pwc-archive/papers-with-abstracts huggingface dataset:
Modelling, Simulation, and Control of a Flexible Space Launch Vehicle
Authors: Muhammad Abdullah Aamer, Qurat Ul Ain, Ushbah Kaleem, Hafiz Zeeshan Iqbal Khan, Jamshed Riaz
Date: 2023-09-22 00:00:00

Modern Space Launch Vehicles (SLVs), being slender in shape and due to the use of lightweight materials, are generally flexible in nature. This structural flexibility, when coupled with sensor and actuator dynamics, can adversely affect the control of SLV, which may lead to vehicle instability and, in the worst-case scenario, to structural failure. This work focuses on modelling and simulation of rigid and flexible dynamics of an SLV and its interactions with the control system. SpaceX's Falcon 9 has been selected for this study. The flexible modes are calculated using modal analysis in Ansys. High-fidelity nonlinear simulation is developed which incorporates the flexible modes and their interactions with rigid degrees of freedom. Moreover, linearized models are developed for flexible body dynamics, over the complete trajectory until the first stage's separation. Using classical control methods, attitude controllers, that keep the SLV on its desired trajectory, are developed, and multiple filters are designed to suppress the interactions of flexible dynamics. The designed controllers along with filters are implemented in the nonlinear simulation. Furthermore, to demonstrate the robustness of designed controllers, Monte-Carlo simulations are carried out and results are presented.
A Graph-Based Technique for the Automated Control-Oriented Modeling of District Heating Networks
Authors: Audrey Blizard, Stephanie Stockar
Date: 2023-09-22 00:00:00

Advanced control strategies for delivering heat to users in a district heating network have the potential to improve performance and reduce wasted energy. To enable the design of such controllers, this paper proposes an automated plant modeling framework that captures the relevant system dynamics, while being adaptable to any network configuration. Starting from the network topology and system parameters, the developed algorithm generates a state-space model of the system, relying on a graph-based technique to facilitate the combination of component models into a full network model. The accuracy of the approach is validated against experimental data collected from a laboratory-scale district heating network. The verification shows an average normalized root mean square error of 0.39 in the mass flow rates delivered to the buildings, and 0.15 in the network return temperature. Furthermore, the ability of the proposed modeling technique to rapidly generate models characterizing different network configurations is demonstrated through its application to topology optimization. The optimal design, obtained via a branch and bound algorithm, reduces network heat losses by 15% as compared to the conventional length-minimized topology.
Renewable fuel regulation: Implications for e-fuel production infrastructure in energy hubs
Authors: Lissy Langer, Ioannis Kountouris, Rasmus Bramstoft, Marie Münster, Dogan Keles
Date: 2023-09-22 00:00:00

Renewable fuels of non-biological origin (RFNBOs) are needed to decarbonize hard-to-electrify sectors that rely on liquid or gaseous fuels, such as long-haul shipping. The EU's Delegated Act on RFNBOs defines renewable hydrogen by considering rules on additionality as well as temporal and geographical correlation of the electricity used. For a Danish case study, we examine the impact on the capacity expansion problem of an energy hub producing renewable hydrogen, e-methanol, and e-ammonia using a mixed-integer linear problem formulation. We analyze the investments in production capacity, storage assets, and Power Purchase Agreement (PPA) volume under different fuel price assumptions for 2030. We find that e-methanol (combined with limited storage to secure hydrogen supply to the synthesizer) provides the best business case with a PPA volume based on the maximum allowed electrolyzer size.
Direct Learning for Parameter-Varying Feedforward Control: A Neural-Network Approach
Authors: Johan Kon, Jeroen van de Wijdeven, Dennis Bruijnen, Roland Tóth, Marcel Heertjes, Tom Oomen
Date: 2023-09-22 00:00:00
Tasks: Scheduling

