pwc-archive/papers-with-abstracts huggingface dataset:
Co-optimization of Battery Routing and Load Restoration for Microgrids with Mobile Energy Storage Systems
Authors: Shourya Bose, Sifat Chowdhury, Yu Zhang
Date: 2022-05-24 00:00:00

Mobile energy storage systems (MESS) offer great operational flexibility to enhance the resiliency of distribution systems in an emergency condition. The optimal placement and sizing of those units are pivotal for quickly restoring the curtailed loads. In this paper, we propose a model for load restoration in a microgrid while concurrently optimizing the MESS routes required for the same. The model is formulated as a mixed integer second order cone program by considering the state of charge and evolution of the lower and upper bounds of battery capacities. Simulation results tested on the IEEE 123- bus benchmark system demonstrate the efficacy of the proposed model.
Comparison of Fractional-Order and Integer-Order H-infinty Control of a Non-Collocated Two-Mass Oscillator
Authors: Benjamin Voß, Michael Ruderman, Christoph Weise, Johann Reger
Date: 2022-05-24 00:00:00

We consider the robust control of a two-mass oscillator with a dominant input delay. Our aim is to compare a fractional-order tuning approach including the partial compensation of non-minimum phase zeros with a classical H-infinity loop-shaping design, since both these designs lead to a relatively high controller order. First of all a detailed physical model is derived and validated using measurement data. Based on the linearized model both controllers are designed to be comparable, i.e. they show a similar crossover frequency in the open loop and the final controller order is reduced to the same range for both designs. The major differences between both are the different methods how the feed-forward action is included. The loop-shaping approach with fractional-order elements relies on the plant inverse using a flat output, whereas the H-infinty design incorporates a two-degree of freedom control, i.e. the reference signal is included into the known inputs of the generalized plant. Each controller is tested in simulation and experiment. As both open-loops are nearly identical in the frequency range of interest, the results from an input disturbance experiment show no major difference. The different design approaches of the feed-forward path are clearly visible in the tracking experiment.
Stability in data-driven MPC: an inherent robustness perspective
Authors: Julian Berberich, Johannes Köhler, Matthias A. Müller, Frank Allgöwer
Date: 2022-05-24 00:00:00
Tasks: LEMMA, Model Predictive Control

Data-driven model predictive control (DD-MPC) based on Willems' Fundamental Lemma has received much attention in recent years, allowing to control systems directly based on an implicit data-dependent system description. The literature contains many successful practical applications as well as theoretical results on closed-loop stability and robustness. In this paper, we provide a tutorial introduction to DD-MPC for unknown linear time-invariant (LTI) systems with focus on (robust) closed-loop stability. We first address the scenario of noise-free data, for which we present a DD-MPC scheme with terminal equality constraints and derive closed-loop properties. In case of noisy data, we introduce a simple yet powerful approach to analyze robust stability of DD-MPC by combining continuity of DD-MPC w.r.t. noise with inherent robustness of model-based MPC, i.e., robustness of nominal MPC w.r.t. small disturbances. Moreover, we discuss how the presented proof technique allows to show closed-loop stability of a variety of DD-MPC schemes with noisy data, as long as the corresponding model-based MPC is inherently robust.
Sensing Performance of Multi-Channel RFID-based Finger Augmentation Devices for Tactile Internet
Authors: Federica Naccarata, Giulio Maria Bianco, Gaetano Marrocco
Date: 2022-05-24 00:00:00
Tasks: FAD

