pwc-archive/papers-with-abstracts huggingface dataset:
Towards a General Market for Cloud-Edge-IoT Continuum
Authors: Hao Che, Hong Jiang, Zhijun Wang
Date: 2024-09-23 00:00:00

Recent years have witnessed the proposals aiming at enabling Vertical, two-sided markets with a Single Marketplace (or exchange) (VSMs) for computing and data resources/services (products) offerings in a multi-cloud and crowdsourced IoT-edge sensing environment. A VSM is designed vertically from bottom up with a broker being a built-in component of the marketplace. While preventing seller lock-in and improving efficiency and availability, a VSM suffers from a key weakness from a buyer's perspective, i.e., the broker and the corresponding marketplace lock-in, which may lead to suboptimal shopping experience for buyers, due to marketplace monopoly by the broker and limited choice of products in the marketplace. In this position paper, we argue that a Horizontal two-sided market with Multiple Marketplaces (HMM), resembling the global stock market, should be developed. In an HMM, different marketplaces may be operated by different parties and sell similar and/or different types of products, e.g., computing and/or sensory data products. A broker is no longer a built-in component of any given marketplace. Instead, it may cover multiple marketplaces at the same time and there can be more than one broker in the HMM. Both the number and types of marketplaces and brokers may grow independently or scale horizontally to meet the growing demand. A buyer shops for a broker through whom the buyer gains access to the needed products sold in the marketplace(s) the broker covers and from whom the buyer receives various possible services, e.g., discount, value-added, or full services. An HMM not only overcomes the key weakness of a VSM but also allows the market to grow incrementally and organically. Finally, two example use cases are given to illustrate the benefits of HMM.
A Contract Theory for Layered Control Architectures
Authors: Manuel Mazo Jr., Will Compton, Max H. Cohen, Aaron D. Ames
Date: 2024-09-23 00:00:00

Autonomous systems typically leverage layered control architectures with a combination of discrete and continuous models operating at different timescales. As a result, layered systems form a new class of hybrid systems composed of systems operating on a diverse set of continuous and discrete signals. This paper formalizes the notion of a layered (hierarchical) control architecture through a theory of relations between its layers. This theory enables us to formulate contracts within layered control systems -- these define interfaces between layers and isolate the design of each layer, guaranteeing that composition of contracts at each layer results in a contract capturing the desired system-wide specification. Thus, the proposed theory yields the ability to analyze layered control architectures via a compositional approach.
Spiking Nonlinear Opinion Dynamics (S-NOD) for Agile Decision-Making
Authors: Charlotte Cathcart, Ian Xul Belaustegui, Alessio Franci, Naomi Ehrich Leonard
Date: 2024-09-18 00:00:00
Tasks: Decision Making, Robot Navigation

We present, analyze, and illustrate a first-of-its-kind model of two-dimensional excitable (spiking) dynamics for decision-making over two options. The model, Spiking Nonlinear Opinion Dynamics (S-NOD), provides superior agility, characterized by fast, flexible, and adaptive response to rapid and unpredictable changes in context, environment, or information received about available options. S-NOD derives through the introduction of a single extra term to the previously presented Nonlinear Opinion Dynamics (NOD) for fast and flexible multi-agent decision-making behavior. The extra term is inspired by the fast-positive, slow-negative mixed-feedback structure of excitable systems. The agile behaviors brought about by the new excitable nature of decision-making driven by S-NOD are analyzed in a general setting and illustrated in an application to multi-robot navigation around human movers.
Distributed Primal-Dual Interior Point Framework for Analyzing Infeasible Combined Transmission and Distribution Grid Networks
Authors: Muhammad Hamza Ali, Amritanshu Pandey
Date: 2024-09-22 00:00:00

