pwc-archive/papers-with-abstracts huggingface dataset:
Performance Analysis of Charging Infrastructure Sharing in UAV and EV-involved Networks
Authors: Yujie Qin, Mustafa A. Kishk, Mohamed-Slim Alouini
Date: 2022-08-14 00:00:00

Electric vehicles (EVs) and unmanned aerial vehicles (UAVs) show great potential in modern transportation and communication networks, respectively. However, with growing demands for such technologies, the limited energy infrastructure becomes the bottleneck for their future growth. It might be of high cost and low energy efficiency for all the operators to each have their own dedicated energy infrastructure, such as charging stations. In this paper, we analyze a wireless charging infrastructure sharing strategy in UAV and EV-involved networks. We consider a scenario where UAVs can charge in EV charging stations and pay for the sharing fee. On the EVs' side, sharing infrastructure can earn extra profit but their service quality, such as waiting time, might slightly reduce. On the UAVs' side, if renting EV charging stations can achieve an acceptable system performance, say high coverage probability, while considering the cost, they may not need to build their dedicated charging stations. In this case, we use tools from stochastic geometry to model the locations and propose an optimization problem that captures the aforementioned trade-offs between cost or profit and quality of service. Our numerical results show that sharing infrastructure slightly increases the waiting time of EVs, say within $5$ min, but dramatically decreases the waiting time of drones, say more than $50$ min, and deploying more charging stations do achieve better performances, but all these better performances are expected to cost more.
A 100% Renewable Energy System: Enabling Zero CO2 Emission Offshore Platforms
Authors: Cunzhi Zhao, Xingpeng Li
Date: 2022-08-14 00:00:00

The total electricity consumption from offshore oil/gas platforms is around 16 TWh worldwide in 2019. The majority offshore platforms are powered by the diesel generators while the rest mainly uses gas turbines, which emits large amounts of CO2 per year. The fast development of offshore wind turbines (WT) can potentially replace traditional fossil fuel based resources to power offshore loads. Thus, a novel offshore hybrid renewable energy sources (OHRES) system is proposed to enable a zero CO2 emission offshore platform mitigating climate change. Battery energy storage system (BESS) and hydrogen energy storage system (HESS) are considered to mitigate the fluctuation of wind power in the proposed OHRES system. Resilience models are designed to enhance the resilience of the proposed OHRES system with extra energy stored in BESS and/or HESS. Case studies demonstrate the feasibility of the proposed OHRES system to power offshore platforms. The economic analysis reports the planning cost for the proposed OHRES system under different resilience levels, which may benefit the decision to balance the carbon emission and investment cost.
A Sparse Sampling Sensor Front-end IC for Low Power Continuous SpO$_2$ \& HR Monitoring
Authors: Sina Faraji Alamouti, Jasmine Jan, Cem Yalcin, Jonathan Ting, Ana Claudia Arias, Rikky Muller
Date: 2022-08-13 00:00:00
Tasks: Photoplethysmography (PPG)

Photoplethysmography (PPG) is an attractive method to acquire vital signs such as heart rate and blood oxygenation and is frequently used in clinical and at-home settings. Continuous operation of health monitoring devices demands a low power sensor that does not restrict the device battery life. Silicon photodiodes (PD) and LEDs are commonly used as the interface devices in PPG sensors; however, using of flexible organic devices can enhance the sensor conformality and reduce the cost of fabrication. In most PPG sensors, most of system power consumption is concentrated in powering LEDs, traditionally consuming mWs. Using organic devices further increases this power demand since these devices exhibit larger parasitic capacitances and typically need higher drive voltages. This work presents a sensor IC for continuous SpO$_2$ and HR monitoring that features an on-chip reconstruction-free sparse sampling algorithm to reduce the overall system power consumption by $\sim$70\% while maintaining the accuracy of the output information. The designed frontend is compatible with a wide range of devices from silicon PDs to organic PDs with parasitic capacitances up to 10 nF. Implemented in a 40 nm HV CMOS process, the chip occupies 2.43 mm$^2$ and consumes 49.7 $\mu$W and 15.2 $\mu$W of power in continuous and sparse sampling modes respectively. The performance of the sensor IC has been verified \textit{in vivo} with both types of devices and the results are compared against a clinical grade reference. Less than 1 bpm and 1\% mean absolute errors were achieved in both continuous and sparse modes of operation.
Sensitivity analysis and experimental evaluation of PID-like continuous sliding mode control
Authors: Michael Ruderman, Johann Reger, Benjamin Calmbach, Leonid Fridman
Date: 2022-08-13 00:00:00
Tasks: Benchmarking, Sensitivity

