pwc-archive/papers-with-abstracts huggingface dataset:
Optimal Time-Invariant Distributed Formation Tracking for Second-Order Multi-Agent Systems
Authors: Marco Fabris, Giulio Fattore, Angelo Cenedese
Date: 2023-07-23 00:00:00

This paper addresses the optimal time-invariant formation tracking problem with the aim of providing a distributed solution for multi-agent systems with second-order integrator dynamics. In the literature, most of the results related to multi-agent formation tracking do not consider energy issues while investigating distributed feedback control laws. In order to account for this crucial design aspect, we contribute by formalizing and proposing a solution to an optimization problem that encapsulates trajectory tracking, distance-based formation control and input energy minimization, through a specific and key choice of potential functions in the optimization cost. To this end, we show how to compute the inverse dynamics in a centralized fashion by means of the Projector-Operator-based Newton's method for Trajectory Optimization (PRONTO) and, more importantly, we exploit such an offline solution as a general reference to devise a stabilizing online distributed control law. Finally, numerical examples involving a cubic formation following a chicane-like path in the 3D space are provided to validate the proposed control strategies.
Adaptive Consensus-based Reference Generation for the Regulation of Open-Channel Networks
Authors: Marco Fabris, Marco D. Bellinazzi, Andrea Furlanetto, Angelo Cenedese
Date: 2023-07-23 00:00:00
Tasks: Management

This paper deals with water management over open-channel networks (OCNs) subject to water height imbalance. The OCN is modeled by means of graph theoretic tools and a regulation scheme is designed basing on an outer reference generation loop for the whole OCN and a set of local controllers. Specifically, it is devised a fully distributed adaptive consensus-based algorithm within the discrete-time domain capable of (i) generating a suitable tracking reference that stabilizes the water increments over the underlying network at a common level; (ii) coping with general flow constraints related to each channel of the considered system. This iterative procedure is derived by solving a guidance problem that guarantees to steer the regulated network - represented as a closed-loop system - while satisfying requirements (i) and (ii), provided that a suitable design for the local feedback law controlling each channel flow is already available. The proposed solution converges exponentially fast towards the average consensus thanks to a Metropolis-Hastings design of the network parameters without violating the imposed constraints over time. In addition, numerical results are reported to support the theoretical findings, and the performance of the developed algorithm is discussed in the context of a realistic scenario.
Stability Constrained Optimization in High IBR-Penetrated Power Systems-Part II: Constraint Validation and Applications
Authors: Zhongda Chu, Fei Teng
Date: 2023-07-22 00:00:00
Tasks: Scheduling

Multiple operational constraints of power system stability are derived analytically and reformulated into Second-Order Cone (SOC) form through a unification method in Part I of this paper. The accuracy and conservativeness of the proposed methods are illustrated in the second part. The validity of the developed constraints is tested against dynamic simulations carried out based on the modified IEEE 39-bus system. Furthermore, the developed power system stability constraints are applied to the optimal system scheduling model. The resulting stability-constrained system scheduling problem aims to achieve most economic system operation while ensuring different stability in power systems with high Inverter-Based Resources (IBR) penetration. Moreover, based on the stability-constrained optimization model, a novel marginal unit pricing scheme is proposed to quantify the stability services of different units appropriately according to their economic value in maintaining system stability, thus providing rational incentives to the stability service provider and insightful information for the stability market development.
Stability Constrained Optimization in High IBR-Penetrated Power Systems-Part I: Constraint Development and Unification
Authors: Zhongda Chu, Fei Teng
Date: 2023-07-22 00:00:00
Tasks: Scheduling

Maintaining power system stability is becoming more and more challenging due to the ever-increasing inverter-interfaced renewable penetration in power systems. To ensure system stability during system operation and to provide appropriate incentives in the future market-based stability maintenance framework, it is essential to develop a comprehensive set of power system stability constraints which can be incorporated into system operation, market design and planning problems. In this paper, different system stability issues, including synchronization, voltage and frequency stability, are investigated and the corresponding stability conditions are analytically formulated as system operation constraints. A unified framework is further proposed to represent the stability constraints in a general form and enables effective reformulation of the impedance-based stability metrics. All the constraints are converted into linear or Second-Order-Cone (SOC) form, which can be readily implemented in any optimisation-based applications, such as system scheduling, planning and market design, thus providing significant value for multiple system stability enhancement and studies.
Recovery of Localization Errors in Sensor Networks using Inter-Agent Measurements
Authors: Shiraz Khan, Inseok Hwang
Date: 2023-07-22 00:00:00

