pwc-archive/papers-with-abstracts huggingface dataset:
Evaluation of the Driving Performance and User Acceptance of a Predictive Eco-Driving Assistance System for Electric Vehicles
Authors: Sai Krishna Chada, Daniel Görges, Achim Ebert, Roman Teutsch, Shreevatsa Puttige Subramanya
Date: 2022-08-24 00:00:00

In this work, a predictive eco-driving assistance system (pEDAS) with the goal to assist drivers in improving their driving style and thereby reducing the energy consumption in battery electric vehicles while enhancing the driving safety and comfort is introduced and evaluated. pEDAS in this work is equipped with two model predictive controllers (MPCs), namely reference-tracking MPC and car-following MPC, that use the information from onboard sensors, signal phase and timing (SPaT) messages from traffic light infrastructure, and geographical information of the driving route to compute an energy-optimal driving speed. An optimal speed suggestion and informative advice are indicated to the driver using a visual feedback. pEDAS provides continuous feedback and encourages the drivers to perform energy-efficient car-following while tracking a preceding vehicle, travel at safe speeds at turns and curved roads, drive at energy-optimal speed determined using dynamic programming in freeway scenarios, and travel with a green-wave optimal speed to cross the signalized intersections at a green phase whenever possible. Furthermore, to evaluate the efficacy of the proposed pEDAS, user studies were conducted with 41 participants on a dynamic driving simulator. The objective analysis revealed that the drivers achieved mean energy savings up to 10%, reduced the speed limit violations, and avoided unnecessary stops at signalized intersections by using pEDAS. Finally, the user acceptance of the proposed pEDAS was evaluated using the Technology Acceptance Model (TAM) and Theory of Planned Behavior (TPB). The results showed an overall positive attitude of users and that the perceived usefulness and perceived behavioral control were found to be the significant factors in influencing the behavioral intention to use pEDAS.
The Effects of Correctly Modeling Generator Step-Up Transformer Status in Geomagnetic Disturbance Studies
Authors: Jessica L. Wert, Pooria Dehghanian, Jonathan Snodgrass, Thomas J. Overbye
Date: 2022-08-23 00:00:00

In order to correctly model the impacts of geomagnetically induced current (GIC) flows, the generator step-up (GSU) transformer status must be properly modeled. In power flow studies, generators are typically removed from service without disconnecting their GSU transformers since the GSU transformer status has little to no impact on the power flow result. In reality, removing a generator from service involves also removing its GSU transformer from service. This difference presents a discrepancy between simulated behavior and system observations during geomagnetic disturbance (GMD) events. This paper presents GMD case studies on 2000-bus and 24,000-bus systems in which reactive power losses and geomagnetically induced currents are compared across scenarios in which the GSU transformers for the disconnected generators are either in-service or out-of-service. The results demonstrate a 3.2 to 15.5 percent error in reactive power losses when the GSU status is modeled incorrectly. Discrepancies of up to 95 A per phase for branch GIC flows and 450 A for transformer neutral GIC flows are also observed and visualized.
Autonomous Driving Vehicles Using Adaptive Learning Method for Data Fusion
Authors: Farhad Aghili
Date: 2022-08-23 00:00:00
Tasks: Autonomous Driving

This paper presents an adaptive learning method for data fusion in autonomous driving vehicles. The localization is based on the integration of Inertial Measurement Unit (IMU) with two Real-Time Kinematic (RTK) Global Positioning System (GPS) units in an adaptive Kalman filter (KF). The observability analysis reveals that $i$) integration of a single GPS with IMU does not constitute an observable system; $ii$) integration of two GPS units with IMU results in a locally observable system provided that the line connecting two GPS antennas is not parallel with the vector of the measured acceleration, i.e., the sum of inertial and gravitational accelerations. The latter case makes it possible to compensate for the error in the estimated orientation due to gyro drift and its bias without needing additional instruments for absolute orientation measurements, e.g., a magnetic compass. Moreover, in order to cope with the fact that GPS systems sometimes lose their signal and receive inaccurate position data, the self-tuning filter estimates the covariance matrix associated with the GPS measurement noise. This allows the KF to incorporate GPS measurements in the data fusion process heavily only when the information received by GPS becomes reliably available.
Asset Participation and Aggregation in Incentive-Based Demand Response Programs
Authors: Utkarsha Agwan, Costas J. Spanos, Kameshwar Poolla
Date: 2022-08-23 00:00:00