Radiofrequency finger augmentation devices (R-FADs) are a recently introduced class of epidermal radiofrequency identification (RFID) sensor-tags attached to the fingers, communicating with a body-worn reader. These devices are promising candidates to enable Tactile Internet (TI) applications in the short term. R-FAD based on auto-tuning RFID microchips can be used as dielectric probes for the material of touched objects. However, due to the nearly unpredictable intrinsic variability of finger-object interaction, a single sensorized finger (single-channel device) is not enough to guarantee reliable data sampling. These limitations can be overcome by exploiting a multi-channel R-FAD sensorizing multiple fingers of the hand. In this paper, the dielectric-sensing performance of a multi-channel R-FAD, composed of sensors encapsulated into soft elastomers, is numerically and experimentally characterized, involving a set of volunteers. The inter-sensor coupling is negligible, thus enabling simultaneous independent dielectric measurements. The multi-sensor configuration allows for 100% reliability of the on-hand communication link for touched objects in a wide range of permittivity. Experiments moreover demonstrate that multi-channel measurements can halve the measurement uncertainty of the single-channel case. The achievable precision is suitable to discriminate among low-, medium-, and high-permittivity materials.
Robotic agricultural instrument for automated extraction of nematode cysts and eggs from soil to improve integrated pest management
Authors: Christopher M. Legner, Gregory L. Tylka, Santosh Pandey
Date: 2022-05-24 00:00:00
Tasks: Diagnostic, Management

Soybeans are an important crop for global food security. Every year, soybean yields are reduced by numerous soybean diseases, particularly the soybean cyst nematode (SCN). It is difficult to visually identify the presence of SCN in the field, let alone its population densities or numbers, as there are no obvious aboveground disease symptoms. The only definitive way to assess SCN population densities is to directly extract the SCN cysts from soil and then extract the eggs from cysts and count them. Extraction is typically conducted in commercial soil analysis laboratories and university plant diagnostic clinics and involves repeated steps of sieving, washing, collecting, grinding, and cleaning. Here we present a robotic instrument to reproduce and automate the functions of the conventional methods to extract nematode cysts from soil and subsequently extract eggs from the recovered nematode cysts. We incorporated mechanisms to actuate the stage system, manipulate positions of individual sieves using the gripper, recover cysts and cyst-sized objects from soil suspended in water, and grind the cysts to release their eggs. All system functions are controlled and operated by a touchscreen interface software. The performance of the robotic instrument is evaluated using soil samples infested with SCN from two farms at different locations and results were comparable to the conventional technique. Our new technology brings the benefits of automation to SCN soil diagnostics, a step towards long-term integrated pest management of this serious soybean pest.
Trends in Workplace Wearable Technologies and Connected-Worker Solutions for Next-Generation Occupational Safety, Health, and Productivity
Authors: Vishal Patel, Austin Chesmore, Christopher M. Legner, Santosh Pandey
Date: 2022-05-24 00:00:00
Tasks: Management

The workplace influences the safety, health, and productivity of workers at multiple levels. To protect and promote total worker health, smart hardware, and software tools have emerged for the identification, elimination, substitution, and control of occupational hazards. Wearable devices enable constant monitoring of individual workers and the environment, whereas connected worker solutions provide contextual information and decision support. Here, the recent trends in commercial workplace technologies to monitor and manage occupational risks, injuries, accidents, and diseases are reviewed. Workplace safety wearables for safe lifting, ergonomics, hazard identification, sleep monitoring, fatigue management, and heat and cold stress are discussed. Examples of workplace productivity wearables for asset tracking, augmented reality, gesture and motion control, brain wave sensing, and work stress management are given. Workplace health wearables designed for work-related musculoskeletal disorders, functional movement disorders, respiratory hazards, cardiovascular health, outdoor sun exposure, and continuous glucose monitoring are shown. Connected worker platforms are discussed with information about the architecture, system modules, intelligent operations, and industry applications. Predictive analytics provide contextual information about occupational safety risks, resource allocation, equipment failure, and predictive maintenance. Altogether, these examples highlight the ground-level benefits of real-time visibility about frontline workers, work environment, distributed assets, workforce efficiency, and safety compliance
A fuzzy feedback linearization scheme applied to vibration control of a smart structure
Authors: Roberta Varela de Albuquerque Herôncio, João Deodato Batista dos Santos, Wallace Moreira Bessa, Aline Souza de Paula, Marcelo Amorim Savi
Date: 2022-05-23 00:00:00

