pwc-archive/papers-with-abstracts huggingface dataset:
Dual adaptive MPC using an exact set-membership reformulation
Authors: Anilkumar Parsi, Diyou Liu, Andrea Iannelli, Roy S. Smith
Date: 2022-11-29 00:00:00
Tasks: Model Predictive Control

Adaptive model predictive control (MPC) methods using set-membership identification to reduce parameter uncertainty are considered in this work. Strong duality is used to reformulate the set-membership equations exactly within the MPC optimization. A predicted worst-case cost is then used to enable performance-oriented exploration. The proposed approach guarantees robust constraint satisfaction and recursive feasibility. It is shown that method can be implemented using homothetic tube and flexible tube parameterizations of state tubes, and a simulation study demonstrates performance improvement over state-of-the-art controllers.
Lettuce modelling for growth control in precision agriculture
Authors: William Rohde, Fulvio Forni
Date: 2022-11-29 00:00:00

Improving the efficiency of agriculture is a growing priority due to food security issues, environmental concerns, and economics. Precision agriculture and variable rate application technology could enable increases in yield while maintaining or reducing fertiliser use. However, this requires the development of control algorithms which are suitable for the challenges of agriculture. In this paper, we propose a new mechanistic open model of lettuce growth for use in control of precision agriculture. We demonstrate that our model is cooperative and fits well to experimental data. We use the model to show, via simulations, that a simple proportional distributed control law increases crop uniformity and yield without increasing nitrogen use, even in the presence of sparse actuation and noisy observations.
Advisory Tool for Managing Failure Cascades in Systems with Wind Power
Authors: Siyu Liu, Marija Ilic
Date: 2022-11-29 00:00:00

This paper concerns the resilience of systems with wind power upon wind reduction by evaluating the potential of corrective actions, such as generation and load dispatch, on minimizing the effects of transmission line failures. Three functions (grid, consumer-centric loss, and resilience impact) are used to statistically evaluate the criticality of initial contingent failures and wind reductions. Our model is learned with Monte Carlo, convex optimization, and adaptive selection, illustrated on the IEEE-30 and IEEE-300 bus systems with both AC and DC models. We highlight the impact of wind reductions and propose physically implementable solutions.
Frequency Domain Gaussian Process Models for $H^\infty$ Uncertainties
Authors: Alex Devonport, Peter Seiler, Murat Arcak
Date: 2022-11-29 00:00:00
Tasks: Gaussian Processes, regression

Complex-valued Gaussian processes are used in Bayesian frequency-domain system identification as prior models for regression. If each realization of such a process were an $H_\infty$ function with probability one, then the same model could be used for probabilistic robust control, allowing for robustly safe learning. We investigate sufficient conditions for a general complex-domain Gaussian process to have this property. For the special case of processes whose Hermitian covariance is stationary, we provide an explicit parameterization of the covariance structure in terms of a summable sequence of nonnegative numbers.
ICS-CTM2: Industrial Control System Cybersecurity Testbed Maturity Model
Authors: Souradeep Bhattacharya, Burhan Hyder, Manimaran Govindarasu
Date: 2022-11-29 00:00:00

Industrial Control System (ICS) testbeds serve as a platform for evaluating and validating control system performances, cybersecurity tools and technologies. In order to build or enhance an ICS testbed, it is vital to have a deeper understanding of its design specifications and characteristic attributes. Satisfying this prerequisite involves examination and assessment of these attributes for existing testbeds. To further increase confidence in a testbed's functionality, it is important to perform a comparative analysis of its specifications with other ICS testbeds. However, at present, there is no standardized methodology available to provide a comparative assessment of different testbeds. In this paper, we propose a methodology for analyzing ICS testbeds, inspired by the Cybersecurity Capability Maturity Model (C2M2). In particular, we then define a ICS Cybersecurity Testbed Maturity Model, its domains, and the associated maturity indicator levels. To demonstrate the benefit of the model, we have conducted a case study analysis for several ICS testbeds, representing different industrial sectors. Our analysis provides deeper insights into the relative strengths and limitations of these testbeds, together with scope for future enhancements, with respect to the domains defined by the model.
Vertical Airborne Wind Energy Farms with High Power Density per Ground Area based on Multi-Aircraft Systems
Authors: Jochem De Schutter, Jakob Harzer, Moritz Diehl
Date: 2022-11-28 00:00:00

