pwc-archive/papers-with-abstracts huggingface dataset:
A Stable Polygamy Approach to Spectrum Access with Channel Reuse
Authors: Dan Ben Ami, Kobi Cohen
Date: 2024-08-22 00:00:00

We introduce a new and broader formulation of the stable marriage problem (SMP), called the stable polygamy problem (SPP), where multiple individuals from a larger group $L$ of $|L|$ individuals can be matched with a single individual from a smaller group $S$ of $|S|$ individuals. Each individual $\ell \in L$ possesses a social constraint set $C_{\ell}$ that contains a subset of $L$ with whom they cannot coexist harmoniously. We define a generalized concept of stability based on the preference and constraints of the individuals. We explore two common settings: common utility, where the utility of a match is the same for individuals from both sets, and preference ranking, where each individual has a preference ranking for every other individual in the opposite set. Our analysis is conducted using a novel graph-theoretical framework. The classic SMP has been investigated in recent years for spectrum access to match cells or users to channels, where only one-to-one matching is allowed. By contrast, the new SPP formulation allows us to solve more general models with channel reuse, where multiple users may access the same channel simultaneously. Interestingly, we show that classic algorithms, such as propose and reject (P&R), and Hungarian method are no longer efficient in the polygamy setting. We develop efficient algorithms to solve the SPP in polynomial time, tailored for implementations in spectrum access with channel reuse. We analytically show that our algorithm always solves the SPP with common utility. While the SPP with preference ranking cannot be solved by any algorithm in all cases, we prove that our algorithm effectively solves it in specific graph structures representing strong and weak interference regimes. Simulation results demonstrate the efficiency of our algorithms across various spectrum access scenarios.
Modularized data-driven approximation of the Koopman operator and generator
Authors: Yang Guo, Manuel Schaller, Karl Worthmann, Stefan Streif
Date: 2024-08-22 00:00:00
Tasks: Transfer Learning

Extended Dynamic Mode Decomposition (EDMD) is a widely-used data-driven approach to learn an approximation of the Koopman operator. Consequently, it provides a powerful tool for data-driven analysis, prediction, and control of nonlinear dynamical (control) systems. In this work, we propose a novel modularized EDMD scheme tailored to interconnected systems. To this end, we utilize the structure of the Koopman generator that allows to learn the dynamics of subsystems individually and thus alleviates the curse of dimensionality by considering observable functions on smaller state spaces. Moreover, our approach canonically enables transfer learning if a system encompasses multiple copies of a model as well as efficient adaption to topology changes without retraining. We provide finite-data bounds on the estimation error using tools from graph theory. The efficacy of the method is illustrated by means of various numerical examples.
Robust Input Shaping Vibration Control via Extended Kalman Filter-Incorporated Residual Neural Network
Authors: Weiyi Yang, Shuai Li, Xin Luo
Date: 2024-08-22 00:00:00

With the rapid development of industry, the vibration control of flexible structures and underactuated systems has been increasingly gaining attention. Input shaping technology enables stable performance for high-speed motion in industrial motion systems. However, existing input shapers generally suffer from the ineffective control performance due to the neglect of observation errors. To address this critical issue, this paper proposes an Extended Kalman Filter-incorporated Residual Neural Network-based input Shaping (ERS) model for vibration control. Its main ideas are two-fold: a) adopting an extended Kalman filter to address a vertical flexible beam's model errors; and b) adopting a residual neural network to cascade with the extended Kalman filter for eliminating the remaining observation errors. Detailed experiments on a real dataset collected from a vertical flexible beam demonstrate that the proposed ERS model has achieved significant vibration control performance over several state-of-the-art models.
An Econometric Analysis of Large Flexible Cryptocurrency-mining Consumers in Electricity Markets
Authors: Subir Majumder, Ignacio Aravena, Le Xie
Date: 2024-08-21 00:00:00