Continuous higher order sliding mode (CHOSM) controllers represent an efficient tool for disturbance rejection. For the systems with relative degree r, CHOSM approaches provide theoretically exact compensation of the matched Lipschitz perturbation, ensuring the finite-time convergence to the (r+1)-th sliding-mode set, by using only information on the sliding output and its derivatives up to the order (r-1). In this paper, we investigate the disturbance rejection properties of a PID-like CHOSM controller, as the simplest and intuitively clear example which incorporates nonlinear actions on the output error, its derivative, and integration of its sign. We use the harmonic balance approach and develop an analysis of propagation of the matched Lipschitz perturbation through the control loop in frequency domain. The resulted solution appears in form of the Bode-like loci which depend also on the amplitude of harmonic disturbances. Such amplitude-frequency characteristics allow certain comparability with standard disturbance sensitivity functions of a linear PID-controlled system in frequency domain. Also a simple and straightforward design procedure for the robust linear PID controller targeting the second-order system plants under investigation is provided for benchmarking. Additional (parasitic) actuator dynamics, which can lead to self-induced steady oscillations, i.e. chattering, is ditto respected. A detailed experimental case study, accomplished on an electro-mechanical actuator in the laboratory setting, highlight and make the pros and cons of both PID and CHOSM controllers well comparable for a broadband disturbance rejection.
Sizing and Location Selection of Medium-Voltage Back-to-Back Converter for DER-Dominated Distribution Systems
Authors: Xiangqi Zhu, Akanksha Singh, Barry Mather
Date: 2022-08-12 00:00:00

Medium-voltage back-to-back (MVB2B) converters can connect two distribution systems and quantifiably transfer power between them. This function can enable the MVB2B converter to exchange distributed energy resource (DER)-generated power between two systems and bring significant value to enhancing distribution system DER adoption. Our previous work analyzed and demonstrated the value the MVB2B converter can bring to DER integration. As continuous work, this paper presents a methodology that helps address the MVB2B converter sizing and location selection problem in distribution systems with high DER penetrations. The proposed methodology aims to address three critical problems for MVB2B converter implementation in the real world: 1) which distribution systems are better to be connected, 2) what converter size is appropriate for connecting the distribution systems, and 3) where the optimal connection points are in the systems for connecting the MVB2B converter. The proposed methodology has been demonstrated by case studies that include various scenarios involving distribution systems with different dominated load types and high photovoltaic penetrations.
Behavioral and Population Data Driven Distribution System Load Modeling
Authors: Isaac Bromley-Dulfano, Xiangqi Zhu, Barry Mather
Date: 2022-08-12 00:00:00
Tasks: Survey

Distribution system residential load modeling and analysis for different geographic areas within a utility or an independent system operator territory are critical for enabling small-scale, aggregated distributed energy resources to participate in grid services under Federal Energy Regulatory Commission Order No. 2222 [1]. In this study, we develop a methodology of modeling residential load profiles in different geographic areas with a focus on human behavior impact. First, we construct a behavior-based load profile model leveraging state-of-the-art appliance models. We simulate human activity and occupancy using Markov chain Monte Carlo methods calibrated with the American Time Use Survey data set. Second, we link our model with cleaned Current Population Survey data from the U.S. Census Bureau. Finally, we populate two sets of 500 households using California and Texas census data, respectively, to perform an initial analysis of the load in different geographic areas with various group features (e.g., different income levels). To distinguish the effect of population behavior differences on aggregated load, we simulate load profiles for both sets assuming fixed physical household parameters and weather data. Analysis shows that average daily load profiles vary significantly by income and income dependency varies by locality.
Grid Impact Analysis and Mitigation of En-Route Charging Stations for Heavy-Duty Electric Vehicles
Authors: Xiangqi Zhu, Partha Mishra, Barry Mather, Mingzhi Zhang, Andrew Meintz
Date: 2022-08-12 00:00:00