This paper proposes and simulates vertical airborne wind energy (AWE) farms based on multi-aircraft systems with high power density (PD) per ground area. These farms consist of many independently ground located systems that are flying at the same inclination angle, but with different tether lengths, such that all aircraft fly in a large planar elliptical area that is vertical to the tethers. The individual systems are assigned non-overlapping flight cylinders depending on the wind direction. Detailed calculations that take into account Betz' limit, assuming a cubically averaged wind power density of 7 m/s, give a potential yearly average PD of 43 MW/km$^2$. A conventional wind farm with typical packing density would yield a PD of 2.4 MW/km$^2$ in the same wind field. More refined simulations using optimal control result in a more modest PD of 6 MW/km$^2$ for practically recommended flight trajectories. This PD can already be achieved with small-scale aircraft with a wing span of 5.5 m. The simulations additionally show that the achievable PD is more than an order of magnitude higher than for a single-aircraft AWE system with the same wing span.
Exploiting STAR-RISs in Near-Field Communications
Authors: Jiaqi Xu, Xidong Mu, Yuanwei Liu
Date: 2022-11-28 00:00:00

The reconfigurable intelligent surface (RIS) is a promising technology to provide smart radio environment. In contrast to the well-studied patch-array-based RISs, this work focuses on the metasurface-based RISs and simultaneously transmitting and reflecting (STAR)-RISs where the elements have millimeter or even molecular sizes. For these meticulous metasurface structures, near-field effects are dominant and a continuous electric current distribution should be adopted for capturing their electromagnetic response instead of discrete phase-shift matrices. Exploiting the electric current distribution, a Green's function method based channel model is proposed. Based on the proposed model, performance analysis is carried out for RISs and STAR-RISs. 1) For the transmitting/reflecting-only RIS-aided single-user scenario, closed-formed expressions for the near-field/far-field boundary and the end-to-end channel gain are derived. Then, degrees-of-freedom (DoFs) and the power scaling laws are obtained. It is proved that the near-field channel exhibits higher DoFs than the far-field channel. It is also confirmed that when communication distance increases beyond the field boundary, the near-field power scaling law degrades to the well-known far-field result. 2) For the STAR-RIS-aided multi-user scenario, three practical STAR-RIS configuration strategies are proposed, namely power splitting (PS), selective element grouping (SEG), and random element grouping (REG) strategies. The channel gains for users are derived within both the pure near-field regime and the hybrid near-field and far-field regime. Finally, numerical results confirm that: 1) for metasurface-based RISs, the field boundary depends on the sizes of both the RIS and the receiver, 2) the received power scales quadratically with the number of elements within the far-field regime and scales linearly within the near-field regime.
Model-based Reconstruction for Multi-Frequency Collimated Beam Ultrasound Systems
Authors: Abdulrahman M. Alanazi, Singanallur Venkatakrishnan, Hector Santos-Villalobos, Gregery T. Buzzard, Charles Bouman
Date: 2022-11-29 00:00:00