In order to manage peak-grid events, utilities run incentive-based demand response (DR) programs in which they offer an incentive to assets who promise to curtail power consumption, and impose penalties if they fail to do so. We develop a probabilistic model for the curtailment capability of these assets, and use it to derive analytic expressions for the optimal participation (i.e., promised curtailment) and profitability from the DR asset perspective. We also investigate the effects of risk-aversion and curtailment uncertainty on both promised curtailment and profit. We use the probabilistic model to evaluate the benefits of forming asset aggregations for participation in DR programs, and develop a numerical test to estimate asset complementarity. We illustrate our results using load data from commercial office buildings.
Cyber-resilient Automatic Generation Control for Systems of AC Microgrids
Authors: Tong Huang, Dan Wu, Marija Ilic
Date: 2022-08-23 00:00:00
Tasks: Cyber Attack Detection

In this paper we propose a co-design of the secondary frequency regulation in systems of AC microgrids and its cyber securty solutions. We term the secondary frequency regulator a Micro-Automatic Generation Control (Micro-AGC) for highlighting its same functionality as the AGC in bulk power systems. We identify sensory challenges and cyber threats facing the Micro-AGC. To address the sensory challenges, we introduce a new microgrid model by exploiting the rank-one deficiency property of microgrid dynamics. This model is used to pose an optimal Micro-AGC control problem that is easily implemented, because it does not require fast frequency measurements. An end-to-end cyber security solution to the False Data Injection (FDI) attack detection and mitigation is developed for the proposed Micro-AGC. The front-end barrier of applying off-the-shelf algorithms for cyber attack detection is removed by introducing a data-driven modeling approach. Finally, we propose an observer-based corrective control for an islanded microgrid and a collaborative mitigation schemes in systems of AC microgrids. We demonstrate a collaborative role of systems of microgrids during cyber attacks. The performance of the proposed cyber-resilient Micro-AGC is tested in a system of two networked microgrids.
Improvement of Sliding Mode Control Strategy Founded on Cascaded Doubly Fed Induction Generator Powered by a Matrix Converter
Authors: A. Maafa, H. Mellah, K. Ghedamsi, D. Aouzellag
Date: 2022-08-20 00:00:00
Tasks: continuous-control, Continuous Control, Point Tracking

The current paper presents a Sliding Mode Controller (SMC) for indirect field-oriented Cascaded Doubly Fed Induction Generator (CDFIG) powered through a Matrix Converter (MC). The proposed SMC employs a continuous control strategy to accomplish free chattering fractional-order sliding-mode control and to ensure that the control of the first DFIG stator's reactive and active power is separated. An MC is used to control the current provided to the second stator of the CDFIG as an alternative to standard voltage source inverters. The two MCs are controlled via Space-Vector Pulse-Width Modulation (SVPWM) and Indirect Field Oriented Control (IFOC). The proposed Wind Power Generation System (WPGS) is used with the purpose to ensure Maximum Power Point Tracking (MPPT) sensing under various disturbance variables such as turbulent wind. The simulation results prove the efficiency and robustness of the proposed method.
Combining phylogeny and coevolution improves the inference of interaction partners among paralogous proteins
Authors: Carlos A. Gandarilla-Perez, Sergio Pinilla, Anne-Florence Bitbol, Martin Weigt
Date: 2022-08-24 00:00:00

