pwc-archive/papers-with-abstracts huggingface dataset:
A Novel Approach to Climate Resilience of Infrastructure Networks
Authors: Qianqian Li, Giuliano Punzo, Craig Robson, Hadi Arbabi, Martin Mayfield
Date: 2022-11-18 00:00:00

With a changing climate, the frequency and intensity of extreme weather events are likely to increase, posing a threat to infrastructure systems' resilience. The response of infrastructure systems to localised failures depends on whether assets are affected randomly, in a targeted strategic way, or any way in between. More than that, infrastructure decisions today, including new routes or improvements to existing assets, will underpin the behaviour of the systems over the next century. It is important to separate and analyse the case of climate-based disruptions and how they affect systems' resilience. This paper presents a probabilistic resilience assessment framework where failure scenarios and network disruptions are generated using weather profile data from climate prediction models with component-level fragility functions. A case study is then carried out to quantify the resilience of Great Britain's railway passenger transport system to high-temperature-related track buckling under the Representative Concentration Pathway 8.5 (RCP8.5) climate change scenario. A 95-year horizon on the resilience of the railway system is drawn. The results also reveal the non-linear responses of the railway system to the increasing temperature and show that models considering random asset failures overestimate the system's resilience.
Nonlinear Moving Horizon Estimation and Model Predictive Control for Buildings with Unknown HVAC Dynamics
Authors: Saman Mostafavi, Harish Doddi, Krishna Kalyanam, David Schwartz
Date: 2022-11-18 00:00:00
Tasks: Management, Model Predictive Control, State Estimation

We present a solution for modeling and online identification for heating, ventilation, and air conditioning (HVAC) control in buildings. Our approach comprises: (a) a resistance-capacitance (RC) model based on first order energy balance for deriving the zone temperature dynamics, and (b) a neural network for modeling HVAC dynamics. State estimation and model identification are simultaneously performed using nonlinear moving horizon estimation (MHE) with physical constraints for system states. We leverage the identified model in model predictive control (MPC) for occupant comfort satisfaction and HVAC energy savings and verify the approach using simulations. Our system relies only on building management system data, does not require extensive data storage, and does not require a detailed building model. This can significantly aid the large scale adoption of MPC for future occupant-centric control of grid-interactive buildings.
Perception-Based Sampled-Data Optimization of Dynamical Systems
Authors: Liliaokeawawa Cothren, Gianluca Bianchin, Sarah Dean, Emiliano Dall'Anese
Date: 2022-11-18 00:00:00
Tasks: Autonomous Driving

Motivated by perception-based control problems in autonomous systems, this paper addresses the problem of developing feedback controllers to regulate the inputs and the states of a dynamical system to optimal solutions of an optimization problem when one has no access to exact measurements of the system states. In particular, we consider the case where the states need to be estimated from high-dimensional sensory data received only at discrete time intervals. We develop a sampled-data feedback controller that is based on adaptations of a projected gradient descent method, and that includes neural networks as integral components to estimate the state of the system from perceptual information. We derive sufficient conditions to guarantee (local) input-to-state stability of the control loop. Moreover, we show that the interconnected system tracks the solution trajectory of the underlying optimization problem up to an error that depends on the approximation errors of the neural network and on the time-variability of the optimization problem; the latter originates from time-varying safety and performance objectives, input constraints, and unknown disturbances. As a representative application, we illustrate our results with numerical simulations for vision-based autonomous driving.
Simple Digital Controls from Approximate Plant Models
Authors: Hugh Lachlan Kennedy
Date: 2022-11-17 00:00:00