A practical challenge which arises in the operation of sensor networks is the presence of sensor faults, biases, or adversarial attacks, which can lead to significant errors incurring in the localization of the agents, thereby undermining the security and performance of the network. We consider the problem of identifying and correcting the localization errors using inter-agent measurements, such as the distances or bearings from one agent to another, which can serve as a redundant source of information about the sensor network's configuration. The problem is solved by searching for a block sparse solution to an underdetermined system of equations, where the sparsity is introduced via the fact that the number of localization errors is typically much lesser than the total number of agents. Unlike the existing works, our proposed method does not require the knowledge of the identities of the anchors, i.e., the agents that do not have localization errors. We characterize the necessary and sufficient conditions on the sensor network configuration under which a given number of localization errors can be uniquely identified and corrected using the proposed method. The applicability of our results is demonstrated numerically by processing inter-agent distance measurements using a sequential convex programming (SCP) algorithm to identify the localization errors in a sensor network.
Dead-time Compensation Method for Bus-clamping Modulated Voltage Source Inverter
Authors: Reza Asrar Ghaderloo, Yidi Shen, Chanaka Singhabahu, Rakesh Resalayyan, Alireza Khaligh
Date: 2023-07-21 00:00:00

Bus-clamping Pulse Width Modulation (PWM) is an effective method to reduce the switching loss in a three-phase voltage source inverter (VSI). In bus-clamping PWM scheme, the phase legs are switched using high frequency PWM signals for two-third of the line cycle, while for the remaining duration of cycle, the pole voltage is clamped to either positive or negative rail of the DC bus. In PWM operation of a half bridge, a dead-time is applied between the gate signals of complementary switches to ensure safe and reliable operation. However, introduction of dead-time leads to poor power quality, increased Total Harmonic Distortion (THD) and variation in actual voltage compared to the intended pole voltage. Moreover, when the bus-clamping technique is used, the PWM has both high frequency switching region and clamped region in a line cycle, and consequently, the undesired effects of dead-time are further aggravated. Therefore, in order to enhance the quality of output voltage, this paper presents a dead-time compensation strategy for a VSI operating with bus-clamping PWM. The proposed method calculates the required compensation term to be added on the modulation signal considering wide range of operating conditions. Additionally, the compensation includes a new strategy for low current conditions near zero-crossing to avoid distortion. The proposed method is verified by simulation and experiments in a three-phase VSI with a switching frequency of 100 kHz and a fundamental frequency of 60Hz.
Large-Scale Multi-Fleet Platoon Coordination: A Dynamic Programming Approach
Authors: Ting Bai, Alexander Johansson, Karl Henrik Johansson, Jonas Mårtensson
Date: 2023-07-21 00:00:00
Tasks: Scheduling

Truck platooning is a promising technology that enables trucks to travel in formations with small inter-vehicle distances for improved aerodynamics and fuel economy. The real-world transportation system includes a vast number of trucks owned by different fleet owners, for example, carriers. To fully exploit the benefits of platooning, efficient dispatching strategies that facilitate the platoon formations across fleets are required. This paper presents a distributed framework for addressing multi-fleet platoon coordination in large transportation networks, where each truck has a fixed route and aims to maximize its own fleet's platooning profit by scheduling its waiting times at hubs. The waiting time scheduling problem of individual trucks is formulated as a distributed optimal control problem with continuous decision space and a reward function that takes non-zero values only at discrete points. By suitably discretizing the decision and state spaces, we show that the problem can be solved exactly by dynamic programming, without loss of optimality. Finally, a realistic simulation study is conducted over the Swedish road network with $5,000$ trucks to evaluate the profit and efficiency of the approach. The simulation study shows that, compared to single-fleet platooning, multi-fleet platooning provided by our method achieves around $15$ times higher monetary profit and increases the CO$_2$ emission reductions from $0.4\%$ to $5.5\%$. In addition, it shows that the developed approach can be carried out in real-time and thus is suitable for platoon coordination in large transportation systems.
What is System Dynamics Modeling? Defining Characteristics and the Opportunities they Create
Authors: Asmeret Naugle, Saeed Langarudi, Timothy Clancy
Date: 2023-07-21 00:00:00
Tasks: Philosophy

A clear definition of system dynamics modeling can provide shared understanding and clarify the impact of the field. We introduce a set of characteristics that define quantitative system dynamics, selected to capture core philosophy, describe theoretical and practical principles, and apply to historical work but be flexible enough to remain relevant as the field progresses. The defining characteristics are: (1) models are based on causal feedback structure, (2) accumulations and delays are foundational, (3) models are equation-based, (4) concept of time is continuous, and (5) analysis focuses on feedback dynamics. We discuss the implications of these principles and use them to identify research opportunities in which the system dynamics field can advance. These research opportunities include causality, disaggregation, data science and artificial intelligence, and contributing to scientific advancement. Progress in these areas has the potential to improve both the science and practice of system dynamics.
Optimizing parameter search for community detection in time evolving networks of complex systems
Authors: ItaloIvo Lima Dias Pinto, Javier Omar Garcia, Kanika Bansal
Date: 2023-07-24 00:00:00
Tasks: Community Detection