The performance of a feedforward controller is primarily determined by the extent to which it can capture the relevant dynamics of a system. The aim of this paper is to develop an input-output linear parameter-varying (LPV) feedforward parameterization and a corresponding data-driven estimation method in which the dependency of the coefficients on the scheduling signal are learned by a neural network. The use of a neural network enables the parameterization to compensate a wide class of constant relative degree LPV systems. Efficient optimization of the neural-network-based controller is achieved through a Levenberg-Marquardt approach with analytic gradients and a pseudolinear approach generalizing Sanathanan-Koerner to the LPV case. The performance of the developed feedforward learning method is validated in a simulation study of an LPV system showing excellent performance.
Can Urban Air Mobility become reality? Opportunities, challenges and selected research results
Authors: Henry Pak, Lukas Asmer, Petra Kokus, Bianca I. Schuchardt, Albert End, Frank Meller, Karolin Schweiger, Christoph Torens, Carolina Barzantny, Dennis Becker, Johannes Maria Ernst, Florian Jäger, Tim Laudien, Nabih Naeem, Anne Papenfuß, Jan Pertz, Prajwal Shiva Prakasha, Patrick Ratei, Fabian Reimer, Patrick Sieb, Chen Zhu
Date: 2023-09-22 00:00:00

Urban Air Mobility (UAM) is a new air transportation system for passengers and cargo in urban environments, enabled by new technologies and integrated into multimodal transportation systems. The vision of UAM comprises the mass use in urban and suburban environments, complementing existing transportation systems and contributing to the decarbonization of the transport sector. Initial attempts to create a market for urban air transportation in the last century failed due to lack of profitability and community acceptance. Technological advances in numerous fields over the past few decades have led to a renewed interest in urban air transportation. UAM is expected to benefit users and to also have a positive impact on the economy by creating new markets and employment opportunities for manufacturing and operation of UAM vehicles and the construction of related ground infrastructure. However, there are also concerns about noise, safety and security, privacy and environmental impacts. Therefore, the UAM system needs to be designed carefully to become safe, affordable, accessible, environmentally friendly, economically viable and thus sustainable. This paper provides an overview of selected key research topics related to UAM and how the German Aerospace Center (DLR) contributed to this research in the project "HorizonUAM - Urban Air Mobility Research at the German Aerospace Center (DLR)". Selected research results that support the realization of the UAM vision are briefly presented.
Adaptive Model Predictive Control for Engine-Driven Ducted Fan Lift Systems using an Associated Linear Parameter Varying Model
Authors: Hanjie Jiang, Ye Zhou, Hann Woei Ho, Wenjie Hu
Date: 2023-09-22 00:00:00
Tasks: model, Model Predictive Control

Ducted fan lift systems (DFLSs) powered by two-stroke aviation piston engines present a challenging control problem due to their complex multivariable dynamics. Current controllers for these systems typically rely on proportional-integral algorithms combined with data tables, which rely on accurate models and are not adaptive to handle time-varying dynamics or system uncertainties. This paper proposes a novel adaptive model predictive control (AMPC) strategy with an associated linear parameter varying (LPV) model for controlling the engine-driven DFLS. This LPV model is derived from a global network model, which is trained off-line with data obtained from a general mean value engine model for two-stroke aviation engines. Different network models, including multi-layer perceptron, Elman, and radial basis function (RBF), are evaluated and compared in this study. The results demonstrate that the RBF model exhibits higher prediction accuracy and robustness in the DFLS application. Based on the trained RBF model, the proposed AMPC approach constructs an associated network that directly outputs the LPV model parameters as an adaptive, robust, and efficient prediction model. The efficiency of the proposed approach is demonstrated through numerical simulations of a vertical take-off thrust preparation process for the DFLS. The simulation results indicate that the proposed AMPC method can effectively control the DFLS thrust with a relative error below 3.5%.
High-Dimensional Controller Tuning through Latent Representations
Authors: Alireza Sarmadi, Prashanth Krishnamurthy, Farshad Khorrami
Date: 2023-09-21 00:00:00
Tasks: Bayesian Optimization

