pwc-archive/papers-with-abstracts huggingface dataset:
Observer-Based Safety Monitoring of Nonlinear Dynamical Systems with Neural Networks via Quadratic Constraint Approach
Authors: Tao Wang, Yapeng Li, Zihao Mo, Wesley Cooke, Weiming Xiang
Date: 2024-11-15 00:00:00

The safety monitoring for nonlinear dynamical systems with embedded neural network components is addressed in this paper. The interval-observer-based safety monitor is developed consisting of two auxiliary neural networks derived from the neural network components of the dynamical system. Due to the presence of nonlinear activation functions in neural networks, we use quadratic constraints on the global sector to abstract the nonlinear activation functions in neural networks. By combining a quadratic constraint approach for the activation function with Lyapunov theory, the interval observer design problem is transformed into a series of quadratic and linear programming feasibility problems to make the interval observer operate with the ability to correctly estimate the system state with estimation errors within acceptable limits. The applicability of the proposed method is verified by simulation of the lateral vehicle control system.
Data-Driven Decentralized Control Design for Discrete-Time Large-Scale Systems
Authors: Jiaping Liao, Shuaizheng Lu, Tao Wang, Weiming Xiang
Date: 2024-11-15 00:00:00

In this paper, a data-driven approach is developed for controller design for a class of discrete-time large-scale systems, where a large-scale system can be expressed in an equivalent data-driven form and the decentralized controllers can be parameterized by the data collected from its subsystems, i.e., system state, control input, and interconnection input. Based on the developed data-driven method and the Lyapunov approach, a data-driven semi-definite programming problem is constructed to obtain decentralized stabilizing controllers. The proposed approach has been validated on a mass-spring chain model, with the significant advantage of avoiding extensive modeling processes.
Two-Stage Robust Optimal Operation of Distribution Networks Considering Renewable Energy and Demand Asymmetric Uncertainties
Authors: Zhisheng Xiong, Bo Zeng, Peter Palensky, Pedro P. Vergara
Date: 2024-11-15 00:00:00

This paper presents a confidence level-based distributionally information gap decision theory (CL-DIGDT) framework for the two-stage robust optimal operation of distribution networks, aiming at deriving an optimal operational scheme capable of addressing asymmetric uncertainties related to renewable energy and load demands. Building on conventional IGDT, the proposed framework utilizes the confidence level to capture the asymmetric characteristics of uncertainties and maximize the risk-averse capability of the solution in a probabilistic manner. To account for the probabilistic consideration, the imprecise Dirichlet model is employed to construct the ambiguity sets of uncertainties, reducing reliance on precise probability distributions. Consequently, a two-stage robust optimal operation model for distribution networks using CL-DIGDT is developed. An iterative method is proposed to solve the model and determine the upper and lower bounds of the objective function. Case study demonstrates that the proposed approach yields a more robust and statistically optimized solution with required accuracy compared to existing method, contributing to a reduction in first-stage cost by 0.84%, second-stage average cost by 6.7%, and significantly increasing the reliability of the solution by 8%.
Distributed Optimization by Network Flows with Spatio-Temporal Compression
Authors: Zihao Ren, Lei Wang, Xinlei Yi, Xi Wang, Deming Yuan, Tao Yang, Zhengguang Wu, Guodong Shi
Date: 2024-08-14 00:00:00
Tasks: Distributed Optimization

Several data compressors have been proposed in distributed optimization frameworks of network systems to reduce communication overhead in large-scale applications. In this paper, we demonstrate that effective information compression may occur over time or space during sequences of node communications in distributed algorithms, leading to the concept of spatio-temporal compressors. This abstraction classifies existing compressors as spatio-temporal compressors, with their effectiveness described by constructive stability criteria from nonlinear system theory. Subsequently, we apply these spatio-temporal compressors to standard continuous-time consensus flows and distributed prime-dual flows, establishing conditions ensuring convergence. Additionally, we introduce a novel observer-based distributed primal-dual continuous flow integrated with spatio-temporal compressors, which provides broader convergence conditions. These continuous flows achieve exponential convergence to the global optimum when the objective function is strongly convex and can be discretized using Euler approximations. Finally, numerical simulations illustrate the versatility of the proposed spatio-temporal compressors and verify the convergence of algorithms.
Enforcing Cooperative Safety for Reinforcement Learning-based Mixed-Autonomy Platoon Control
Authors: Jingyuan Zhou, Longhao Yan, Jinhao Liang, Kaidi Yang
Date: 2024-11-15 00:00:00
Tasks: Conformal Prediction, Multi-agent Reinforcement Learning