Predicting protein-protein interactions from sequences is an important goal of computational biology. Various sources of information can be used to this end. Starting from the sequences of two interacting protein families, one can use phylogeny or residue coevolution to infer which paralogs are specific interaction partners within each species. We show that these two signals can be combined to improve the performance of the inference of interaction partners among paralogs. For this, we first align the sequence-similarity graphs of the two families through simulated annealing, yielding a robust partial pairing. We next use this partial pairing to seed a coevolution-based iterative pairing algorithm. This combined method improves performance over either separate method. The improvement obtained is striking in the difficult cases where the average number of paralogs per species is large or where the total number of sequences is modest.
Using Conservation Laws to Infer Deep Learning Model Accuracy of Richtmyer-meshkov Instabilities
Authors: Charles F. Jekel, Dane M. Sterbentz, Sylvie Aubry, Youngsoo Choi, Daniel A. White, Jonathan L. Belof
Date: 2022-07-19 00:00:00
Tasks: Deep Learning

Richtmyer-Meshkov Instability (RMI) is a complicated phenomenon that occurs when a shockwave passes through a perturbed interface. Over a thousand hydrodynamic simulations were performed to study the formation of RMI for a parameterized high velocity impact. Deep learning was used to learn the temporal mapping of initial geometric perturbations to the full-field hydrodynamic solutions of density and velocity. The continuity equation was used to include physical information into the loss function, however only resulted in very minor improvements at the cost of additional training complexity. Predictions from the deep learning model appear to accurately capture temporal RMI formations for a variety of geometric conditions within the domain. First principle physical laws were investigated to infer the accuracy of the model's predictive capability. While the continuity equation appeared to show no correlation with the accuracy of the model, conservation of mass and momentum were weakly correlated with accuracy. Since conservation laws can be quickly calculated from the deep learning model, they may be useful in applications where a relative accuracy measure is needed.
Robust Tests of Model Incompleteness in the Presence of Nuisance Parameters
Authors: Shuowen Chen, Hiroaki Kaido
Date: 2022-08-24 00:00:00

Economic models may exhibit incompleteness depending on whether or not they admit certain policy-relevant features such as strategic interaction, self-selection, or state dependence. We develop a novel test of model incompleteness and analyze its asymptotic properties. A key observation is that one can identify the least-favorable parametric model that represents the most challenging scenario for detecting local alternatives without knowledge of the selection mechanism. We build a robust test of incompleteness on a score function constructed from such a model. The proposed procedure remains computationally tractable even with nuisance parameters because it suffices to estimate them only under the null hypothesis of model completeness. We illustrate the test by applying it to a market entry model and a triangular model with a set-valued control function.
Will the last be the first? School closures and educational outcomes
Authors: Michele Battisti, Giuseppe Maggio
Date: 2022-08-24 00:00:00

Governments have implemented school closures and online learning as one of the main tools to reduce the spread of Covid-19. Despite the potential benefits in terms of reduction of cases, the educational costs of these policies may be dramatic. This work identifies the educational costs, expressed as decrease in test scores, for the whole universe of Italian students attending the 5th, 8th and 13th grade of the school cycle during the 2021/22 school year. The analysis relies on a difference-in-difference model in relative time, where the control group is the closest generation before the Covid-19 pandemic. The results suggest a national average loss between 1.6-4.1% and 0.5-2.4% in Mathematics and Italian test scores, respectively. After collecting the precise number of days of school closures for the universe of students in Sicily, we estimate that 30 additional days of closure decrease the test score by 1%. However, the impact is much larger for students from high schools (1.8%) compared to students from low and middle schools (0.5%). This is likely explained by the lower relevance of parental inputs and higher reliance on peers inputs, within the educational production function, for higher grades. Findings are also heterogeneous across class size and parental job conditions, pointing towards potential growing inequalities driven by the lack of in front teaching.
Panacea or Placebo? Exploring Causal Effects of Nonlocal Vehicle Driving Restriction Policies on Traffic Congestion Using Difference-in-differences Approach
Authors: Yuan Liang, Quan Yuan, Daoge Wang, Yong Feng, Pengfei Xu, Jiangping Zhou
Date: 2022-08-24 00:00:00

