shi-Yan Weng, Shih-Hsuan Chiu, Yao-Ting Sung, Berlin Chen
Date: 2021-04-09 00:00:00
Proceeding: IJCLCLP 2021 6
Tasks: Data Augmentation, Speech Enhancement, speech-recognition, Speech Recognition, Transfer Learning

This paper describes the NTNU ASR system participating in the Formosa Speech Recognition Challenge 2020 (FSR-2020) supported by the Formosa Speech in the Wild project (FSW). FSR-2020 aims at fostering the development of Taiwanese speech recognition. Apart from the issues on tonal and dialectical variations of the Taiwanese language, speech artificially contaminated with different types of real-world noise also has to be dealt with in the final test stage; all of these make FSR-2020 much more challenging than before. To work around the under-resourced issue, the main technical aspects of our ASR system include various deep learning techniques, such as transfer learning, semi-supervised learning, front-end speech enhancement and model ensemble, as well as data cleansing and data augmentation conducted on the training data. With the best configuration, our system obtains 13.1 % syllable error rate (SER) on the final-test set, achieving the first place among all participating systems on Track 3.
What is the ground truth? Reliability of multi-annotator data for audio tagging
Authors: Irene Martin-Morato, Annamaria Mesaros
Date: 2021-04-09 00:00:00
Tasks: Audio Tagging

Crowdsourcing has become a common approach for annotating large amounts of data. It has the advantage of harnessing a large workforce to produce large amounts of data in a short time, but comes with the disadvantage of employing non-expert annotators with different backgrounds. This raises the problem of data reliability, in addition to the general question of how to combine the opinions of multiple annotators in order to estimate the ground truth. This paper presents a study of the annotations and annotators' reliability for audio tagging. We adapt the use of Krippendorf's alpha and multi-annotator competence estimation (MACE) for a multi-labeled data scenario, and present how MACE can be used to estimate a candidate ground truth based on annotations from non-expert users with different levels of expertise and competence.