The proliferation of distributed energy resources has heightened the interactions between transmission and distribution (T&D) systems, necessitating novel analyses for the reliable operation and planning of interconnected T&D networks. A critical gap is an analysis approach that identifies and localizes the weak spots in the combined T\&D networks, providing valuable information to system planners and operators. The research goal is to efficiently model and simulate infeasible (i.e. unsolvable in general settings) combined positive sequence transmission and three-phase distribution networks with a unified solution algorithm. We model the combined T&D network with the equivalent circuit formulation. To solve the overall T&D network, we build a Gauss-Jacobi-Newton (GJN) based distributed primal dual interior point optimization algorithm capable of isolating weak nodes. We validate the approach on large combined T&D networks with 70k+ T and 15k+ D nodes and demonstrate performance improvement over the alternating direction method of multipliers (ADMM) method.
A Review of Scalable and Privacy-Preserving Multi-Agent Frameworks for Distributed Energy Resources
Authors: Xiang Huo, Hao Huang, Katherine R. Davis, H. Vincent Poor, Mingxi Liu
Date: 2024-09-22 00:00:00
Tasks: Privacy Preserving

Distributed energy resources (DERs) are gaining prominence due to their advantages in improving energy efficiency, reducing carbon emissions, and enhancing grid resilience. Despite the increasing deployment, the potential of DERs has yet to be fully explored and exploited. A fundamental question restrains the management of numerous DERs in large-scale power systems, "How should DER data be securely processed and DER operations be efficiently optimized?" To address this question, this paper considers two critical issues, namely privacy for processing DER data and scalability in optimizing DER operations, then surveys existing and emerging solutions from a multi-agent framework perspective. In the context of scalability, this paper reviews state-of-the-art research that relies on parallel control, optimization, and learning within distributed and/or decentralized information exchange structures, while in the context of privacy, it identifies privacy preservation measures that can be synthesized into the aforementioned scalable structures. Despite research advances in these areas, challenges remain because these highly interdisciplinary studies blend a wide variety of scalable computing architectures and privacy preservation techniques from different fields, making them difficult to adapt in practice. To mitigate this issue, this paper provides a holistic review of trending strategies that orchestrate privacy and scalability for large-scale power system operations from a multi-agent perspective, particularly for DER control problems. Furthermore, this review extrapolates new approaches for future scalable, privacy-aware, and cybersecure pathways to unlock the full potential of DERs through controlling, optimizing, and learning generic multi-agent-based cyber-physical systems.
Few-Shot Testing of Autonomous Vehicles with Scenario Similarity Learning
Authors: Shu Li, Honglin He, Jingxuan Yang, Jianming Hu, Yi Zhang, Shuo Feng
Date: 2024-09-22 00:00:00
Tasks: Autonomous Vehicles

Testing and evaluation are critical to the development and deployment of autonomous vehicles (AVs). Given the rarity of safety-critical events such as crashes, millions of tests are typically needed to accurately assess AV safety performance. Although techniques like importance sampling can accelerate this process, it usually still requires too many numbers of tests for field testing. This severely hinders the testing and evaluation process, especially for third-party testers and governmental bodies with very limited testing budgets. The rapid development cycles of AV technology further exacerbate this challenge. To fill this research gap, this paper introduces the few-shot testing (FST) problem and proposes a methodological framework to tackle it. As the testing budget is very limited, usually smaller than 100, the FST method transforms the testing scenario generation problem from probabilistic sampling to deterministic optimization, reducing the uncertainty of testing results. To optimize the selection of testing scenarios, a cross-attention similarity mechanism is proposed to learn to extract the information of AV's testing scenario space. This allows iterative searches for scenarios with the smallest evaluation error, ensuring precise testing within budget constraints. Experimental results in cut-in scenarios demonstrate the effectiveness of the FST method, significantly enhancing accuracy and enabling efficient, precise AV testing.
Robust Data-Driven Tube-Based Zonotopic Predictive Control with Closed-Loop Guarantees
Authors: Mahsa Farjadnia, Angela Fontan, Amr Alanwar, Marco Molinari, Karl Henrik Johansson
Date: 2024-09-22 00:00:00