It is recognized that the control of mixed-autonomy platoons comprising connected and automated vehicles (CAVs) and human-driven vehicles (HDVs) can enhance traffic flow. Among existing methods, Multi-Agent Reinforcement Learning (MARL) appears to be a promising control strategy because it can manage complex scenarios in real time. However, current research on MARL-based mixed-autonomy platoon control suffers from several limitations. First, existing MARL approaches address safety by penalizing safety violations in the reward function, thus lacking theoretical safety guarantees due to the black-box nature of RL. Second, few studies have explored the cooperative safety of multi-CAV platoons, where CAVs can be coordinated to further enhance the system-level safety involving the safety of both CAVs and HDVs. Third, existing work tends to make an unrealistic assumption that the behavior of HDVs and CAVs is publicly known and rationale. To bridge the research gaps, we propose a safe MARL framework for mixed-autonomy platoons. Specifically, this framework (i) characterizes cooperative safety by designing a cooperative Control Barrier Function (CBF), enabling CAVs to collaboratively improve the safety of the entire platoon, (ii) provides a safety guarantee to the MARL-based controller by integrating the CBF-based safety constraints into MARL through a differentiable quadratic programming (QP) layer, and (iii) incorporates a conformal prediction module that enables each CAV to estimate the unknown behaviors of the surrounding vehicles with uncertainty qualification. Simulation results show that our proposed control strategy can effectively enhance the system-level safety through CAV cooperation of a mixed-autonomy platoon with a minimal impact on control performance.
Exploring the Influence of Residential Electric Vehicle Charging on Distribution System Hosting Capacity -- A Case-Study in Arizona
Authors: Mohammad Golgol, Anamitra Pal, Vijay Vittal, Christine Fini, Ernest Palomino, Kyle Girardi
Date: 2024-11-15 00:00:00

The installation of high-capacity fast chargers for electric vehicles (EVs) is posing a significant risk to the distribution grid as the increased demand from widespread residential EV charging could exceed the technical limits of the distribution system. Addressing this issue is critical, given that current infrastructure upgrades to enhance EV hosting capacity are both costly and time-consuming. Moreover, the inherent uncertainties associated with EV charging parameters make it challenging for power utilities to accurately assess the impact of EVs added to specific locations. To address these knowledge gaps, this study (a) introduces an algorithm to coordinate residential EV charging, and (b) proposes a comprehensive framework that evaluates all transformers within a feeder. The proposed method is applied to a real-world feeder, which includes 120 transformers of varying capacities. The results demonstrate that this approach effectively manages a substantial number of EVs without overloading any of the transformers, while also pinpointing locations that must be prioritized for future upgrades. This framework can serve as a valuable reference for utilities when conducting distribution system evaluations for supporting the growing EV penetration.
A Secure Estimator with Gaussian Bernoulli Mixture Model
Authors: Xingzhou Chen, Nachuan Yang, Peihu Duan, Shilei Li, Ling Shi
Date: 2024-11-15 00:00:00
Tasks: Computational Efficiency, model, State Estimation

