pwc-archive/papers-with-abstracts huggingface dataset:
Safe Imitation Learning-based Optimal Energy Storage Systems Dispatch in Distribution Networks
Authors: Shengren Hou, Peter Palensky, Pedro P. Vergara
Date: 2024-11-01 00:00:00
Tasks: Deep Reinforcement Learning, Imitation Learning, reinforcement-learning, Reinforcement Learning

The integration of distributed energy resources (DER) has escalated the challenge of voltage magnitude regulation in distribution networks. Traditional model-based approaches, which rely on complex sequential mathematical formulations, struggle to meet real-time operational demands. Deep reinforcement learning (DRL) offers a promising alternative by enabling offline training with distribution network simulators, followed by real-time execution. However, DRL algorithms tend to converge to local optima due to limited exploration efficiency. Additionally, DRL algorithms can not enforce voltage magnitude constraints, leading to potential operational violations when implemented in the distribution network operation. This study addresses these challenges by proposing a novel safe imitation reinforcement learning (IRL) framework that combines IRL and a designed safety layer, aiming to optimize the operation of Energy Storage Systems (ESSs) in active distribution networks. The proposed safe IRL framework comprises two phases: offline training and online execution. During the offline phase, optimal state-action pairs are collected using an NLP solver, guiding the IRL policy iteration. In the online phase, the trained IRL policy's decisions are adjusted by the safety layer to maintain safety and constraint compliance. Simulation results demonstrate the efficacy of Safe IRL in balancing operational efficiency and safety, eliminating voltage violations, and maintaining low operation cost errors across various network sizes, while meeting real-time execution requirements.
Set-Theoretic Direct Data-driven Predictive Control
Authors: Mohammad Bajelani, Walter Lucia, Klaske van Heusden
Date: 2024-11-01 00:00:00
Tasks: State Estimation

Designing the terminal ingredients of direct data-driven predictive control presents challenges due to its reliance on an implicit, non-minimal input-output data-driven representation. By considering the class of constrained LTI systems with unknown time delays, we propose a set-theoretic direct data-driven predictive controller that does not require a terminal cost to provide closed-loop guarantees. In particular, first, starting from input/output data series, we propose a sample-based method to build N-step input output backward reachable sets. Then, we leverage the constructed family of backward reachable sets to derive a data-driven control law. The proposed method guarantees finite-time convergence and recursive feasibility, independent of objective function tuning. It requires neither explicit state estimation nor an explicit prediction model, relying solely on input-output measurements; therefore, unmodeled dynamics can be avoided. Finally, a numerical example highlights the effectiveness of the proposed method in stabilizing the system, whereas direct data-driven predictive control without terminal ingredients fails under the same conditions.
Model Predictive Contouring Control with Barrier and Lyapunov Functions for Stable Path-Following in UAV systems
Authors: Bryan S. Guevara, Viviana Moya, Luis F. Recalde, David Pozo-Espin, Daniel C. Gandolfo, Juan M. Toibero
Date: 2024-11-01 00:00:00
Tasks: Navigate

In this study, we propose a novel method that integrates Nonlinear Model Predictive Contour Control (NMPCC) with an Exponentially Stabilizing Control Lyapunov Function (ES-CLF) and Exponential Higher-Order Control Barrier Functions to achieve stable path-following and obstacle avoidance in UAV systems. This framework enables unmanned aerial vehicles (UAVs) to safely navigate around both static and dynamic obstacles while strictly adhering to desired paths. The quaternion-based formulation ensures precise orientation and attitude control, while a robust optimization solver enforces the constraints imposed by the Control Lyapunov Function (CLF) and Control Barrier Functions (CBF), ensuring reliable real-time performance. The method was validated in a Model-in-the-Loop (MiL) environment, demonstrating effective path tracking and obstacle avoidance. The results highlight the framework's ability to minimize both orthogonal and tangential errors, ensuring stability and safety in complex environments.
Identification of Analytic Nonlinear Dynamical Systems with Non-asymptotic Guarantees
Authors: Negin Musavi, Ziyao Guo, Geir Dullerud, YingYing Li
Date: 2024-11-01 00:00:00