Car dependence has been threatening transportation sustainability as it contributes to congestion and associated externalities. In response, various transport policies that restrict the use of private vehicle have been implemented. However, empirical evaluations of such policies have been limited. To assess these policies' benefits and costs, it is imperative to accurately evaluate how such policies affect traffic conditions. In this study, we compile a refined spatio-temporal resolution data set of the floating-vehicle-based traffic performance index to examine the effects of a recent nonlocal vehicle driving restriction policy in Shanghai, one of most populous cities in the world. Specifically, we explore whether and how the policy impacted traffic speeds in the short term by employing a quasi-experimental difference-in-differences modeling approach. We find that: (1) In the first month, the policy led to an increase of the network-level traffic speed by 1.47% (0.352 km/h) during evening peak hours (17:00-19:00) but had no significant effects during morning peak hours (7:00-9:00). (2) The policy also helped improve the network-level traffic speed in some unrestricted hours (6:00, 12:00, 14:00, and 20:00) although the impact was marginal. (3) The short-term effects of the policy exhibited heterogeneity across traffic analysis zones. The lower the metro station density, the greater the effects were. We conclude that driving restrictions for non-local vehicles alone may not significantly reduce congestion, and their effects can differ both temporally and spatially. However, they can have potential side effects such as increased purchase and usage of new energy vehicles, owners of which can obtain a local license plate of Shanghai for free.
Cognitive Hierarchies in Multi-Stage Games of Incomplete Information: Theory and Experiment
Authors: Po-Hsuan Lin
Date: 2022-08-23 00:00:00

Sequential equilibrium is the conventional approach for analyzing multi-stage games of incomplete information. It relies on mutual consistency of beliefs. To relax mutual consistency, I theoretically and experimentally explore the dynamic cognitive hierarchy (DCH) solution. One property of DCH is that the solution can vary between two different games sharing the same reduced normal form, i.e., violation of invariance under strategic equivalence. I test this prediction in a laboratory experiment using two strategically equivalent versions of the dirty-faces game. The game parameters are calibrated to maximize the expected difference in behavior between the two versions, as predicted by DCH. The experimental results indicate significant differences in behavior between the two versions, and more importantly, the observed differences align with DCH. This suggests that implementing a dynamic game experiment in reduced normal form (using the "strategy method") could lead to distortions in behavior.
Hierarchical Reinforcement Learning Based Video Semantic Coding for Segmentation
Authors: Guangqi Xie, Xin Li, Shiqi Lin, Li Zhang, Kai Zhang, Yue Li, Zhibo Chen
Date: 2022-08-24 00:00:00
Tasks: Hierarchical Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Semantic Compression, Video Segmentation, Video Semantic Segmentation

The rapid development of intelligent tasks, e.g., segmentation, detection, classification, etc, has brought an urgent need for semantic compression, which aims to reduce the compression cost while maintaining the original semantic information. However, it is impractical to directly integrate the semantic metric into the traditional codecs since they cannot be optimized in an end-to-end manner. To solve this problem, some pioneering works have applied reinforcement learning to implement image-wise semantic compression. Nevertheless, video semantic compression has not been explored since its complex reference architectures and compression modes. In this paper, we take a step forward to video semantic compression and propose the Hierarchical Reinforcement Learning based task-driven Video Semantic Coding, named as HRLVSC. Specifically, to simplify the complex mode decision of video semantic coding, we divided the action space into frame-level and CTU-level spaces in a hierarchical manner, and then explore the best mode selection for them progressively with the cooperation of frame-level and CTU-level agents. Moreover, since the modes of video semantic coding will exponentially increase with the number of frames in a Group of Pictures (GOP), we carefully investigate the effects of different mode selections for video semantic coding and design a simple but effective mode simplification strategy for it. We have validated our HRLVSC on the video segmentation task with HEVC reference software HM16.19. Extensive experimental results demonstrated that our HRLVSC can achieve over 39% BD-rate saving for video semantic coding under the Low Delay P configuration.
Deep Hyperspectral and Multispectral Image Fusion with Inter-image Variability
Authors: Xiuheng Wang, Ricardo Augusto Borsoi, Cédric Richard, Jie Chen
Date: 2022-08-24 00:00:00