In recent years, power grids have seen a surge in large cryptocurrency mining firms, with individual consumption levels reaching 700MW. This study examines the behavior of these firms in Texas, focusing on how their consumption is influenced by cryptocurrency conversion rates, electricity prices, local weather, and other factors. We transform the skewed electricity consumption data of these firms, perform correlation analysis, and apply a seasonal autoregressive moving average model for analysis. Our findings reveal that, surprisingly, short-term mining electricity consumption is not directly correlated with cryptocurrency conversion rates. Instead, the primary influencers are the temperature and electricity prices. These firms also respond to avoid transmission and distribution network (T&D) charges - commonly referred to as four Coincident peak (4CP) charges - during the summer months. As the scale of these firms is likely to surge in future years, the developed electricity consumption model can be used to generate public, synthetic datasets to understand the overall impact on the power grid. The developed model could also lead to better pricing mechanisms to effectively use the flexibility of these resources towards improving power grid reliability.
Decoupling Power Quality Issues in Grid-Microgrid Network Using Microgrid Building Blocks
Authors: Samrat Acharya, Priya Mana, Hisham Mahmood, Francis Tuffner, Alok Kumar Bharati
Date: 2024-08-21 00:00:00

Microgrids are evolving as promising options to enhance reliability of the connected transmission and distribution systems. Traditional design and deployment of microgrids require significant engineering analysis. Microgrid Building Blocks (MBB), consisting of modular blocks that integrate seamlessly to form effective microgrids, is an enabling concept for faster and broader adoption of microgrids. Back-to-Back converter placed at the point of common coupling of microgrid is an integral part of the MBB. This paper presents applications of MBB to decouple power quality issues in grid-microgrid network serving power quality sensitive loads such as data centers, new grid-edge technologies such as vehicle-to-grid generation, and serving electric vehicle charging loads during evacuation before disaster events. Simulation results show that MBB effectively decouples the power quality issues across networks and helps maintain good power quality in the power quality sensitive network based on the operational scenario.
CAMEO:A Co-design Architecture for Multi-objective Energy System Optimization
Authors: Rounak Meyur, Tonya Martin, Sumit Purohit
Date: 2024-08-21 00:00:00

Co-design plays a pivotal role in energy system planning as it allows for the holistic optimization of interconnected components, fostering efficiency, resilience, and sustainability by addressing complex interdependencies and trade-offs within the system. This leads to reduced operational costs and improved financial performance through optimized system design, resource allocation, and system-wide synergies. In addition, system planners must consider multiple probable scenarios to plan for potential variations in operating conditions, uncertainties, and future demands, ensuring robust and adaptable solutions that can effectively address the needs and challenges of various systems. This research introduces Co-design Architecture for Multi-objective Energy System Optimization (CAMEO), which facilitates design space exploration of the co-design problem via a modular and automated workflow system, enhancing flexibility and accelerating the design and validation cycles. The cloud-scale automation provides a user-friendly interface and enable energy system modelers to efficiently explore diverse design alternatives. CAMEO aims to revolutionize energy system optimization by developing next-generation design assistant with improved scalability, usability, and automation, thereby enabling the development of optimized energy systems with greater ease and speed.
Consensus over Clustered Networks Using Intermittent and Asynchronous Output Feedback
Authors: Federico M. Zegers, Sean Phillips
Date: 2024-08-21 00:00:00