The implementation of cyber-physical systems in real-world applications is challenged by safety requirements in the presence of sensor threats. Most cyber-physical systems, in particular the vulnerable multi-sensor systems, struggle to detect the attack in observation signals. In this paper, we tackle this issue by proposing a Gaussian-Bernoulli Secure (GBS) estimator, which effectively transforms the assessment of sensor status into an optimal estimation problem concerning the system state and observation indicators. It encompasses two theoretical sub-problems: sequential state estimation with partial observations and estimation updates with disordered new observations. Within the framework of Kalman filter, we derive closed-form solutions for these two issues. However, due to their computational inefficiency, we propose the iterative approach employing proximal gradient descent to accelerate the estimation update. We conduct comprehensive experiments from three perspectives: computational efficiency, detection and estimation performance, and characterization of observation error. Our GBS estimator shows the improvements compared to other methods.
Regulating Stability Margins in Symbiotic Control: A Low-Pass Filter Approach
Authors: Emre Yildirim, Tansel Yucelen, John T. Hrynuk
Date: 2024-11-15 00:00:00

Symbiotic control synergistically integrates fixed-gain control and adaptive learning architectures to mitigate system uncertainties more predictably than adaptive learning alone and without requiring prior knowledge of uncertainty bounds as compared to fixed-gain control alone. Specifically, increasing the fixed-gain control parameter achieves a desired level of closed-loop system performance while the adaptive law simultaneously learns and suppresses the system uncertainties. However, stability margins can be reduced when this parameter is large and this paper aims to address this practical challenge. To this end, we propose a new fixed-gain control architecture predicated on a low-pass filter approach to regulate stability margins in the symbiotic control framework. In addition to the presented system-theoretical results focusing on the stability of the closed-loop system, we provide two illustrative numerical examples to demonstrate how the low-pass filter parameters are chosen for the stability margin regulation problem without significantly compromising the closed-loop system performance.
Exploring the Use of Autonomous Unmanned Vehicles for Supporting Power Grid Operations
Authors: Yuqi Zhou, Cong Feng, Mingzhi Zhang, Rui Yang
Date: 2024-11-14 00:00:00
Tasks: Autonomous Vehicles

This paper explores the use of autonomous unmanned vehicles to support power grid operations. With built-in batteries and the capability to carry additional battery energy storage, the rising number of autonomous vehicles can represent a substantial amount of capacity that is currently underutilized in the power grid. Unlike traditional electric vehicles that require drivers, the operations of autonomous vehicles can be performed without human intervention. To guide idle vehicles to autonomously support power grids, we propose a tractable optimization-based method to effectively integrate these "mobile batteries" into grid operations. During real-time operations, the vehicles are strategically routed to target locations to maintain power balance and reduce operating costs. Numerical studies have confirmed both the validity and the scalability of the proposed algorithm to efficiently integrate autonomous vehicles into routine power system operations.
ModelPredictiveControl.jl: advanced process control made easy in Julia
Authors: Francis Gagnon, Alex Thivierge, André Desbiens, Fredrik Bagge Carlson
Date: 2024-11-14 00:00:00

Proprietary closed-source software is still the norm in advanced process control. Transparency and reproducibility are key aspects of scientific research. Free and open-source toolkit can contribute to the development, sharing and advancement of new and efficient control approaches, and the industrial sector will certainly benefit from them. This paper presents ModelPredictiveControl.jl, an open-source software package for designing model predictive controllers in the Julia programming language. It is designed to be easy to use and modular, while providing advanced features like nonlinear control and moving horizon estimation. It relies on powerful control system and mathematical optimization frameworks to simplify the construction and testing of state estimators and predictive controllers. It also integrates with the standard plotting library to quickly visualize closed-loop data. The paper presents the main functionalities and illustrates them with two case studies in simulation. The first example is a continuously stirred tank reactor described by linear dynamics. The second one implements a nonlinear, an economic, and a successive linearization model predictive controllers for an inverted pendulum. The solving times are benchmarked against equivalent implementations in MATLAB to show the efficiency of the package.
Integrating Fuzzy Set Theory with Pandora Temporal Fault Trees for Dynamic Failure Analysis of Complex Systems
Authors: Hitesh Khungla, Mohit Kumar
Date: 2024-11-14 00:00:00