In this paper, we propose a method to automatically and efficiently tune high-dimensional vectors of controller parameters. The proposed method first learns a mapping from the high-dimensional controller parameter space to a lower dimensional space using a machine learning-based algorithm. This mapping is then utilized in an actor-critic framework using Bayesian optimization (BO). The proposed approach is applicable to complex systems (such as quadruped robots). In addition, the proposed approach also enables efficient generalization to different control tasks while also reducing the number of evaluations required while tuning the controller parameters. We evaluate our method on a legged locomotion application. We show the efficacy of the algorithm in tuning the high-dimensional controller parameters and also reducing the number of evaluations required for the tuning. Moreover, it is shown that the method is successful in generalizing to new tasks and is also transferable to other robot dynamics.
Differentially Private Reward Functions in Policy Synthesis for Markov Decision Processes
Authors: Alexander Benvenuti, Calvin Hawkins, Brandon Fallin, Bo Chen, Brendan Bialy, Miriam Dennis, Matthew Hale
Date: 2023-09-21 00:00:00

Markov decision processes often seek to maximize a reward function, but onlookers may infer reward functions by observing the states and actions of such systems, revealing sensitive information. Therefore, in this paper we introduce and compare two methods for privatizing reward functions in policy synthesis for multi-agent Markov decision processes, which generalize Markov decision processes. Reward functions are privatized using differential privacy, a statistical framework for protecting sensitive data. The methods we develop perturb either (1) each agent's individual reward function or (2) the joint reward function shared by all agents. We show that approach (1) provides better performance. We then develop a polynomial-time algorithm for the numerical computation of the performance loss due to privacy on a case-by-case basis. Next, using approach (1), we develop guidelines for selecting reward function values to preserve ``goal" and ``avoid" states while still remaining private, and we quantify the increase in computational complexity needed to compute policies from privatized rewards. Numerical simulations are performed on three classes of systems and they reveal a surprising compatibility with privacy: using reasonably strong privacy ($\epsilon =1.3$) on average induces as little as a~$5\%$ decrease in total accumulated reward and a $0.016\%$ increase in computation time.
Safety Index Synthesis with State-dependent Control Space
Authors: Rui Chen, WeiYe Zhao, Changliu Liu
Date: 2023-09-21 00:00:00

This paper introduces an approach for synthesizing feasible safety indices to derive safe control laws under state-dependent control spaces. The problem, referred to as Safety Index Synthesis (SIS), is challenging because it requires the existence of feasible control input in all states and leads to an infinite number of constraints. The proposed method leverages Positivstellensatz to formulate SIS as a nonlinear programming (NP) problem. We formally prove that the NP solutions yield safe control laws with two imperative guarantees: forward invariance within user-defined safe regions and finite-time convergence to those regions. A numerical study validates the effectiveness of our approach.
Electric Autonomous Mobility-on-Demand: Jointly Optimal Vehicle Design and Fleet Operation
Authors: Fabio Paparella, Theo Hofman, Mauro Salazar
Date: 2023-09-21 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles

The advent of autonomous driving and electrification is enabling the deployment of Electric Autonomous Mobility-on-Demand (E-AMoD) systems, whereby electric autonomous vehicles provide on-demand mobility. Crucially, the design of the individual vehicles and the fleet, and the operation of the system are strongly coupled. Hence, to maximize the system-level performance, they must be optimized in a joint fashion. To this end, this paper presents a framework to jointly optimize the fleet design in terms of battery capacity and number of vehicles, and the operational strategies of the E-AMoD system, with the aim of maximizing the operator's total profit. Specifically, we first formulate this joint optimization problem using directed acyclic graphs as a mixed integer linear program, which can be solved using commercial solvers with optimality guarantees. Second, to solve large instances of the problem, we propose a solution algorithm that solves for randomly sampled sub-problems, providing a more conservative solution of the full problem, and devise a heuristic approach to tackle larger individual sub-problem instances. Finally, we showcase our framework on a real-world case study in Manhattan, where we demonstrate the interdependence between the number of vehicles, their battery size, and operational and fixed costs. Our results indicate that to maximize a mobility operator's profit, a fleet of small and light vehicles with battery capacity of 20 kWh only can strike the best trade-off in terms of battery degradation, fixed costs and operational efficiency.
Quantifying Harmony between Direct and Indirect Pathways in The Basal Ganglia; Healthy and Parkinsonian States
Authors: Sang-Yoon Kim, Woochang Lim
Date: 2023-09-21 00:00:00