Hyperspectral and multispectral image fusion allows us to overcome the hardware limitations of hyperspectral imaging systems inherent to their lower spatial resolution. Nevertheless, existing algorithms usually fail to consider realistic image acquisition conditions. This paper presents a general imaging model that considers inter-image variability of data from heterogeneous sources and flexible image priors. The fusion problem is stated as an optimization problem in the maximum a posteriori framework. We introduce an original image fusion method that, on the one hand, solves the optimization problem accounting for inter-image variability with an iteratively reweighted scheme and, on the other hand, that leverages light-weight CNN-based networks to learn realistic image priors from data. In addition, we propose a zero-shot strategy to directly learn the image-specific prior of the latent images in an unsupervised manner. The performance of the algorithm is illustrated with real data subject to inter-image variability.
Deep Joint Source-Channel Coding Based on Semantics of Pixels
Authors: Qizheng Sun, Caili Guo, Yang Yang, Jiujiu Chen, Rui Tang, Chuanhong Liu
Date: 2022-08-24 00:00:00

The semantic information of the image for intelligent tasks is hidden behind the pixels, and slight changes in the pixels will affect the performance of intelligent tasks. In order to preserve semantic information behind pixels for intelligent tasks during wireless image transmission, we propose a joint source-channel coding method based on semantics of pixels, which can improve the performance of intelligent tasks for images at the receiver by retaining semantic information. Specifically, we first utilize gradients of intelligent task's perception results with respect to pixels to represent the semantic importance of pixels. Then, we extract the semantic distortion, and train the deep joint source-channel coding network with the goal of minimizing semantic distortion rather than pixel's distortion. Experiment results demonstrate that the proposed method improves the performance of the intelligent classification task by 1.38% and 66% compared with the SOTA deep joint source-channel coding method and the traditional separately source-channel coding method at the same transmission ra te and signal-to-noise ratio.
Multidimensional orthogonal matching pursuit: theory and application to high accuracy joint localization and communication at mmWave
Authors: Joan Palacios, Nuria González-Prelcic, Cristian Rusu
Date: 2022-08-24 00:00:00

Greedy approaches in general, and orthogonal matching pursuit in particular, are the most commonly used sparse recovery techniques in a wide range of applications. The complexity of these approaches is highly dependent on the size of the dictionary chosen to represent the sparse signal. When the dictionary has to be large to enable high accuracy reconstructions, greedy strategies might however incur in prohibitive complexity. In this paper, we propose first the formulation of a new type of sparse recovery problems where the sparse signal is represented by a set of independent and smaller dictionaries instead of a large single one. Then, we derive a low complexity multdimensional orthogonal matching pursuit (MOMP) strategy for sparse recovery with a multdimensional dictionary. The projection step is performed iteratively on every dimension of the dictionary while fixing all other dimensions to achieve high accuracy estimation at a reasonable complexity. Finally, we formulate the problem of high resolution time domain channel estimation at millimeter wave (mmWave) frequencies as a multidimensional sparse recovery problem that can be solved with MOMP. The channel estimates are later transformed into high accuracy user position estimates exploiting a new localization algorithm that leverages the particular geometry of indoor channels. Simulation results show the effectiveness of MOMP for high accuracy localization at millimeter wave frequencies when operating in realistic 3D scenarios, with practical MIMO architectures feasible at mmWave, and without resorting to perfect synchronization assumptions that simplify the problem.