Two ways of designing low-order discrete-time (i.e. digital) controls for low-order plant (i.e. process) models are considered in this tutorial. The first polynomial method finds the controller coefficients that place the poles of the closed-loop feedback system at specified positions for adroit controls, i.e. for a rapid and compressed transient response, when the plant model is known precisely. The poles and zeros of the resulting controller are unconstrainted, although an integrator may be included in the controller structure as a special case to drive steady-state errors towards zero. The second frequency method ensures that the feedback system has the desired phase-margin at a specified gain cross-over frequency (for the desired bandwidth) yielding robust stability with respect to plant model uncertainty. The poles of the controller are at specified positions, e.g. for a standard Proportional-Integral (PI), Proportional-Derivative (PD), Proportional-Integral-Derivative (PID), structure or other more general configurations if necessary, and the problem is solved for the controller zeros. The poles and zeros of the resulting closed-loop feedback system are unconstrained. These complementary design procedures allow simple and effective controls to be derived analytically from a plant model, using a matrix inverse operation to solve a small set of linear simultaneous equations, as an alternative to more heuristic (e.g. trial-and-error) or empirical PID-tuning approaches. An azimuth controller for a pan-tilt-zoom camera mount is used as an illustrative example. The ways in which both procedures may be used to design controls with the desired balance between adroitness and robustness are discussed.
From LQR to Static Output Feedback: a New LMI Approach
Authors: Luis Rodrigues
Date: 2022-11-17 00:00:00

This paper proposes a new Linear Matrix Inequality (LMI) for static output feedback control assuming that a Linear Quadratic Regulator (LQR) has been previously designed for the system. The main idea is to use a quadratic candidate Lyapunov function for the closed-loop system parameterized by the unique positive definite matrix that solves the Riccati equation. A converse result will also be proved guaranteeing the existence of matrices verifying the LMI if the system is static output feedback stabilizable. The proposed method will then be extended to the design of static output feedback for the H1 control problem. Besides being a sufficient condition for which a converse result is proved, there are another four main advantages of the proposed methodology. First, it is computationally tractable. Second, one can use weighting matrices and obtain a solution in a similar way to LQR design. Third, the proposed method has an extremely simple LMI structure when compared with other LMI methods proposed in the literature. Finally, for the cases where the output is equal to the state it is shown that the LQR solution verifies the proposed LMI. Therefore, the static output feedback includes the LQR solution as a special case when the state is available, which is a desired property. Several examples show that the method is consistently successful and works well in practice.
Tire-road friction estimation and uncertainty assessment to improve electric aircraft braking system
Authors: Francesco Crocetti, G. Costante, M. L. Fravolini, P. Valigi
Date: 2022-11-14 00:00:00
Tasks: Friction

The accurate online estimation of the road-friction coefficient is an essential feature for any advanced brake control system. In this study, a data-driven scheme based on a MLP Neural Net is proposed to estimate the optimum friction coefficient as a function of windowed slip-friction measurements. A stochastic NN weights drop-out mechanism is used to online estimate the confidence interval of the estimated best friction coefficient thus providing a characterization of the epistemic uncertainty associated to the NN block. Open loop and closed loop simulations of the landing phase of an aircraft on an unknown surface are used to show the potentiality and efficacy of the proposed robust friction estimation approach.
Damping analysis of Floating Offshore Wind Turbine (FOWT): a new control strategy reducing the platform vibrations
Authors: Matteo CapaldoO, Paul Mella
Date: 2022-11-13 00:00:00

In this paper, the coupled dynamics of the floating platform and the WTG rotor is analysed. In particular, the damping is explicitly derived from the coupled equations of rotor and floating platform. The analysis of the damping leads to the study of the instability phenomena and it derives the explicit conditions that lead to the Non Minimum Phase Zero (NMPZ). Two NMPZs, one related to the rotor dynamics and the other one to the platform pitch dynamics, are analysed. The latter is a novelty and it is analysed in this work, providing the community of an explicit condition for its verification. The domain of the instability of the platform is explicitly derived from the coupled system of equations. In the second part of the paper, from the analysis of the damping of the floating platform, a new strategy for the control of FOWTs is proposed. This strategy allows one to impose to the controller an explicit level of damping in the platform pitch motion without changing the period of platform pitching. Finally the new strategy is compared to the one without compensation by performing aero-hydro-servo-elastic numerical simulations of the UMaine IEA15MW FOWT. Generated power, movements, blade pitch and tower base fatigue are compared showing that the new control strategy can reduce fatigue in the structure without affecting the power production.
Astrometric Calibration and Source Characterisation of the Latest Generation Neuromorphic Event-based Cameras for Space Imaging
Authors: Nicholas Owen Ralph, Alexandre Marcireau, Saeed Afshar, Nicholas Tothill, André van Schaik, Gregory Cohen
Date: 2022-11-17 00:00:00
Tasks: Astronomy, Camera Calibration