This paper presents a consolidated grid impact analysis design and corresponding mitigation strategies for heavy-duty electric vehicle (EV) charging stations. The charging load of heavy-duty charging station can reach several megawatts, which could induce adverse impacts on the distribution grid if not effectively mitigated. To analyze the impacts and provide corresponding solutions, we select four representative distribution systems - including both single-feeder cases and a multi-feeder case - and design thorough test metrics for the impact analysis. The charging load profiles used in the analysis are derived from realistic conventional heavy-duty vehicle travel data. Based on the analysis results, charging stations are placed at three different representative locations in each distribution system: best, good, and worst locations. Mitigation strategies using a combination of smart charger functionality, on-site photovoltaic (PV) generation, and on-site energy storage (ES) are proposed and tested. A sizing method is also proposed to find the optimal PV-ES-charger capacity that minimizes the capital cost.
Trajectory Tracking Control of the Bionic Joint Actuated by Pneumatic Artificial Muscle Based on Robust Modeling
Authors: Yang Wang, Qiang Zhang, Xiao-hui Xiao
Date: 2022-08-10 00:00:00
Tasks: Position

To simply and effectively realize the trajectory tracking control of a bionic joint actuated by a single pneumatic artificial muscle (PAM), a cascaded control strategy is proposed based on the robust modeling method. Firstly, the relationship between the input voltage of the proportional directional control valve and the inner driving pressure of PAM is expressed as a nonlinear model analytically. Secondly, the nonlinear relationship between the driving pressure input of PAM and the angular position output of the bionic joint is described as a second-order linear time-invariant model (LTI) accompanied by parametric perturbations, equivalently, and then the parameters of the model are identified by the robust modeling method. Then, a hybrid model is established based on the two models (the nonlinear model and the LTI model) and corresponding to it, a cascaded controller is developed, the outer loop of which is an H-infinite controller for the angular position tracking designed by loop-shaping design procedure (LSDP) and the inner loop is a nonlinear controller based on the feedback linearization theory for the PAM driving pressure control. Finally, the experiment is accomplished within the joint rotation range of 90 degrees and with the working frequency upper bound of 1.25 rad/s. And the joint with the developed cascaded controller tracks given reference trajectories with steady-state errors smaller than 2%. Results show that the trajectory tracking control of a highly nonlinear system is highly efficient using the proposed strategy in the case of relatively low work frequency.
Terrain-based vehicle localization using an active suspension system
Authors: Yu Jiang, John Eisenmann, William Graves, Vijayaraghavan Sridhar, Zackary Anderson
Date: 2022-07-22 00:00:00

This paper, for the first time, presents a terrain-based localization approach using sensor data from an active suspension system. The contribution is four-fold. First, it is shown that a location dependent road height profile can be created from sensor data of the active suspension system. Second, an algorithm is developed to extract a pitch profile from the road height profile data. The ideal pitch profile is vehicle-independent and only depends on the road. This pitch profile generated from an on-board computer is matched with a known terrain map to achieve real-time positioning. Third, a crowd-sourced map creation algorithm is developed to create and improve the terrain map that contains pitch profile. Fourth, experiments have been conducted to validate the accuracy and robustness of the proposed localization approach.
From the historical Roman road network to modern infrastructure in Italy
Authors: Luca De Benedictis, Vania Licio, Anna Pinna
Date: 2022-08-13 00:00:00