This work proposes a robust data-driven tube-based zonotopic predictive control (TZPC) approach for discrete-time linear systems, designed to ensure stability and recursive feasibility in the presence of bounded noise. The proposed approach consists of two phases. In an initial learning phase, we provide an over-approximation of all models consistent with past input and noisy state data using zonotope properties. Subsequently, in a control phase, we formulate an optimization problem, which by integrating terminal ingredients is proven to be recursively feasible. Moreover, we prove that implementing this data-driven predictive control approach guarantees robust exponential stability of the closed-loop system. The effectiveness and competitive performance of the proposed control strategy, compared to recent data-driven predictive control methods, are illustrated through numerical simulations.
Exploring the Use of Contingency for Nuclear Electrical Studies
Authors: Cameron Khanpour, Jon T. Fontejon
Date: 2024-09-22 00:00:00

This paper examines the use of contingency analysis for a nuclear power plant to determine its potential benefits for the nuclear industry. Various N-1 contingencies were analyzed for a model of an existing nuclear plant, primarily inspecting voltage violations resulting from a failure. Remedial Actions Schemes were suggested to support the reduction of voltage violations in the event of a failure within the system. Many of the schemes presented were solved by existing redundancies and protection schemes that have been provided through the use of industry standard bounding analysis in the design process. This paper proposes the future use of real-time contingency analysis for nuclear power plants, conducted using constantly updating voltage, current, and power measurements through the system. This will provide real-time information of the system and can serve as historical data to reduce the analysis needed for pending design changes in the plant.
Available Transfer Capability Calculation for Wind-Integrated Power Systems Considering Wind Speed Spatiotemporal Correlation and Primal-Dual Interior Point Method
Authors: Xia-Liang Huangpu
Date: 2024-09-21 00:00:00

This paper explores the intricate effects of wind power integration on the Available Transfer Capability (ATC) of power systems, emphasizing the significance of spatiotemporal correlations in wind speed. We present an innovative optimal power flow model that integrates the Primal-Dual Interior Point Method (PDIPM), ensuring both computational accuracy and efficiency. This research pioneers a systematic analysis of how spatiotemporal wind speed correlations influence wind power output, thereby refining ATC calculations and improving prediction reliability. Furthermore, we assess the impacts of wind farm integration capacity, location, and connection methods on ATC, offering valuable insights for power system planning and market operations.
Data-driven Modeling of Linearizable Power Flow for Large-scale Grid Topology Optimization
Authors: Young-ho Cho, Hao Zhu
Date: 2024-09-21 00:00:00
Tasks: Computational Efficiency

Effective power flow (PF) modeling critically affects the solution accuracy and computational complexity of large-scale grid optimization problems. Especially for grid optimization involving flexible topology to enhance resilience, obtaining a tractable yet accurate approximation of nonlinear AC-PF is essential. This work puts forth a data-driven approach to obtain piecewise linear (PWL) PF approximation using an innovative neural network (NN) architecture, effectively aligning with the inherent generative structure of AC-PF equations. Accordingly, our proposed generative NN (GenNN) method directly incorporates binary topology variables, efficiently enabling a mixed-integer linear program (MILP) formulation for grid optimization tasks like optimal transmission switching (OTS) and restoration ordering problems (ROP). To attain model scalability for large-scale applications, we develop an area-partitioning-based sparsification approach by using fixed-size areas to attain a linear growth rate of model parameters, as opposed to the quadratic one of existing work. Numerical tests on the IEEE 118-bus and 6716-bus synthetic Texas grid demonstrate that our sparse GenNN achieves superior accuracy and computational efficiency, substantially outperforming existing approaches in large-scale PF modeling and topology optimization.
Safe Control of Grid-Interfacing Inverters with Current Magnitude Limits
Authors: Trager Joswig-Jones, Baosen Zhang
Date: 2024-09-20 00:00:00