Pandora temporal fault tree, as one notable extension of the fault tree, introduces temporal gates and temporal laws. Pandora Temporal Fault Tree(TFT) enhances the capability of fault trees and enables the modeling of system failure behavior that depends on sequences. The calculation of system failure probability in Pandora TFT relies on precise probabilistic information on component failures. However, obtaining such precise failure data can often be challenging. The data may be uncertain as historical records are used to derive failure data for system components. To mitigate this uncertainty, in this study, we proposed a method that integrates fuzzy set theory with Pandora TFT. This integration aims to enable dynamic analysis of complex systems, even in cases where quantitative failure data of components is unreliable or imprecise. The proposed work introduces the development of Fuzzy AND, Fuzzy OR, Fuzzy PAND, and Fuzzy POR logic gates for Pandora TFT. We also introduce a fuzzy importance measure for criticality analysis of basic events. All events in our analysis are assumed to have exponentially distributed failures, with their failure rates represented as triangular fuzzy numbers. We illustrate the proposed method through a case study of the Aircraft Fuel Distribution System (AFDS), highlighting its practical application and effectiveness in analyzing complex systems. The results are compared with existing results from Petri net and Bayesian network techniques to validate the findings.
Two-step registration method boosts sensitivity in longitudinal fixel-based analyses
Authors: Aurélie Lebrun, Michel Bottlaender, Julien Lagarde, Marie Sarazin, Yann Leprince
Date: 2024-11-15 00:00:00
Tasks: Sensitivity

Longitudinal analyses are increasingly used in clinical studies as they allow the study of subtle changes over time within the same subjects. In most of these studies, it is necessary to align all the images studied to a common reference by registering them to a template. In the study of white matter using the recently developed fixel-based analysis (FBA) method, this registration is important, in particular because the fiber bundle cross-section metric is a direct measure of this registration. In the vast majority of longitudinal FBA studies described in the literature, sessions acquired for a same subject are directly independently registered to the template. However, it has been shown in T1-based morphometry that a 2-step registration through an intra-subject average can be advantageous in longitudinal analyses. In this work, we propose an implementation of this 2-step registration method in a typical longitudinal FBA aimed at investigating the evolution of white matter changes in Alzheimer's disease (AD). We compared at the fixel level the mean absolute effect and standard deviation yielded by this registration method and by a direct registration, as well as the results obtained with each registration method for the study of AD in both fixelwise and tract-based analyses. We found that the 2-step method reduced the variability of the measurements and thus enhanced statistical power in both types of analyses.
A reconfigurable non-linear active metasurface for coherent wave down-conversion
Authors: Pouria Sanjari, Firooz Aflatouni
Date: 2024-11-15 00:00:00

Metasurfaces can manipulate the amplitude and phase of electromagnetic waves, offering applications ranging from antenna design and cloaking to imaging and communication. Additionally, temporal, and non-linear metasurfaces have the potential to adjust the frequency of impinging waves, driving advancements in frequency conversion, sensing, and quantum systems. Here, we report the demonstration of a non-linear active electronic-photonic metasurface that transfers information from an impinging optical wave to a millimeter-wave (mm-wave) beam. The proof-of-concept metasurface is designed to radiate a steerable 28GHz beam when illuminated with an optical wave at 193THz and consists of optically synchronized electronic-photonic chips tiled on a printed circuit board containing a microstrip patch antenna array. Input light, modulated with a data-encoded mm-wave carrier, is coupled into electronic-photonic chips using microlenses. Within each chip, the mm-wave signal is detected, phase-adjusted, amplified, and routed to an off-chip antenna. Beam-steering over a range of 60$^{\circ}$ in elevation and azimuth and data transmission at 2Gb/s over a fiber-wireless link is demonstrated. Free-space optical synchronization can significantly reduce the complexity of large-scale metasurfaces composed of non-uniform or randomly placed elements, is compatible with scalable architectures, and facilitates data transfer and mm-wave beam shaping, allowing for large-scale high-bandwidth and energy-efficient links with reduced complexity for the next generation communication, computation, sensing and quantum systems.
Sharp Testable Implications of Encouragement Designs
Authors: Yuehao Bai, Shunzhuang Huang, Max Tabord-Meehan
Date: 2024-11-14 00:00:00