As an emerging approach to space situational awareness and space imaging, the practical use of an event-based camera in space imaging for precise source analysis is still in its infancy. The nature of event-based space imaging and data collection needs to be further explored to develop more effective event-based space image systems and advance the capabilities of event-based tracking systems with improved target measurement models. Moreover, for event measurements to be meaningful, a framework must be investigated for event-based camera calibration to project events from pixel array coordinates in the image plane to coordinates in a target resident space object's reference frame. In this paper, the traditional techniques of conventional astronomy are reconsidered to properly utilise the event-based camera for space imaging and space situational awareness. This paper presents the techniques and systems used for calibrating an event-based camera for reliable and accurate measurement acquisition. These techniques are vital in building event-based space imaging systems capable of real-world space situational awareness tasks. By calibrating sources detected using the event-based camera, the spatio-temporal characteristics of detected sources or `event sources' can be related to the photometric characteristics of the underlying astrophysical objects. Finally, these characteristics are analysed to establish a foundation for principled processing and observing techniques which appropriately exploit the capabilities of the event-based camera.
Fractional integration and cointegration
Authors: Javier Hualde, Morten Ørregaard Nielsen
Date: 2022-11-18 00:00:00
Tasks: Time Series, Time Series Analysis

In this chapter we present an overview of the main ideas and methods in the fractional integration and cointegration literature. We do not attempt to give a complete survey of this enormous literature, but rather a more introductory treatment suitable for a researcher or graduate student wishing to learn about this exciting field of research. With this aim, we have surely overlooked many relevant references for which we apologize in advance. Knowledge of standard time series methods, and in particular methods related to nonstationary time series, at the level of a standard graduate course or advanced undergraduate course is assumed.
A nation-wide experiment: fuel tax cuts and almost free public transport for three months in Germany -- Report 5 Insights into four months of mobility tracking
Authors: Lennart Adenaw, David Ziegler, Nico Nachtigall, Felix Gotzler, Allister Loder, Markus B. Siewert, Markus Lienkamp, Klaus Bogenberger
Date: 2022-11-18 00:00:00

In spring 2022, the German federal government agreed on a set of measures that aim at reducing households' financial burden resulting from a recent price increase, especially in energy and mobility. These measures include among others, a nation-wide public transport ticket for 9 EUR per month and a fuel tax cut that reduces fuel prices by more than 15%. In transportation research this is an almost unprecedented behavioral experiment. It allows to study not only behavioral responses in mode choice and induced demand but also to assess the effectiveness of transport policy instruments. We observe this natural experiment with a three-wave survey and an app-based travel diary on a sample of hundreds of participants as well as an analysis of traffic counts. In this fifth report, we present first analyses of the recorded tracking data. 910 participants completed the tracking until September, 30th. First, an overview over the socio-demographic characteristics of the participants within our tracking sample is given. We observe an adequate representation of female and male participants, a slight over-representation of young participants, and an income distribution similar to the one known from the "Mobilit\"at in Deutschland" survey. Most participants of the tracking study live in Munich, Germany. General transportation statistics are derived from the data for all phases of the natural experiment - prior, during, and after the 9 EUR-Ticket - to assess potential changes in the participants' travel behavior on an aggregated level. A significant impact of the 9 EUR-Ticket on modal shares can be seen. An analysis of the participants' mobility behavior considering trip purposes, age, and income sheds light on how the 9 EUR-Ticket impacts different social groups and activities. We find that age, income, and trip purpose significantly influence the impact of the 9 EUR-Ticket on the observed modal split.
Effective and scalable programs to facilitate labor market transitions for women in technology
Authors: Susan Athey, Emil Palikot
Date: 2022-11-18 00:00:00
Tasks: counterfactual