Collimated beam ultrasound systems are a technology for imaging inside multi-layered structures such as geothermal wells. These systems work by using a collimated narrow-band ultrasound transmitter that can penetrate through multiple layers of heterogeneous material. A series of measurements can then be made at multiple transmit frequencies. However, commonly used reconstruction algorithms such as Synthetic Aperture Focusing Technique (SAFT) tend to produce poor quality reconstructions for these systems both because they do not model collimated beam systems and they do not jointly reconstruct the multiple frequencies. In this paper, we propose a multi-frequency ultrasound model-based iterative reconstruction (UMBIR) algorithm designed for multi-frequency collimated beam ultrasound systems. The combined system targets reflective imaging of heterogeneous, multi-layered structures. For each transmitted frequency band, we introduce a physics-based forward model to accurately account for the propagation of the collimated narrow-band ultrasonic beam through the multi-layered media. We then show how the joint multi-frequency UMBIR reconstruction can be computed by modeling the direct arrival signals, detector noise, and incorporating a spatially varying image prior. Results using both simulated and experimental data indicate that multi-frequency UMBIR reconstruction yields much higher reconstruction quality than either single frequency UMBIR or SAFT.
Score-based calibration testing for multivariate forecast distributions
Authors: Malte Knüppel, Fabian Krüger, Marc-Oliver Pohle
Date: 2022-11-29 00:00:00
Tasks: Time Series, Time Series Analysis

Calibration tests based on the probability integral transform (PIT) are routinely used to assess the quality of univariate distributional forecasts. However, PIT-based calibration tests for multivariate distributional forecasts face various challenges. We propose two new types of tests based on proper scoring rules, which overcome these challenges. They arise from a general framework for calibration testing in the multivariate case, introduced in this work. The new tests have good size and power properties in simulations and solve various problems of existing tests. We apply the tests to forecast distributions for macroeconomic and financial time series data.
Bayesian Multivariate Quantile Regression with alternative Time-varying Volatility Specifications
Authors: Matteo Iacopini, Francesco Ravazzolo, Luca Rossini
Date: 2022-11-29 00:00:00
Tasks: quantile regression, regression

This article proposes a novel Bayesian multivariate quantile regression to forecast the tail behavior of energy commodities, where the homoskedasticity assumption is relaxed to allow for time-varying volatility. In particular, we exploit the mixture representation of the multivariate asymmetric Laplace likelihood and the Cholesky-type decomposition of the scale matrix to introduce stochastic volatility and GARCH processes and then provide an efficient MCMC to estimate them. The proposed models outperform the homoskedastic benchmark mainly when predicting the distribution's tails. We provide a model combination using a quantile score-based weighting scheme, which leads to improved performances, notably when no single model uniformly outperforms the other across quantiles, time, or variables.
Geographical balancing of wind power decreases storage needs in a 100% renewable European power sector
Authors: Alexander Roth, Wolf-Peter Schill
Date: 2022-11-29 00:00:00

To reduce greenhouse gas emissions, many countries plan to massively expand wind power and solar photovoltaic capacities. These variable renewable energy sources require additional flexibility in the power sector. Both geographical balancing enabled by interconnection and electricity storage can provide such flexibility. In a 100% renewable energy scenario of twelve central European countries, we investigate how geographical balancing between countries reduces the need for electricity storage. Our principal contribution is to separate and quantify the different factors at play. Applying a capacity expansion model and a factorization method, we disentangle the effect of interconnection on optimal storage capacities through distinct factors: differences in countries' solar PV and wind power availability patterns, load profiles, as well as hydropower and bioenergy capacity portfolios. Results show that interconnection reduces storage needs by around 30% in contrast to a scenario without interconnection. Differences in wind power profiles between countries explain around 80% of that effect.
Unified Container Shipping Industry Data From 1966: Freight Rate, Shipping Quantity, Newbuilding, Secondhand, and Scrap Price
Authors: Takuma Matsuda, Suguru Otani
Date: 2022-11-29 00:00:00
Tasks: Descriptive

We construct a new unified panel dataset that combines route-year-level freight rates with shipping quantities for the six major routes and industry-year-level newbuilding, secondhand, and scrap prices from 1966 (the beginning of the industry) to 2009. We offer detailed instructions on how to merge various datasets and validate the data's consistency by industry experts and former executives who have historical knowledge and experience. Using this dataset, we provide a quantitative and descriptive analysis of the industry dynamics known as the container crisis. Finally, we identify structural breaks for each variable to demonstrate the impact of the shipping cartels' collapse.
Repeat Voting: Two-Vote May Lead More People To Vote
Authors: Sergiu Hart
Date: 2022-11-29 00:00:00
Tasks: Vocal Bursts Valence Prediction