Smart structures are usually designed with a stimulus-response mechanism to mimic the autoregulatory process of living systems. In this work, in order to simulate this natural and self-adjustable behavior, a fuzzy feedback linearization scheme is applied to a shape memory two-bar truss. This structural system exhibits both constitutive and geometrical nonlinearities presenting the snap-through behavior and chaotic dynamics. On this basis, a nonlinear controller is employed for vibration suppression in the chaotic smart truss. The control scheme is primarily based on feedback linearization and enhanced by a fuzzy inference system to cope with modeling inaccuracies and external disturbances. The overall control system performance is evaluated by means of numerical simulations, promoting vibration reduction and avoiding snap-through behavior.
A framework for the development of intelligent mechanical systems
Authors: Wallace M. Bessa
Date: 2022-05-23 00:00:00
Tasks: Self-Driving Cars

From autonomous vacuum cleaners to self-driving cars, intelligent mechanical systems are becoming an intrinsic part of our daily lives. In this work, a framework for the development of intelligent mechanical systems is presented.Considering that in this scenario the adopted control approach plays an essential role, I show that the proposed scheme should be able to not only adapt itself to changes in the environment, but also learn from its own experience.
An intelligent controller for underactuated mechanical systems
Authors: Josiane Maria de Macedo Fernande, Marcelo Costa Tanaka, Wallace Moreira Bessa, Edwin Kreuzer
Date: 2022-05-23 00:00:00

This paper presents an intelligent controller for uncertain underactuated nonlinear systems. The adopted approach is based on sliding mode control and enhanced by an artificial neural network to cope with modeling inaccuracies and external disturbances that can arise. The sliding surfaces are defined as a linear combination of both actuated and unactuated variables. A radial basis function is added to compensate the performance drop when, in order to avoid the chattering phenomenon, the sign function is substituted by a saturation function in the conventional sliding mode controller. An application of the proposed scheme is introduced for an inverted pendulum, in order to illustrate the controller design method, and numerical results are presented to demonstrate the improved performance of the resulting intelligent controller.
Data-Driven Learning of Safety-Critical Control with Stochastic Control Barrier Functions
Authors: Chuanzheng Wang, Yiming Meng, Stephen L. Smith, Jun Liu
Date: 2022-05-22 00:00:00

Control barrier functions are widely used to synthesize safety-critical controls. The existence of Gaussian-type noise may lead to unsafe actions and result in severe consequences. While studies are widely done in safety-critical control for stochastic systems, in many real-world applications, we do not have the knowledge of the stochastic component of the dynamics. In this paper, we study safety-critical control of stochastic systems with an unknown diffusion part and propose a data-driven method to handle these scenarios. More specifically, we propose a data-driven stochastic control barrier function (DDSCBF) framework and use supervised learning to learn the unknown stochastic dynamics via the DDSCBF scheme. Under some reasonable assumptions, we provide guarantees that the DDSCBF scheme can approximate the It\^{o} derivative of the stochastic control barrier function (SCBF) under partially unknown dynamics using the universal approximation theorem. We also show that we can achieve the same safety guarantee using the DDSCBF scheme as with SCBF in previous work without requiring the knowledge of stochastic dynamics. We use two non-linear stochastic systems to validate our theory in simulations.
Core-shell enhanced single particle model for LiFePO$_4$ batteries
Authors: Aki Takahashi, Gabriele Pozzato, Anirudh Allam, Vahid Azimi, Xueyan Li, Donghoon Lee, Johan Ko, Simona Onori
Date: 2022-05-20 00:00:00

In this paper, a novel electrochemical model for LiFePO$_4$ battery cells that accounts for the positive particle lithium intercalation and deintercalation dynamics is proposed. Starting from the enhanced single particle model, mass transport and balance equations along with suitable boundary conditions are introduced to model the phase transformation phenomena during lithiation and delithiation in the positive electrode material. The lithium-poor and lithium-rich phases are modeled using the core-shell principle, where a core composition is encapsulated with a shell composition. The coupled partial differential equations describing the phase transformation are discretized using the finite difference method, from which a system of ordinary differential equations written in state-space representation is obtained. Finally, model parameter identification is performed using experimental data from a 49Ah LFP pouch cell.
Ptychographic reconstruction with wavefront initialization
Authors: Felix Wittwer, Peter Modregger
Date: 2022-05-22 00:00:00
Tasks: Retrieval