An integrated and widespread road system, like the one built during the Roman Empire in Italy, plays an important role today in facilitating the construction of new infrastructure. This paper investigates the historical path of Roman roads as main determinant of both motorways and railways in the country. The empirical analysis shows how the modern Italian transport infrastructure followed the path traced in ancient times by the Romans in constructing their roads. Being paved and connecting Italy from North to South, consular trajectories lasted in time, representing the starting physical capital for developing the new transport networks.
Do Investors Hedge Against Green Swans? Option-Implied Risk Aversion to Wildfires
Authors: Amine Ouazad
Date: 2022-08-14 00:00:00

Measuring beliefs about natural disasters is challenging. Deep out-of-the-money options allow investors to hedge at a range of strikes and time horizons, thus the 3-dimensional surface of firm-level option prices provides information on (i) skewed and fat-tailed beliefs about the impact of natural disaster risk across space and time dimensions at daily frequency; and (ii) information on the covariance of wildfire-exposed stocks with investors' marginal utility of wealth. Each publicly-traded company's daily surface of option prices is matched with its network of establishments and wildfire perimeters over two decades. First, wildfires affect investors' risk neutral probabilities at short and long maturities; investors price asymmetric downward tail risk and a probability of upward jumps. The volatility smile is more pronounced. Second, comparing risk-neutral and physical distributions reveals the option-implied risk aversion with respect to wildfire-exposed stock prices. Investors' marginal utility of wealth is correlated with wildfire shocks. Option-implied risk aversion identifies the wildfire-exposed share of portfolios. For risk aversions consistent with Barro (2012), equity options suggest (i) investors hold larger shares of wildfire-exposed stocks than the market portfolio; or (ii) investors may have more pessimistic beliefs about wildfires' impacts than what observed returns suggest, such as pricing low-probability unrealized downward tail risk. We calibrate options with models featuring both upward and downward risk. Results are consistent a significant pricing of downward jumps.
The Growing US-Mexico Natural Gas Trade and Its Regional Economic Impacts in Mexico
Authors: Haoying Wang, Rafael Garduno Rivera
Date: 2022-08-14 00:00:00

With the recent administration change in Mexico, the fluctuations in national energy policy have generated widespread concerns among investors and the public. The debate centers around Mexico's energy dependence on the US and how Mexico's energy development should move forward. The goal of this study is two-fold. We first review the history and background of the recent energy reforms in Mexico. The focus of the study is on quantifying the state-level regional economic impact of the growing US-Mexico natural gas trade in Mexico. We examine both the quantity effect (impact of import volume) and the price effect (impact of natural gas price changes). Our empirical analysis adopts a fixed-effects regression model and the instrumental variables (IV) estimation approach to address spatial heterogeneities and the potential endogeneity associated with natural gas import. The quantity effect analysis suggests a statistically significant positive employment impact of imports in non-mining sectors. The impact in the mining sector, however, is insignificant. The state-level average (non-mining) employment impact is 127 jobs per million MCFs of natural gas imported from the US. The price effect analysis suggests a statistically significant positive employment impact of price increases in the mining sector. A one-percentage increase in natural gas price (1.82 Pesos/GJ, in 2015 Peso) leads to an average state-level mining employment increase of 140 (or 2.38%). We also explored the implications of our findings for Mexico's energy policy, trade policy, and energy security.
Assessing the Impact of Patent Attributes on the Value of Discrete and Complex Innovations
Authors: Mohd Shadab Danish, Pritam Ranjan, Ruchi Sharma
Date: 2022-08-10 00:00:00