A "repeat voting" procedure is proposed, whereby voting is carried out in two identical rounds. Every voter can vote in each round, the results of the first round are made public before the second round, and the final result is determined by adding up all the votes in both rounds. It is argued that this simple modification of election procedures may well increase voter participation and result in more accurate and representative outcomes.
Motion Magnification Algorithms for Video-Based Breathing Monitoring
Authors: Veronica Mattioli, Davide Alinovi, Gianluigi Ferrari, Francesco Pisani, Riccardo Raheli
Date: 2022-11-29 00:00:00
Tasks: Motion Magnification

In this paper, we present two video processing techniques for contact-less estimation of the Respiratory Rate (RR) of framed subjects. Due to the modest extent of movements related to respiration in both infants and adults, specific algorithms to efficiently detect breathing are needed. For this reason, motion-related variations in video signals are exploited to identify respiration of the monitored patient and simultaneously estimate the RR over time. Our estimation methods rely on two motion magnification algorithms that are exploited to enhance the subtle respiration-related movements. In particular, amplitude- and phase-based algorithms for motion magnification are considered to extract reliable motion signals. The proposed estimation systems perform both spatial decomposition of the video frames combined with proper temporal filtering to extract breathing information. After periodic (or quasi-periodic) respiratory signals are extracted and jointly analysed, we apply the Maximum Likelihood (ML) criterion to estimate the fundamental frequency, corresponding to the RR. The performance of the presented methods is first assessed by comparison with reference data. Videos framing different subjects, i.e., newborns and adults, are tested. Finally, the RR estimation accuracy of both methods is measured in terms of normalized Root Mean Squared Error (RMSE).
Shadow-Oriented Tracking Method for Multi-Target Tracking in Video-SAR
Authors: Xiaochuan Ni, Xiaoling Zhang, Xu Zhan, Zhenyu Yang, Jun Shi, Shunjun Wei, Tianjiao Zeng
Date: 2022-11-29 00:00:00

This work focuses on multi-target tracking in Video synthetic aperture radar. Specifically, we refer to tracking based on targets' shadows. Current methods have limited accuracy as they fail to consider shadows' characteristics and surroundings fully. Shades are low-scattering and varied, resulting in missed tracking. Surroundings can cause interferences, resulting in false tracking. To solve these, we propose a shadow-oriented multi-target tracking method (SOTrack). To avoid false tracking, a pre-processing module is proposed to enhance shadows from surroundings, thus reducing their interferences. To avoid missed tracking, a detection method based on deep learning is designed to thoroughly learn shadows' features, thus increasing the accurate estimation. And further, a recall module is designed to recall missed shadows. We conduct experiments on measured data. Results demonstrate that, compared with other methods, SOTrack achieves much higher performance in tracking accuracy-18.4%. And ablation study confirms the effectiveness of the proposed modules.
GreenMO: Virtualized User-proportionate MIMO
Authors: Agrim Gupta, Sajjad Nassirpour, Manideep Dunna, Eamon Patamasing, Alireza Vahid, Dinesh Bharadia
Date: 2022-11-29 00:00:00