Grid-interfacing inverters allow renewable resources to be connected to the electric grid and offer fast and programmable control responses. However, inverters are subject to significant physical constraints. One such constraint is a current magnitude limit required to protect semiconductor devices. While many current limiting methods are available, they can often unpredictably alter the behavior of the inverter control during overcurrent events leading to instability or poor performance. In this paper, we present a safety filter approach to limit the current magnitude of inverters controlled as voltage sources. The safety filter problem is formulated with a control barrier function constraint that encodes the current magnitude limit. To ensure feasibility of the problem, we prove the existence of a safe linear controller for a specified reference. This approach allows for the desired voltage source behavior to be minimally altered while safely limiting the current output.
State space models, emergence, and ergodicity: How many parameters are needed for stable predictions?
Authors: Ingvar Ziemann, Nikolai Matni, George J. Pappas
Date: 2024-09-20 00:00:00
Tasks: Self-Supervised Learning, State Space Models

How many parameters are required for a model to execute a given task? It has been argued that large language models, pre-trained via self-supervised learning, exhibit emergent capabilities such as multi-step reasoning as their number of parameters reach a critical scale. In the present work, we explore whether this phenomenon can analogously be replicated in a simple theoretical model. We show that the problem of learning linear dynamical systems -- a simple instance of self-supervised learning -- exhibits a corresponding phase transition. Namely, for every non-ergodic linear system there exists a critical threshold such that a learner using fewer parameters than said threshold cannot achieve bounded error for large sequence lengths. Put differently, in our model we find that tasks exhibiting substantial long-range correlation require a certain critical number of parameters -- a phenomenon akin to emergence. We also investigate the role of the learner's parametrization and consider a simple version of a linear dynamical system with hidden state -- an imperfectly observed random walk in $\mathbb{R}$. For this situation, we show that there exists no learner using a linear filter which can succesfully learn the random walk unless the filter length exceeds a certain threshold depending on the effective memory length and horizon of the problem.
The system dynamics analysis, resilient and fault-tolerant control for cyber-physical systems
Authors: Linlin Li, Steven X. Ding, Liutao Zhou, Maiying Zhong, Kaixiang Peng
Date: 2024-09-20 00:00:00

This paper is concerned with the detection, resilient and fault-tolerant control issues for cyber-physical systems. To this end, the impairment of system dynamics caused by the defined types of cyber-attacks and process faults is analyzed. Then, the relation of the system input and output signals with the residual subspaces spanned by both the process and the controller is studied. Considering the limit capacity of standard observer-based detection and feedback control schemes in detecting and handling the cyber-attacks, a modified configuration for cyber-physical systems is developed by transmitting the combinations of the input and output residuals instead of the input and output signals, which is facile for dealing with both the process faults and cyber-attacks. It is followed by the integrated design of fault and attack detection, resilient and fault-tolerant control schemes. To enhance the detectability of cyber-attacks, the potential stealthy attack mechanisms on deteriorating the tracking behavior and feedback control performance are developed from the attackers' point of view, and the associated detection schemes for such stealthy attacks are proposed from the defenders' point of view. A case study on the robotino system is utilized to demonstrate the proposed resilient cyber-physical configuration.
Balanced Truncation via Tangential Interpolation
Authors: Umair Zulfiqar, Zhi-Hua Xiao, Qiu-Yan Song, Victor Sreeram
Date: 2024-09-20 00:00:00