The basal ganglia (BG) show a variety of functions for motor and cognition. There are two competitive pathways in the BG; direct pathway (DP) which facilitates movement and indirect pathway (IP) which suppresses movement. It is well known that diverse functions of the BG may be made through "balance" between DP and IP. But, to the best of our knowledge, so far no quantitative analysis for such balance was done. In this paper, as a first time, we introduce the competition degree ${\cal C}_d$ between DP and IP. Then, by employing ${\cal C}_d$, we quantify their competitive harmony (i.e., competition and cooperative interplay), which could lead to improving our understanding of the traditional "balance" so clearly and quantitatively. We first consider the case of normal dopamine (DA) level of $\phi^*=0.3$. In the case of phasic cortical input (10 Hz), a healthy state with ${\cal C}_d^* = 2.82$ (i.e., DP is 2.82 times stronger than IP) appears. In this case, normal movement occurs via harmony between DP and IP. Next, we consider the case of decreased DA level, $\phi = \phi^*(=0.3)~x_{DA}$ ($1 > x_{DA} \geq 0$). With decreasing $x_{DA}$ from 1, the competition degree ${\cal C}_d$ between DP and IP decreases monotonically from ${\cal C}_d^*$, which results in appearance of a pathological Parkinsonian state with reduced ${\cal C}_d$. In this Parkinsonian state, strength of IP is much increased than that in the case of normal healthy state, leading to disharmony between DP and IP. Due to such break-up of harmony between DP and IP, impaired movement occurs. Finally, we also study treatment of the pathological Parkinsonian state via recovery of harmony between DP and IP.
A comparative data study on dinosaur, bird and human bone attributes -- A supporting study for convergent evolution
Authors: Akshita Patil, Nishchal Dwivedi
Date: 2023-09-21 00:00:00

For over 165 million years, dinosaurs reigned on this planet. Their entire existence saw variations in their body size and mass . Understanding the relationship between various attributes such as femur length, breadth; humerus length, breadth; tibia length, breadth and body mass of dinosaurs contributes to our understanding of the Jurassic era and further provides reasoning for bone and body size evolution of modern day descendants of those from the Dinosauria clade. The following work consists of statistical evidence derived from an encyclopedic data set consisting of a wide variety of measurements pertaining to discovered fossils of a particular taxa of dinosaur. Our study establishes linearly regressive correspondence between femur and humerus length and radii. Furthermore, there is also a comparison with terrestrial bird bone lengths, to verify the claim of birds being closest alive species to dinosaurs. An analysis into bone ratios of early humans shows that terrestrial birds are closer to humans than that of dinosaurs. Not only on one hand it challenges the closeness of birds with dinosaurs, but on the other hand it makes a case of convergent evolution between birds and humans, due to their closeness in regressive fits. A correlation between bone ratios of dinosaurs and early humans also advances understanding in the structural and physical distinctions between the two species. Overall, the work contains evaluation of dinosaur skeletons and promotes further exploration and research in the paleontological field to strengthen the conclusions drawn thus far.
The Micro-Aggregated Profit Share
Authors: Thomas Hasenzagl, Luis Perez
Date: 2023-09-22 00:00:00

How much has market power increased in the United States in the last fifty years? And how did the rise in market power affect aggregate profits? Using micro-level data from U.S. Compustat, we find that several indicators of market power have steadily increased since 1970. In particular, the aggregate markup has gone up from 10% of price over marginal cost in 1970 to 23% in 2020, and aggregate returns to scale have risen from 1.00 to 1.13. We connect these market-power indicators to profitability by showing that the aggregate profit share can be expressed in terms of the aggregate markup, aggregate returns to scale, and a sufficient statistic for production networks that captures double marginalization in the economy. We find that despite the rise in market power, the profit share has been constant at 18% of GDP because the increase in monopoly rents has been completely offset by rising fixed costs and changes in technology. Our empirical results have subtle implications for policymakers: overly aggressive enforcement of antitrust law could decrease firm dynamism and paradoxically lead to lower competition and higher market power.
Searching for Smurfs: Testing if Money Launderers Know Alert Thresholds
Authors: Rasmus Ingemann Tuffveson Jensen, Joras Ferwerda, Christian Remi Wewer
Date: 2023-09-22 00:00:00
Tasks: counterfactual