With the turn of new decade, wireless communications face a major challenge on connecting many more new users and devices, at the same time being energy efficient and minimizing its carbon footprint. However, the current approaches to address the growing number of users and spectrum demands, like traditional fully digital architectures for Massive MIMO, demand exorbitant energy consumption. The reason is that traditionally MIMO requires a separate RF chain per antenna, so the power consumption scales with number of antennas, instead of number of users, hence becomes energy inefficient. Instead, GreenMO creates a new massive MIMO architecture which is able to use many more antennas while keeping power consumption to user-proportionate numbers. To achieve this GreenMO introduces for the first time, the concept of virtualization of the RF chain hardware. Instead of laying the RF chains physically to each antenna, GreenMO creates these RF chains virtually in digital domain. This also enables GreenMO to be the first flexible massive MIMO architecture. Since GreenMO's virtual RF chains are created on the fly digitally, it can tune the number of these virtual chains according to the user load, hence always flexibly consume user-proportionate power. Thus, GreenMO paves the way for green and flexible massive MIMO. We prototype GreenMO on a PCB with eight antennas and evaluate it with a WARPv3 SDR platform in an office environment. The results demonstrate that GreenMO is 3x more power-efficient than traditional Massive MIMO and 4x more spectrum-efficient than traditional OFDMA systems, while multiplexing 4 users, and can save upto 40% power in modern 5G NR base stations.
Load Profile Inpainting for Missing Load Data Restoration and Baseline Estimation
Authors: Yiyan Li, Lidong Song, Yi Hu, Hanpyo Lee, Di wu, PJ Rehm, Ning Lu
Date: 2022-11-29 00:00:00
Tasks: Time Series, Time Series Analysis

This paper introduces a Generative Adversarial Nets (GAN) based, Load Profile Inpainting Network (Load-PIN) for restoring missing load data segments and estimating the baseline for a demand response event. The inputs are time series load data before and after the inpainting period together with explanatory variables (e.g., weather data). We propose a Generator structure consisting of a coarse network and a fine-tuning network. The coarse network provides an initial estimation of the data segment in the inpainting period. The fine-tuning network consists of self-attention blocks and gated convolution layers for adjusting the initial estimations. Loss functions are specially designed for the fine-tuning and the discriminator networks to enhance both the point-to-point accuracy and realisticness of the results. We test the Load-PIN on three real-world data sets for two applications: patching missing data and deriving baselines of conservation voltage reduction (CVR) events. We benchmark the performance of Load-PIN with five existing deep-learning methods. Our simulation results show that, compared with the state-of-the-art methods, Load-PIN can handle varying-length missing data events and achieve 15-30% accuracy improvement.
MmWave Mapping and SLAM for 5G and Beyond
Authors: Yu Ge, Ossi Kaltiokallio, Hyowon Kim, Jukka Talvitie, Sunwoo Kim, Lennart Svensson, Mikko Valkama, Henk Wymeersch
Date: 2022-11-29 00:00:00
Tasks: Integrated sensing and communication, Simultaneous Localization and Mapping

Device localization and radar-like mapping are at the heart of integrated sensing and communication, enabling not only new services and applications, but can also improve communication quality with reduced overheads. These forms of sensing are however susceptible to data association problems, due to the unknown relation between measurements and detected objects or targets. In this chapter, we provide an overview of the fundamental tools used to solve mapping, tracking, and simultaneous localization and mapping (SLAM) problems. We distinguish the different types of sensing problems and then focus on mapping and SLAM as running examples. Starting from the applicable models and definitions, we describe the different algorithmic approaches, with a particular focus on how to deal with data association problems. In particular, methods based on random finite set theory and Bayesian graphical models are introduced in detail. A numerical study with synthetic and experimental data is then used to compare these approaches in a variety of scenarios.
H-bonds in Crambin: Coherence in an alpha helix
Authors: Stanley Nicholson, David Minh, Robert Eisenberg
Date: 2022-11-29 00:00:00

We applied coherence analysis to molecular dynamics simulations of the plant protein crambin, a thionin storage protein found in Abyssinian cabbage. Coherence analysis was developed by engineers to identify linear interactions, without statistical assumptions. Coherence is greater than 0.9 between the displacement of oxygen and nitrogen atoms of H bonds in alpha helices for frequencies between 0.391 GHz and 5.08 GHz (corresponding reciprocally to times of 2.56 ns and 0.197 ns). These H bonds act much like a linear system. Unrelated atoms have uncorrelated motions and much smaller coherence, say 0.02. Groups of atoms (that form a layer of an alpha helix) were averaged and the coherence function of two groups was evaluated. Layers of the alpha helix form a linear system, suggesting that the harmonic analysis of classical molecular dynamics can successfully describe the allosteric interactions of the layers of an alpha helix.