Network representations have been effectively employed to analyze complex systems across various areas and applications, leading to the development of network science as a core tool to study systems with multiple components and complex interactions. There is a growing interest in understanding the temporal dynamics of complex networks to decode the underlying dynamic processes through the temporal changes in network structure. Community detection algorithms, which are specialized clustering algorithms, have been instrumental in studying these temporal changes. They work by grouping nodes into communities based on the structure and intensity of network connections over time aiming to maximize modularity of the network partition. However, the performance of these algorithms is highly influenced by the selection of resolution parameters of the modularity function used, which dictate the scale of the represented network, both in size of communities and the temporal resolution of dynamic structure. The selection of these parameters has often been subjective and heavily reliant on the characteristics of the data used to create the network structure. Here, we introduce a method to objectively determine the values of the resolution parameters based on the elements of self-organization. We propose two key approaches: (1) minimization of the biases in spatial scale network characterization and (2) maximization of temporal scale-freeness. We demonstrate the effectiveness of these approaches using benchmark network structures as well as real-world datasets. To implement our method, we also provide an automated parameter selection software package that can be applied to a wide range of complex systems.
Atomic number estimation of dual energy cargo radiographs using a semiempirical transparency model
Authors: Peter Lalor, Areg Danagoulian
Date: 2023-07-22 00:00:00
Tasks: Image Segmentation, Semantic Segmentation

Dual energy cargo inspection systems are sensitive to both the area density and the atomic number of an imaged container due to the $Z$ dependence of photon attenuation. The ability to identify cargo contents by their atomic number enables improved detection capabilities of illicit materials. This work introduces a novel method for atomic number reconstruction by minimizing the chi-squared error between measured transparency values and a semiempirical transparency model. This method is tested using two Geant4 Monte Carlo simulated radiographic phantoms, demonstrating the ability to obtain accurate material predictions on noisy input images, even in the presence of shielding. Furthermore, we provide a simple procedure for porting this method to a commercial system, requiring an approximate model of the scanner's beam spectra and detector response, along with only three calibration measurements.
Memory Effects, Multiple Time Scales and Local Stability in Langevin Models of the S&P500 Market Correlation
Authors: Tobias Wand, Martin Heßler, Oliver Kamps
Date: 2023-07-24 00:00:00

The analysis of market correlations is crucial for optimal portfolio selection of correlated assets, but their memory effects have often been neglected. In this work, we analyse the mean market correlation of the S&P500 which corresponds to the main market mode in principle component analysis. We fit a generalised Langevin equation (GLE) to the data whose memory kernel implies that there is a significant memory effect in the market correlation ranging back at least three trading weeks. The memory kernel improves the forecasting accuracy of the GLE compared to models without memory and hence, such a memory effect has to be taken into account for optimal portfolio selection to minimise risk or for predicting future correlations. Moreover, a Bayesian resilience estimation provides further evidence for non-Markovianity in the data and suggests the existence of a hidden slow time scale that operates on much slower times than the observed daily market data. Assuming that such a slow time scale exists, our work supports previous research on the existence of locally stable market states.
A Cascade Transformer-based Model for 3D Dose Distribution Prediction in Head and Neck Cancer Radiotherapy
Authors: Tara Gheshlaghi, Shahabedin Nabavi, Samire Shirzadikia, Mohsen Ebrahimi Moghaddam, Nima Rostampour
Date: 2023-07-22 00:00:00
Tasks: Decoder, Segmentation

Radiation therapy is the primary method used to treat cancer in the clinic. Its goal is to deliver a precise dose to the planning target volume (PTV) while protecting the surrounding organs at risk (OARs). However, the traditional workflow used by dosimetrists to plan the treatment is time-consuming and subjective, requiring iterative adjustments based on their experience. Deep learning methods can be used to predict dose distribution maps to address these limitations. The study proposes a cascade model for organs at risk segmentation and dose distribution prediction. An encoder-decoder network has been developed for the segmentation task, in which the encoder consists of transformer blocks, and the decoder uses multi-scale convolutional blocks. Another cascade encoder-decoder network has been proposed for dose distribution prediction using a pyramid architecture. The proposed model has been evaluated using an in-house head and neck cancer dataset of 96 patients and OpenKBP, a public head and neck cancer dataset of 340 patients. The segmentation subnet achieved 0.79 and 2.71 for Dice and HD95 scores, respectively. This subnet outperformed the existing baselines. The dose distribution prediction subnet outperformed the winner of the OpenKBP2020 competition with 2.77 and 1.79 for dose and DVH scores, respectively. The predicted dose maps showed good coincidence with ground truth, with a superiority after linking with the auxiliary segmentation task. The proposed model outperformed state-of-the-art methods, especially in regions with low prescribed doses.
Identification Robust Inference for the Risk Premium in Term Structure Models
Authors: Frank Kleibergen, Lingwei Kong
Date: 2023-07-24 00:00:00