In recent years, multi-agent teaming has garnered considerable interest since complex objectives, such as intelligence, surveillance, and reconnaissance, can be divided into multiple cluster-level sub-tasks and assigned to a cluster of agents with the appropriate functionality. Yet, coordination and information dissemination between clusters may be necessary to accomplish a desired objective. Distributed consensus protocols provide a mechanism for spreading information within clustered networks, allowing agents and clusters to make decisions without requiring direct access to the state of the ensemble. Hence, we propose a strategy for achieving system-wide consensus in the states of identical linear time-invariant systems coupled by an undirected graph whose directed sub-graphs are available only at sporadic times. Within this work, the agents of the network are organized into pairwise disjoint clusters, which induce sub-graphs of the undirected parent graph. Some cluster sub-graph pairs are linked by an inter-cluster sub-graph, where the union of all cluster and inter-cluster sub-graphs yields the undirected parent graph. Each agent utilizes a distributed consensus protocol with components that are updated intermittently and asynchronously with respect to other agents. The closed-loop ensemble dynamics is modeled as a hybrid system, and a Lyapunov-based stability analysis yields sufficient conditions for rendering the agreement subspace (consensus set) globally exponentially stable. Furthermore, an input-to-state stability argument demonstrates the consensus set is robust to a class of perturbations. A numerical simulation considering both nominal and perturbed scenarios is provided for validation purposes.
Data-driven H2-optimal Model Reduction via Offline Transfer Function Sampling
Authors: Umair Zulfiqar
Date: 2024-08-21 00:00:00

$\mathcal{H}_2$-optimal model order reduction algorithms represent a significant class of techniques, known for their accuracy, which has been extensively validated over the past two decades. Among these, the Iterative Rational Krylov Algorithm (IRKA) is widely regarded as a benchmark for constructing $\mathcal{H}_2$-optimal reduced-order models. However, a key challenge in its data-driven implementation lies in the need for transfer function samples and their derivatives, which must be updated iteratively. Conducting new experiments to acquire these samples each time IRKA updates the interpolation data is impractical. Additionally, for discrete-time systems, obtaining transfer function samples at frequencies outside the unit circle is challenging, as these are not easily accessible through measurements. This paper proposes a method to sample the transfer function and its derivative offline using frequency or time-domain data, which is commonly measured for various design and analysis purposes in industry. By leveraging this approach, there is no need to directly measure transfer function samples at interpolation points, as these can be generated offline using the pre-existing data. This facilitates the offline implementation of IRKA within the frequency- or time-domain Loewner framework. The approach is also extended to discrete-time systems in this work. A numerical example is provided to validate the theoretical findings presented.
Emerging clean technologies: policy-driven cost reductions, implications and perspectives
Authors: Mohamed Atouife, Jesse Jenkins
Date: 2024-08-20 00:00:00

Hydrogen production from water electrolysis, direct air capture (DAC), and synthetic kerosene derived from hydrogen and CO2 (`e-kerosene') are expected to play an important role in global decarbonization efforts. So far, the economics of these nascent technologies hamper their market diffusion. However, a wave of recent policy support in the United States, Europe, China, and elsewhere is anticipated to drive their commercial liftoff and bring their costs down. To this end, we evaluate the potential cost reductions driven by policy-induced scale-up of these emerging technologies through 2030 using an experience curves approach accounting for both local and global learning effects. We then analyze the consequences of projected cost declines on the competitiveness of these nascent technologies compared to conventional fossil alternatives, where applicable, and highlight some of the tradeoffs associated with their expansion. Our findings indicate that enacted policies could lead to substantial capital cost reductions for electrolyzers. Nevertheless, electrolytic hydrogen production at $1-2/kg would still require some form of policy support. Given expected costs and experience curves, it is unlikely that liquid solvent DAC (L-DAC) scale-up will bring removal costs to stated targets of $100/tCO2, though a $200/tCO2 may eventually be within reach. We also underscore the importance of tackling methane leakage for natural gas-powered L-DAC: unmitigated leaks amplify net removal costs, exacerbate the investment requirements to reach targeted costs, and cast doubt on L-DAC's role in the clean energy transition. Lastly, despite reductions in electrolysis and L-DAC costs, e-kerosene remains considerably more expensive than fossil jet fuel. The economics of e-kerosene and the resources required for production raise questions about the fuel's ultimate viability as a decarbonization tool for aviation.
Ultra-Fast and Efficient Design Method Using Deep Learning for Capacitive Coupling WPT System
Authors: Rasool Keshavarz, Ehsan Majidi, Ali Raza, Negin Shariati
Date: 2024-08-21 00:00:00