To combat money laundering, banks raise and review alerts on transactions that exceed confidential thresholds. This paper presents a data-driven approach to detect smurfing, i.e., money launderers seeking to evade detection by breaking up large transactions into amounts under the secret thresholds. The approach utilizes the notion of a counterfactual distribution and relies on two assumptions: (i) smurfing is unfeasible for the very largest financial transactions and (ii) money launderers have incentives to make smurfed transactions close to the thresholds. Simulations suggest that the approach can detect smurfing when as little as 0.1-0.5\% of all bank transactions are subject to smurfing. An application to real data from a systemically important Danish bank finds no evidence of smurfing and, thus, no evidence of leaked confidential thresholds. An implementation of our approach will be available online, providing a free and easy-to-use tool for banks.
ISLAND: Interpolating Land Surface Temperature using land cover
Authors: Yuhao Liu, Pranavesh Panakkal, Sylvia Dee, Guha Balakrishnan, Jamie Padgett, Ashok Veeraraghavan
Date: 2023-09-21 00:00:00
Tasks: Blocking, Time Series Analysis

Cloud occlusion is a common problem in the field of remote sensing, particularly for retrieving Land Surface Temperature (LST). Remote sensing thermal instruments onboard operational satellites are supposed to enable frequent and high-resolution observations over land; unfortunately, clouds adversely affect thermal signals by blocking outgoing longwave radiation emission from the Earth's surface, interfering with the retrieved ground emission temperature. Such cloud contamination severely reduces the set of serviceable LST images for downstream applications, making it impractical to perform intricate time-series analysis of LST. In this paper, we introduce a novel method to remove cloud occlusions from Landsat 8 LST images. We call our method ISLAND, an acronym for Interpolating Land Surface Temperature using land cover. Our approach uses LST images from Landsat 8 (at 30 m resolution with 16-day revisit cycles) and the NLCD land cover dataset. Inspired by Tobler's first law of Geography, ISLAND predicts occluded LST through a set of spatio-temporal filters that perform distance-weighted spatio-temporal interpolation. A critical feature of ISLAND is that the filters are land cover-class aware, making it particularly advantageous in complex urban settings with heterogeneous land cover types and distributions. Through qualitative and quantitative analysis, we show that ISLAND achieves robust reconstruction performance across a variety of cloud occlusion and surface land cover conditions, and with a high spatio-temporal resolution. We provide a public dataset of 20 U.S. cities with pre-computed ISLAND LST outputs. Using several case studies, we demonstrate that ISLAND opens the door to a multitude of high-impact urban and environmental applications across the continental United States.
Multiple Satellites Collaboration for Joint Code-aided CFOs and CPOs Estimation
Authors: Pingyue Yue, Yixuan Li, Yue Li, Rui Zhang, Shuai Wang, Jianping An
Date: 2023-09-22 00:00:00
Tasks: parameter estimation

Low Earth Orbit (LEO) satellites are being extensively researched in the development of secure Internet of Remote Things (IoRT). In scenarios with miniaturized terminals, the limited transmission power and long transmission distance often lead to low Signal-to-Noise Ratio (SNR) at the satellite receiver, which degrades communication performance. A solution to address this issue is the utilization of cooperative satellites, which can combine signals received from multiple satellites, thereby significantly improve SNR. However, in order to maximize the combination gain, the signal coherent combining is necessary, which requires the carrier frequency and phase of each receiving signal to be aligned. Under low SNR circumstances, carrier parameter estimation can be a significant challenge, especially for short burst transmission with no training sequence. In order to tackle it, we propose an iterative code-aided estimation algorithm for joint Carrier Frequency Offset (CFO) and Carrier Phase Offset (CPO). The Cram\'er-Rao Lower Bound (CRLB) is suggested as the limit on the parameter estimation performance. Simulation results demonstrate that the proposed algorithm can approach Bit Error Rate (BER) performance bound within 0.4 dB with regards to four-satellite collaboration.