X-ray ptychography is a cutting edge imaging technique providing ultra-high spatial resolutions. In ptychography, phase retrieval, i.e., the recovery of a complex valued signal from intensity-only measurements, is enabled by exploiting a redundancy of information contained in diffraction patterns measured with overlapping illuminations. For samples that are considerably larger than the probe we show that during the iteration the bulk information has to propagate from the sample edges to the center. This constitutes an inherent limitation of reconstruction speed for algorithms that use a flat initialization. Here, we experimentally demonstrate that a considerable improvement of computational speed can be achieved by utilizing a low resolution sample wavefront retrieved from measured diffraction patterns as initialization. In addition, we show that this approach avoids phase singularity artifacts due to strong phase gradients. Wavefront initialization is computationally fast and compatible with non-bulky samples. Therefore, the presented approach is readily adaptable with established ptychographic reconstruction algorithms implying a wide spread use.
Open Droplet Microfluidics for Testing Multi-Drug Resistance and Antibiotic Resilience in Bacteria
Authors: Taejoon Kong, Nicholas Backes, Gregory Phillips, Santosh Pandey
Date: 2022-05-24 00:00:00

New combinations of existing antibiotics are being investigated to combat bacterial resilience. This requires detection technologies with reasonable cost, accuracy, resolution, and throughput. Here, we present a multi -drug screening platform for bacterial cultures by combining droplet microfluidics, search algorithms, and imaging with a wide field of view. We remotely alter the chemical microenvironment around cells and test 12 combinations of resistant cell types and chemicals. Fluorescence intensity readouts allow us to infer bacterial resistance to specific antibiotics within 8 hours. The platform has potential to detect and identify parameters of bacterial resilience in cell cultures, biofilms, and microbial aggregates.
Automated WBRT Treatment Planning via Deep Learning Auto-Contouring and Customizable Landmark-Based Field Aperture Design
Authors: Yao Xiao, Carlos Cardenas, Dong Joo Rhee, Tucker Netherton, Lifei Zhang, Callistus Nguyen, Raphael Douglas, Raymond Mumme, Stephen Skett, Tina Patel, Chris Trauernicht, Caroline Chung, Hannah Simonds, Ajay Aggarwal, Laurence Court
Date: 2022-05-24 00:00:00

In this work, we developed and evaluated a novel pipeline consisting of two landmark-based field aperture generation approaches for WBRT treatment planning; they are fully automated and customizable. The automation pipeline is beneficial for both clinicians and patients, where we can reduce clinician workload and reduce treatment planning time. The customizability of the field aperture design addresses different clinical requirements and allows the personalized design to become feasible. The performance results regarding quantitative and qualitative evaluations demonstrated that our plans were comparable with the original clinical plans. This technique has been deployed as part of a fully automated treatment planning tool for whole-brain cancer and could be translated to other treatment sites in the future.
A Novel Data-Driven Method for the Analysis and Reconstruction of Cardiac Cine MRI
Authors: Nourelhouda Groun, Maria Villalba-Orero, Enrique Lara-Pezzi, Eusebio Valero, Jesus Garicano-Mena, Soledad Le Clainche
Date: 2022-05-24 00:00:00

Cardiac cine magnetic resonance imaging (MRI) can be considered the optimal criterion for measuring cardiac function. This imaging technique can provide us with detailed information about cardiac structure, tissue composition and even blood flow. This work considers the application of the higher order dynamic mode decomposition (HODMD) method to a set of MR images of a heart, with the ultimate goal of identifying the main patterns and frequencies driving the heart dynamics. A novel algorithm based on singular value decomposition combined with HODMD is introduced, providing a three-dimensional reconstruction of the heart. This algorithm is applied (i) to reconstruct corrupted or missing images, and (ii) to build a reduced order model of the heart dynamics.
Estimation and Inference for High Dimensional Factor Model with Regime Switching
Authors: Giovanni Urga, Fa Wang
Date: 2022-05-24 00:00:00
Tasks: Vocal Bursts Intensity Prediction