Capacitive coupling wireless power transfer (CCWPT) is one of the pervasive methods to transfer power in the reactive near-field zone. In this paper, a flexible design methodology based on Binary Particle Swarm Optimization (BPSO) algorithm is proposed for a pixelated microstrip structure. The pixel configuration of each parallel plate (43x43 pixels) determines the frequency response of the system (S-parameters) and by changing this configuration, we can achieve the dedicated operating frequency (resonance frequency) and its related |S21| value. Due to the large number of pixels, iterative optimization algorithm (BPSO) is the solution for designing a CCWPT system. However, the output of each iteration should be simulated in electromagnetic simulators (e.g., CST, HFSS, etc.), hence, the whole optimization process is time-consuming. This paper develops a rapid, agile and efficient method for designing two parallel pixelated microstrip plates of a CCWPT system based on deep neural networks. In the proposed method, CST-based BPSO algorithm is replaced with an AI-based method using ResNet-18. Advantages of the AI-based iterative method are automatic design process, more efficient, less time-consuming, less computational resource-consuming and less background EM knowledge requirements compared to the conventional techniques. Finally, the prototype of the proposed simulated structure is fabricated and measured. The simulation and measurement results validate the design procedure accuracy, using AI-based BPSO algorithm. The MAE (Mean Absolute Error) of prediction for the main resonance frequency and related |S21| are 110 MHz and 0.18 dB, respectively and according to the simulation results, the whole design process is 3629 times faster than the CST-based BPSO algorithm.
Real-Time Discrete Fractional Fourier Transform Using Metamaterial Coupled Lines Network
Authors: Rasool Keshavarz, Negin Shariati, Mohammad-Ali Miri
Date: 2024-08-21 00:00:00

Discrete Fractional Fourier Transforms (DFrFT) are universal mathematical tools in signal processing, communications and microwave sensing. Despite the excessive applications of DFrFT, implementation of corresponding fractional orders in the baseband signal often leads to bulky, power-hungry, and high-latency systems. In this paper, we present a passive metamaterial coupled lines network (MCLN) that performs the analog DFrFT in real-time at microwave frequencies. The proposed MCLN consists of M parallel microstrip transmission lines (TLs) in which adjacent TLs are loaded with interdigital capacitors to enhance the coupling level. We show that with proper design of the coupling coefficients between adjacent channels, the MCLN can perform an M-point DFrFT of an arbitrary fractional order that can be designed through the length of the network. In the context of real-time signal processing for realization of DFrFT, we design, model, simulate and implement a 16x16 MCLN and experimentally demonstrate the performance of the proposed structure. The proposed innovative approach is versatile and is capable to be used in various applications where DFrFT is an essential tool. The proposed design scheme based on MCLN is scalable across the frequency spectrum and can be applied to millimeter and submillimeter wave systems.
Fast State Stabilization using Deep Reinforcement Learning for Measurement-based Quantum Feedback Control
Authors: Chunxiang Song, Yanan Liu, Daoyi Dong, Hidehiro Yonezawa
Date: 2024-08-21 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning

The stabilization of quantum states is a fundamental problem for realizing various quantum technologies. Measurement-based-feedback strategies have demonstrated powerful performance, and the construction of quantum control signals using measurement information has attracted great interest. However, the interaction between quantum systems and the environment is inevitable, especially when measurements are introduced, which leads to decoherence. To mitigate decoherence, it is desirable to stabilize quantum systems faster, thereby reducing the time of interaction with the environment. In this paper, we utilize information obtained from measurement and apply deep reinforcement learning (DRL) algorithms, without explicitly constructing specific complex measurement-control mappings, to rapidly drive random initial quantum state to the target state. The proposed DRL algorithm has the ability to speed up the convergence to a target state, which shortens the interaction between quantum systems and their environments to protect coherence. Simulations are performed on two-qubit and three-qubit systems, and the results show that our algorithm can successfully stabilize random initial quantum system to the target entangled state, with a convergence time faster than traditional methods such as Lyapunov feedback control and several DRL algorithms with different reward functions. Moreover, it exhibits robustness against imperfect measurements and delays in system evolution.
How many autonomous vehicles are required to stabilize traffic flow?
Authors: MirSaleh Bahavarnia, Ahmad F. Taha
Date: 2024-08-20 00:00:00
Tasks: Autonomous Vehicles