This paper studies a potential outcome model with a continuous or discrete outcome, a discrete multi-valued treatment, and a discrete multi-valued instrument. We derive sharp, closed-form testable implications for a class of restrictions on potential treatments where each value of the instrument encourages towards at most one unique treatment choice; such restrictions serve as the key identifying assumption in several prominent recent empirical papers. Borrowing the terminology used in randomized experiments, we call such a setting an encouragement design. The testable implications are inequalities in terms of the conditional distributions of choices and the outcome given the instrument. Through a novel constructive argument, we show these inequalities are sharp in the sense that any distribution of the observed data that satisfies these inequalities is compatible with this class of restrictions on potential treatments. Based on these inequalities, we propose tests of the restrictions. In an empirical application, we show some of these restrictions are violated and pinpoint the substitution pattern that leads to the violation.
Bayesian estimation of finite mixtures of Tobit models
Authors: Caio Waisman
Date: 2024-11-14 00:00:00
Tasks: Data Augmentation

This paper outlines a Bayesian approach to estimate finite mixtures of Tobit models. The method consists of an MCMC approach that combines Gibbs sampling with data augmentation and is simple to implement. I show through simulations that the flexibility provided by this method is especially helpful when censoring is not negligible. In addition, I demonstrate the broad utility of this methodology with applications to a job training program, labor supply, and demand for medical care. I find that this approach allows for non-trivial additional flexibility that can alter results considerably and beyond improving model fit.
Estimating the Cost of Informal Care with a Novel Two-Stage Approach to Individual Synthetic Control
Authors: Maria Petrillo, Daniel Valdenegro, Charles Rahal, Yanan Zhang, Gwilym Pryce, Matthew R. Bennett
Date: 2024-11-15 00:00:00

Informal carers provide the majority of care for people living with challenges related to older age, long-term illness, or disability. However, the care they provide often results in a significant income penalty for carers, a factor largely overlooked in the economics literature and policy discourse. Leveraging data from the UK Household Longitudinal Study, this paper provides the first robust causal estimates of the caring income penalty using a novel individual synthetic control based method that accounts for unit-level heterogeneity in post-treatment trajectories over time. Our baseline estimates identify an average relative income gap of up to 45%, with an average decrease of {\pounds}162 in monthly income, peaking at {\pounds}192 per month after 4 years, based on the difference between informal carers providing the highest-intensity of care and their synthetic counterparts. We find that the income penalty is more pronounced for women than for men, and varies by ethnicity and age.
Natural resources balance sheets accounting: theoretical framework and practice in the Shaanxi province of China
Authors: Wentao Wang, Guoping Li, Andreas Kontoleon, Yiming Ma, Weishan Guo
Date: 2024-11-15 00:00:00
Tasks: Management

To achieve sustainable development, there is widespread of the need to protect natural resource and improve government oversight in achieving China's economic security and ecological civilization. Compilation of natural resources balance sheet (NRBS) and enhancement of resources management are becoming an important topic in China. How to compile NRBS to affix the responsibility for government and officials for inadequate supervision is still not resolved satisfactorily. This paper proposes the NRBS to enable governments to identify the importance of natural resource restoration and to hold leading cadres accountable for a lack of adequate supervision. The NRBS consist of three accounts: natural resource assets, natural resource liabilities, and net worth. Important components of the NRBS for the liabilities account with a property rights regime are developed to measure and assign responsibility. The compilation of an NRBS is applied to the Chinese province of Shaanxi as an illustration to demonstrate that the accounting framework and the compilation steps are tractable using financial methods and available data. The accounting results of natural resource assets and liabilities unveil the threat to resource management and the policy implications to government and officials. Finally, the advantages and limitations of NRBS are discussed.