This paper proposes maximum (quasi)likelihood estimation for high dimensional factor models with regime switching in the loadings. The model parameters are estimated jointly by the EM (expectation maximization) algorithm, which in the current context only requires iteratively calculating regime probabilities and principal components of the weighted sample covariance matrix. When regime dynamics are taken into account, smoothed regime probabilities are calculated using a recursive algorithm. Consistency, convergence rates and limit distributions of the estimated loadings and the estimated factors are established under weak cross-sectional and temporal dependence as well as heteroscedasticity. It is worth noting that due to high dimension, regime switching can be identified consistently after the switching point with only one observation. Simulation results show good performance of the proposed method. An application to the FRED-MD dataset illustrates the potential of the proposed method for detection of business cycle turning points.
Subgeometrically ergodic autoregressions with autoregressive conditional heteroskedasticity
Authors: Mika Meitz, Pentti Saikkonen
Date: 2022-05-24 00:00:00

In this paper, we consider subgeometric (specifically, polynomial) ergodicity of univariate nonlinear autoregressions with autoregressive conditional heteroskedasticity (ARCH). The notion of subgeometric ergodicity was introduced in the Markov chain literature in 1980s and it means that the transition probability measures converge to the stationary measure at a rate slower than geometric; this rate is also closely related to the convergence rate of $\beta$-mixing coefficients. While the existing literature on subgeometrically ergodic autoregressions assumes a homoskedastic error term, this paper provides an extension to the case of conditionally heteroskedastic ARCH-type errors, considerably widening the scope of potential applications. Specifically, we consider suitably defined higher-order nonlinear autoregressions with possibly nonlinear ARCH errors and show that they are, under appropriate conditions, subgeometrically ergodic at a polynomial rate. An empirical example using energy sector volatility index data illustrates the use of subgeometrically ergodic AR-ARCH models.
The impact of conspicuous consumption in social Media on purchasing intentions
Authors: İbrahim Halil Efendioğku
Date: 2022-05-24 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

With the rapid increase in the use of social media in the last decade, the conspicuous consumption lifestyle within society has been now transferred to the social media. Along with the changing culture of consumption, the consumer who witnesses such portrayals on social media aspires to and desires the same products and services. Having regard to this situation, this study examines the impact of the conspicuous consumption trend in social media on purchasing intentions. Accordingly, the study aims to discover whether social media is being used as a conspicuous consumption channel and whether these conspicuous portrayals affect purchasing intentions
Incentive-compatible public transportation fares with random inspection
Authors: Inácio Bó, Chiu Yu Ko
Date: 2022-05-24 00:00:00
Tasks: Blocking

We consider the problem of designing prices for public transport where payment enforcing is done through random inspection of passengers' tickets as opposed to physically blocking their access. Passengers are fully strategic such that they may choose different routes or buy partial tickets in their optimizing decision. We derive expressions for the prices that make every passenger choose to buy the full ticket. Using travel and pricing data from the Washington DC metro, we show that a switch to a random inspection method for ticketing while keeping current prices could lead to more than 59% of revenue loss due to fare evasion, while adjusting prices to take incentives into consideration would reduce that loss to less than 20%, without any increase in prices.
Desirable Rankings: A New Method for Ranking Outcomes of a Competitive Process
Authors: Thayer Morrill, Peter Troyan
Date: 2022-05-24 00:00:00

We consider the problem of aggregating individual preferences over alternatives into a social ranking. A key feature of the problems that we consider - and the one that allows us to obtain positive results, in contrast to negative results such as Arrow's Impossibililty Theorem - is that the alternatives to be ranked are outcomes of a competitive process. Examples include rankings of colleges or academic journals. The foundation of our ranking method is that alternatives that agents rank higher than the one they receive (and thus have been rejected by) should also be ranked higher in the aggregate ranking. We introduce axioms to formalize this idea, and call any ranking that satisfies our axioms a desirable ranking. We show that as the market grows large, any desirable ranking coincides with the true underlying ranking of colleges by quality. Last, we provide an algorithm for constructing desirable rankings, and show that the outcome of this algorithm is the unique ranking of the colleges that satisfy our axioms.