We describe the design, implementation, and evaluation of a low-cost (approximately $15 per person) and scalable program, called Challenges, aimed at aiding women in Poland transition to technology-sector jobs. This program helps participants develop portfolios demonstrating job-relevant competencies. We conduct two independent evaluations, one of the Challenges program and the other of a traditional mentoring program -- Mentoring -- where experienced tech professionals work individually with mentees to support them in their job search. Exploiting the fact that both programs were oversubscribed, we randomized admissions and measured their impact on the probability of finding a job in the technology sector. We estimate that Mentoring increases the probability of finding a technology job within four months from 29% to 42% and Challenges from 20% to 29%, and the treatment effects do not attenuate over 12 months. Since both programs are capacity constrained in practice (only 28% of applicants can be accommodated), we evaluate the effectiveness of several alternative prioritization rules based on applicant characteristics. We find that a policy that selects applicants based on their predicted treatment effects increases the average treatment effect across the two programs to 22 percentage points. We further analyze how alternative prioritization rules compare to the selection that mentors used. We find that mentors selected applicants who were more likely to get a tech job even without participating in the program, and the treatment effect for applicants with similar characteristics to those selected by mentors is about half of the effect attainable when participants are prioritized optimally.
The Texas Shootout under Uncertainty
Authors: Gerrit Bauch, Frank Riedel
Date: 2022-11-18 00:00:00

We investigate the allocation of a co-owned company to a single owner using the Texas Shoot-Out mechanism with private valuations. We identify Knightian Uncertainty about the peer's distribution as a reason for its deterrent effect of a premature dissolving. Modeling uncertainty by a distribution band around a reference distribution $F$, we derive the optimal price announcement for an ambiguity averse divider. The divider hedges against uncertainty for valuations close to the median of $F$, while extracting expected surplus for high and low valuations. The outcome of the mechanism is efficient for valuations around the median. A risk neutral co-owner prefers to be the chooser, even strictly so for any valuation under low levels of uncertainty and for extreme valuations under high levels of uncertainty. If valuations are believed to be close, less uncertainty is required for the mechanism to always be efficient and reduce premature dissolvements.
Generative Model Based Highly Efficient Semantic Communication Approach for Image Transmission
Authors: Tianxiao Han, Jiancheng Tang, Qianqian Yang, Yiping Duan, Zhaoyang Zhang, Zhiguo Shi
Date: 2022-11-18 00:00:00
Tasks: Semantic Communication

Deep learning (DL) based semantic communication methods have been explored to transmit images efficiently in recent years. In this paper, we propose a generative model based semantic communication to further improve the efficiency of image transmission and protect private information. In particular, the transmitter extracts the interpretable latent representation from the original image by a generative model exploiting the GAN inversion method. We also employ a privacy filter and a knowledge base to erase private information and replace it with natural features in the knowledge base. The simulation results indicate that our proposed method achieves comparable quality of received images while significantly reducing communication costs compared to the existing methods.
Tractography-Based Parcellation of Cerebellar Dentate Nuclei via a Deep Nonnegative Matrix Factorization Clustering Method
Authors: Xiao Xu, Yuqian Chen, Leo Zekelman, Yogesh Rathi, Nikos Makris, Fan Zhang, Lauren J. O'Donnell
Date: 2022-11-18 00:00:00
Tasks: Clustering, Diffusion  MRI

As the largest human cerebellar nucleus, the dentate nucleus (DN) functions significantly in the communication between the cerebellum and the rest of the brain. Structural connectivity-based parcellation has the potential to reveal the topography of the DN and enable the study of its subregions. In this paper, we investigate a deep nonnegative matrix factorization clustering method (DNMFC) for parcellation of the human DN based on its structural connectivity using diffusion MRI tractography. We propose to describe the connectivity of the DN using a set of curated tractography fiber clusters within the cerebellum. Experiments are conducted on the diffusion MRI data of 50 healthy adults from the Human Connectome Project. In comparison with state-of-the-art clustering methods, DN parcellations resulting from DNMFC show better quality and consistency of parcels across subjects.
Efficient Feature Compression for Edge-Cloud Systems
Authors: Zhihao Duan, Fengqing Zhu
Date: 2022-11-17 00:00:00
Tasks: Classification, Feature Compression, image-classification, Image Classification