This study assesses the degree to which the social value of patents can be connected to the private value of patents across discrete and complex innovation. The underlying theory suggests that the social value of cumulative patents is less related to the private value of patents. We use the patents applied between 1995 to 2002 and granted on or before December 2018 from the Indian Patent Office (IPO). Here the patent renewal information is utilized as a proxy for the private value of the patent. We have used a variety of logit regression model for the impact assessment analysis. The results reveal that the technology classification (i.e., discrete versus complex innovations) plays an important role in patent value assessment, and some technologies are significantly different than the others even within the two broader classifications. Moreover, the non-resident patents in India are more likely to have a higher value than the resident patents. According to the conclusions of this study, only a few technologies from the discrete and complex innovation categories have some private value. There is no evidence that patent social value indicators are less useful in complicated technical classes than in discrete ones.
On spatial majority voting with an even (vis-a-vis odd) number of voters: a note
Authors: Anindya Bhattacharya, Francesco Ciardiello
Date: 2022-08-14 00:00:00

In this note we consider situations of (multidimensional) spatial majority voting. We show that under some assumptions usual in this literature, with an even number of voters if the core of the voting situation is singleton (and in the interior of the policy space) then the element in the core is never a Condorcet winner. This is in sharp contrast with what happens with an odd number of voters: in that case, under identical assumptions, it is well known that if the core of the voting situation is non-empty then the single element in the core is the Condorcet winner as well.
A reconstruction method for binary limited-data tomography using a dictionary-based sparse shape recovery
Authors: Haytham A. Ali, katsuya Fujii, Hiroyuki Kudo
Date: 2022-08-14 00:00:00
Tasks: Image Reconstruction, Object

Binary tomography is concerned with reconstructing a binary image from a very small number or other limited CT projection data. This problem itself not only possesses several medical imaging applications but also can be considered a model of general inverse problems to recover the object shape from limited measured data. Several approaches such as the Mumford-Shah method and various level-set methods have been investigated, but most of them lead to a non-convex optimization due to the difficulty to handle the binary constraint. We propose a new method based on a convex optimization inspired by dictionary-based shape recovery. In the proposed method, the object boundary of the binary image is represented by a level set of linear combinations of basis vectors in the dictionary. Using the dictionary, the object boundary is reconstructed by finding weights of the linear combination that best match the measured data. We create the dictionary by using the Gaussian radial basis function (GRBF). More concretely, we use Gaussian functions as a basis function placed at sparse grid points to represent the parametric level-set function and provide more flexibility in the binary representation of the reconstructed image. The simulation results of CT image reconstruction from only four projection data demonstrate that the proposed method can recover the object boundary more accurately compared with other competitive methods. The significance of our approach is the formulation with a tractable convex program while keeping moderate mathematical rigorousness.
Dynamic Task Software Caching-assisted Computation Offloading for Multi-Access Edge Computing
Authors: Zhixiong Chen, Wenqiang Yi, Atm S. Alam, Arumugam Nallanathan
Date: 2022-08-15 00:00:00
Tasks: Edge-computing

In multi-access edge computing (MEC), most existing task software caching works focus on statically caching data at the network edge, which may hardly preserve high reusability due to the time-varying user requests in practice. To this end, this work considers dynamic task software caching at the MEC server to assist users' task execution. Specifically, we formulate a joint task software caching update (TSCU) and computation offloading (COMO) problem to minimize users' energy consumption while guaranteeing delay constraints, where the limited cache size and computation capability of the MEC server, as well as the time-varying task demand of users are investigated. This problem is proved to be non-deterministic polynomial-time hard, so we transform it into two sub-problems according to their temporal correlations, i.e., the real-time COMO problem and the Markov decision process-based TSCU problem. We first model the COMO problem as a multi-user game and propose a decentralized algorithm to address its Nash equilibrium solution. We then propose a double deep Q-network (DDQN)-based method to solve the TSCU policy. To reduce the computation complexity and convergence time, we provide a new design for the deep neural network (DNN) in DDQN, named state coding and action aggregation (SCAA). In SCAA-DNN, we introduce a dropout mechanism in the input layer to code users' activity states. Additionally, at the output layer, we devise a two-layer architecture to dynamically aggregate caching actions, which is able to solve the huge state-action space problem. Simulation results show that the proposed solution outperforms existing schemes, saving over 12% energy, and converges with fewer training episodes.