This paper examines the construction of rth-order truncated balanced realizations via tangential interpolation at r specified interpolation points. It is demonstrated that when the truncated Hankel singular values are negligible-that is, when the discarded states are nearly uncontrollable and unobservable-balanced truncation simplifies to a bi-tangential Hermite interpolation problem at r interpolation points. In such cases, the resulting truncated balanced realization is nearly H2-optimal and thus interpolates the original model at the mirror images of its poles along its residual directions. Like standard H2-optimal model reduction, where the interpolation points and tangential directions that yield a local optimum are not known, in balanced truncation as well, the interpolation points and tangential directions required to produce a truncated balanced realization remain unknown. To address this, we propose an iterative tangential interpolation-based algorithm for balanced truncation. Upon convergence, the algorithm yields a low-rank truncated balanced realization that accurately preserves the r largest Hankel singular values of the original system. An adaptive scheme to automatically select the order r of the reduced model is also proposed. The algorithm is fully automatic, choosing both the interpolation data and the model order without user intervention. Additionally, an adaptive low-rank solver for Lyapunov equations based on tangential interpolation is proposed, automatically selecting both the interpolation data and the rank without user intervention. The performance of the proposed algorithms is evaluated on benchmark models, confirming their efficacy.
Wireless Interconnection Network (WINE) for Post-Exascale High-Performance Computing
Authors: Hong Ki Kim, Yong Hun Jang, Hee Soo Kim, Won Young Kang, Young-Chai Ko, Sang Hyun Lee
Date: 2024-09-20 00:00:00

Interconnection networks, or `interconnects,' play a crucial role in administering the communication among computing units of high-performance computing (HPC) systems. Efficient provisioning of interconnects minimizes the processing delay wherein computing units await information sharing between each other, thereby enhancing the overall computation efficiency. Ideally, interconnects are designed with topologies tailored to match specific workflows, requiring diverse structures for different applications. However, since modifying their structures mid-operation renders impractical, indirect communication incurs across distant units. In managing numerous long-routed data deliveries, heavy burdens on the network side may lead to the under-utilization of computing resources. In view of state-of-the-art HPC paradigms that solicit dense interconnections for diverse computation-hungry applications, this article presents a versatile wireless interconnecting framework, coined as Wireless Interconnection NEtwork (WINE). The framework exploits cutting-edge wireless technologies that promote workload adaptability and scalability of modern interconnects. Design and implementation of wirelessly reliable links are strategized under network-oriented scrutiny of HPC architectures. A virtual HPC platform is developed to assess WINE's feasibilities, verifying its practicality for integration into modern HPC infrastructures.
Aircraft Conflict Detection and Avoidance through Interacting Multiple Model (IMM) Estimation
Authors: Raja Manish, David Webster
Date: 2024-09-19 00:00:00

The practical problem of tracking a maneuvering aircraft during flight has always been a crucial task in order to safeguard airborne assets from unknown threats. Therefore, the need for an efficient target detection and identification technique is substantial and growing. The multiple model (MM) estimation have proven to be one of the most reliable and accurate among various filtering algorithms. In this paper we will implement the Interacting Multiple Model (IMM) estimation technique for the aforementioned purpose of target identification. This target's motion, though defined by predefined dynamics, is obscured due to the noises from tracking sensors. The algorithm intends to predict the location of target and provide feedback maneuver to the reference aircraft in order to avoid a conflict.
Informative Input Design for Dynamic Mode Decomposition
Authors: Joshua Ott, Mykel J. Kochenderfer, Stephen Boyd
Date: 2024-09-19 00:00:00

Efficiently estimating system dynamics from data is essential for minimizing data collection costs and improving model performance. This work addresses the challenge of designing future control inputs to maximize information gain, thereby improving the efficiency of the system identification process. We propose an approach that integrates informative input design into the Dynamic Mode Decomposition with control (DMDc) framework, which is well-suited for high-dimensional systems. By formulating an approximate convex optimization problem that minimizes the trace of the estimation error covariance matrix, we are able to efficiently reduce uncertainty in the model parameters while respecting constraints on the system states and control inputs. This method outperforms traditional techniques like Pseudo-Random Binary Sequences (PRBS) and orthogonal multisines, which do not adapt to the current system model and often gather redundant information. We validate our approach using aircraft and fluid dynamics simulations to demonstrate the practical applicability and effectiveness of our method. Our results show that strategically planning control inputs based on the current model enhances the accuracy of system identification while requiring less data. Furthermore, we provide our implementation and simulation interfaces as an open-source software package, facilitating further research development and use by industry practitioners.