Optimizing computation in an edge-cloud system is an important yet challenging problem. In this paper, we consider a three-way trade-off between bit rate, classification accuracy, and encoding complexity in an edge-cloud image classification system. Our method includes a new training strategy and an efficient encoder architecture to improve the rate-accuracy performance. Our design can also be easily scaled according to different computation resources on the edge device, taking a step towards achieving a rate-accuracy-complexity (RAC) trade-off. Under various settings, our feature coding system consistently outperforms previous methods in terms of the RAC performance.
Adaptive De-noising of Photoacoustic Signal and Image based on Modified Kalman Filter
Authors: Tianqu Hu, Zihao Huang, Peng Ge, Feng Gao, Fei Gao
Date: 2022-11-18 00:00:00
Tasks: Diagnostic

As a burgeoning medical imaging method based on hybrid fusion of light and ultrasound, photoacoustic imaging (PAI) has demonstrated high potential in various biomedical applications recently, especially in revealing the functional and molecular information to improve diagnostic accuracy. However, stemming from weak amplitude and unavoidable random noise, caused by limited laser power and severe attenuation in deep tissue imaging, PA signals are usually of low signal-to-noise ratio (SNR), and reconstructed PA images are of low quality. Despite that conventional Kalman Filter (KF) can remove Gaussian noise in time domain, it lacks adaptability in real-time estimating condition due to its fixed model. Moreover, KF-based de-noising algorithm has not been applied in PAI before. In this paper, we propose an adaptive Modified Kalman Filter (MKF) targeted at PAI de-noising by tuning system noise matrix Q and measurement noise matrix R in the conventional KF model. Additionally, in order to compensate the signal skewing caused by KF, we cascade the backward part of Rauch-Tung-Striebel smoother (BRTS), which also utilizes the newly determined Q. Finally, as a supplement, we add a commonly used differential filter to remove in-band reflection artifacts. Experimental results using phantom and ex vivo colorectal tissue are provided to prove the validity of the algorithm.
DASECount: Domain-Agnostic Sample-Efficient Wireless Indoor Crowd Counting via Few-shot Learning
Authors: Huawei Hou, Suzhi Bi, Lili Zheng, Xiaohui Lin, Yuan Wu, Zhi Quan
Date: 2022-11-18 00:00:00
Tasks: Crowd Counting, Few-Shot Learning, Knowledge Distillation

Accurate indoor crowd counting (ICC) is a key enabler to many smart home/office applications. In this paper, we propose a Domain-Agnostic and Sample-Efficient wireless indoor crowd Counting (DASECount) framework that suffices to attain robust cross-domain detection accuracy given very limited data samples in new domains. DASECount leverages the wisdom of few-shot learning (FSL) paradigm consisting of two major stages: source domain meta training and target domain meta testing. Specifically, in the meta-training stage, we design and train two separate convolutional neural network (CNN) modules on the source domain dataset to fully capture the implicit amplitude and phase features of CSI measurements related to human activities. A subsequent knowledge distillation procedure is designed to iteratively update the CNN parameters for better generalization performance. In the meta-testing stage, we use the partial CNN modules to extract low-dimension features out of the high-dimension input target domain CSI data. With the obtained low-dimension CSI features, we can even use very few shots of target domain data samples (e.g., 5-shot samples) to train a lightweight logistic regression (LR) classifier, and attain very high cross-domain ICC accuracy. Experiment results show that the proposed DASECount method achieves over 92.68\%, and on average 96.37\% detection accuracy in a 0-8 people counting task under various domain setups, which significantly outperforms the other representative benchmark methods considered.