We propose identification robust statistics for testing hypotheses on the risk premia in dynamic affine term structure models. We do so using the moment equation specification proposed for these models in Adrian et al. (2013). We extend the subset (factor) Anderson-Rubin test from Guggenberger et al. (2012) to models with multiple dynamic factors and time-varying risk prices. Unlike projection-based tests, it provides a computationally tractable manner to conduct identification robust tests on a larger number of parameters. We analyze the potential identification issues arising in empirical studies. Statistical inference based on the three-stage estimator from Adrian et al. (2013) requires knowledge of the factors' quality and is misleading without full-rank beta's or with sampling errors of comparable size as the loadings. Empirical applications show that some factors, though potentially weak, may drive the time variation of risk prices, and weak identification issues are more prominent in multi-factor models.
Scenario Sampling for Large Supermodular Games
Authors: Bryan S. Graham, Andrin Pelican
Date: 2023-07-21 00:00:00
Tasks: Numerical Integration

This paper introduces a simulation algorithm for evaluating the log-likelihood function of a large supermodular binary-action game. Covered examples include (certain types of) peer effect, technology adoption, strategic network formation, and multi-market entry games. More generally, the algorithm facilitates simulated maximum likelihood (SML) estimation of games with large numbers of players, $T$, and/or many binary actions per player, $M$ (e.g., games with tens of thousands of strategic actions, $TM=O(10^4)$). In such cases the likelihood of the observed pure strategy combination is typically (i) very small and (ii) a $TM$-fold integral who region of integration has a complicated geometry. Direct numerical integration, as well as accept-reject Monte Carlo integration, are computationally impractical in such settings. In contrast, we introduce a novel importance sampling algorithm which allows for accurate likelihood simulation with modest numbers of simulation draws.
The Yule-Frisch-Waugh-Lovell Theorem for Linear Instrumental Variables Estimation
Authors: Deepankar Basu
Date: 2023-07-12 00:00:00

In this paper, I discuss three aspects of the Frisch-Waugh-Lovell theorem. First, I show that the theorem holds for linear instrumental variables estimation of a multiple regression model that is either exactly or overidentified. I show that with linear instrumental variables estimation: (a) coefficients on endogenous variables are identical in full and partial (or residualized) regressions; (b) residual vectors are identical for full and partial regressions; and (c) estimated covariance matrices of the coefficient vectors from full and partial regressions are equal (up to a degree of freedom correction) if the estimator of the error vector is a function only of the residual vectors and does not use any information about the covariate matrix other than its dimensions. While estimation of the full model uses the full set of instrumental variables, estimation of the partial model uses the residualized version of the same set of instrumental variables, with residualization carried out with respect to the set of exogenous variables. Second, I show that: (a) the theorem applies in large samples to the K-class of estimators, including the limited information maximum likelihood (LIML) estimator, and (b) the theorem does not apply in general to linear GMM estimators, but it does apply to the two step optimal linear GMM estimator. Third, I trace the historical and analytical development of the theorem and suggest that it be renamed as the Yule-Frisch-Waugh-Lovell (YFWL) theorem to recognize the pioneering contribution of the statistician G. Udny Yule in its development.
Power sector benefits of flexible heat pumps
Authors: Alexander Roth, Carlos Gaete-Morales, Dana Kirchem, Wolf-Peter Schill
Date: 2023-07-24 00:00:00

Heat pumps play a major role in decreasing fossil fuel use in heating. They increase electricity demand, but could also foster the system integration of variable renewable energy sources. We analyze three scenarios for expanding decentralized heat pumps in Germany by 2030, focusing on the role of buffer heat storage. Using an open-source power sector model, we assess costs, capacity investments, and emissions effects. We find that investments in solar photovoltaics can cost-effectively accompany the roll-out of heat pumps in case wind power expansion potentials are limited. Results further show that short-duration heat storage substantially reduces the need for firm capacity and battery storage. Larger heat storage sizes do not substantially change the results. Increasing the number of heat pumps from 1.7 to 10 million units could annually save around a quarter of Germany's overall natural gas consumption and around half of households' building-related CO2 emissions.