The collective behavior of human-driven vehicles (HVs) produces the well-known stop-and-go waves potentially leading to higher fuel consumption and emissions. This paper investigates the stabilization of traffic flow via a minimum number of autonomous vehicles (AVs) subject to constraints on the control parameters aiming to reduce the number of vehicles on the road while achieving lower fuel consumption and emissions. The unconstrained scenario has been well-studied in recent studies. The main motivation to investigate the constrained scenario is that, in realistic engineering applications, lower and upper bounds exist on the control parameters. For the constrained scenario, we optimally find the minimum number of required AVs (via computing the optimal lower bound on the AV penetration rate) to stabilize traffic flow for a given number of HVs. As an immediate consequence, we conclude that for a given number of AVs, the number of HVs in the stabilized traffic flow may not be arbitrarily large in the constrained scenario unlike the unconstrained scenario studied in the literature. We systematically propose a procedure to compute the optimal lower bound on the AV penetration rate using nonlinear optimization techniques. Finally, we validate the theoretical results via numerical simulations. Numerical simulations suggest that enlarging the constraint intervals makes a smaller optimal lower bound on the AV penetration rate attainable. However, it leads to a slower transient response due to a dominant pole closer to the origin.
Safety-Critical Stabilization of Force-Controlled Nonholonomic Mobile Robots
Authors: Tianyu Han, Bo wang
Date: 2024-08-20 00:00:00
Tasks: Autonomous Vehicles, valid

We present a safety-critical controller for the problem of stabilization for force-controlled nonholonomic mobile robots. The proposed control law is based on the constructions of control Lyapunov functions (CLFs) and control barrier functions (CBFs) for cascaded systems. To address nonholonomicity, we design the nominal controller that guarantees global asymptotic stability and local exponential stability for the closed-loop system in polar coordinates and construct a strict Lyapunov function valid on any compact sets. Furthermore, we present a procedure for constructing CBFs for cascaded systems, utilizing the CBF of the kinematic model through integrator backstepping. Quadratic programming is employed to combine CLFs and CBFs to integrate both stability and safety in the closed loop. The proposed control law is time-invariant, continuous along trajectories, and easy to implement. Our main results guarantee both safety and local asymptotic stability for the closed-loop system.
How Much Reserve Fuel: Quantifying the Maximal Energy Cost of System Disturbances
Authors: Ram Padmanabhan, Craig Bakker, Siddharth Abhijit Dinkar, Melkior Ornik
Date: 2024-08-20 00:00:00

Motivated by the design question of additional fuel needed to complete a task in an uncertain environment, this paper introduces metrics to quantify the maximal additional energy used by a control system in the presence of bounded disturbances when compared to a nominal, disturbance-free system. In particular, we consider the task of finite-time stabilization for a linear time-invariant system. We first derive the nominal energy required to achieve this task in a disturbance-free system, and then the worst-case energy over all feasible disturbances. The latter leads to an optimal control problem with a least-squares solution, and then an infinite-dimensional optimization problem where we derive an upper bound on the solution. The comparison of these energies is accomplished using additive and multiplicative metrics, and we derive analytical bounds on these metrics. Simulation examples on an ADMIRE fighter jet model demonstrate the practicability of these metrics, and their variation with the task hardness, a combination of the distance of the initial condition from the origin and the task completion time.