This paper focuses on the system identification of an important class of nonlinear systems: linearly parameterized nonlinear systems, which enjoys wide applications in robotics and other mechanical systems. We consider two system identification methods: least-squares estimation (LSE), which is a point estimation method; and set-membership estimation (SME), which estimates an uncertainty set that contains the true parameters. We provide non-asymptotic convergence rates for LSE and SME under i.i.d. control inputs and control policies with i.i.d. random perturbations, both of which are considered as non-active-exploration inputs. Compared with the counter-example based on piecewise-affine systems in the literature, the success of non-active exploration in our setting relies on a key assumption on the system dynamics: we require the system functions to be real-analytic. Our results, together with the piecewise-affine counter-example, reveal the importance of differentiability in nonlinear system identification through non-active exploration. Lastly, we numerically compare our theoretical bounds with the empirical performance of LSE and SME on a pendulum example and a quadrotor example.
Constraint-Driven Multi-USV Coverage Path Generation for Aquatic Environmental Monitoring
Authors: Yo Toyomoto, Toshiyuki Oshima, Kosei Oishi, José M. Maestre, Takeshi Hatanaka
Date: 2024-11-01 00:00:00

In this article, we address aquatic environmental monitoring using a fleet of unmanned surface vehicles (USVs). Specifically, we develop an online path generator that provides either circular or elliptic paths based on the real-time feedback so that the USVs efficiently sample the sensor data over given aquatic environment. To this end, we begin by formulating a novel online path generation problem for a group of Dubins vehicles in the form of cost minimization based on the formulation of persistent coverage control. We then transform the cost minimization into a constraint-based specification so that a prescribed performance level is certified. An online coverage path generator is then designed based on the so-called constraint-based control in order to meet the performance certificate together with additional constraints inherent in the parameters that specify the paths. It is also shown there that the present constraint-based approach allows one to drastically reduce the computational complexity stemming from combinations of binary variables corresponding to the turning directions of the USVs. The present coverage path generator is finally demonstrated through simulations and experiments on an original testbed of multiple USVs.
Weighted Null Space Fitting (WNSF): A Link between The Prediction Error Method and Subspace Identification
Authors: Jiabao He, Håkan Hjalmarsson
Date: 2024-11-01 00:00:00
Tasks: State Space Models

Subspace identification method (SIM) has been proven to be very useful and numerically robust for estimating state-space models. However, it is in general not believed to be as accurate as the prediction error method (PEM). Conversely, PEM, although more accurate, comes with non-convex optimization problems and requires local non-linear optimization algorithms and good initialization points. This contribution proposes a weighted null space fitting (WNSF) method to identify a state-space model, combining some advantages of the two mainstream approaches aforementioned. It starts with the estimate of a non-parametric model using least-squares, and then the reduction to a state-space model in the observer canonical form is a multi-step least-squares procedure where each step consists of the solution of a quadratic optimization problem. Unlike SIM, which focuses on the range space of the extended observability matrix, WNSF estimates its null space, avoiding the need for singular value decomposition. Moreover, the statistically optimal weighting for the null space fitting problem is derived. It is conjectured that WNSF is asymptotically efficient, which is supported by a simulation study.
A KAN-based Interpretable Framework for Process-Informed Prediction of Global Warming Potential
Authors: Jaewook Lee, Xinyang Sun, Ethan Errington, Miao Guo
Date: 2024-11-01 00:00:00
Tasks: Prediction

Accurate prediction of Global Warming Potential (GWP) is essential for assessing the environmental impact of chemical processes and materials. Traditional GWP prediction models rely predominantly on molecular structure, overlooking critical process-related information. In this study, we present an integrative GWP prediction model that combines molecular descriptors (MACCS keys and Mordred descriptors) with process information (process title, description, and location) to improve predictive accuracy and interpretability. Using a deep neural network (DNN) model, we achieved an R-squared of 86% on test data with Mordred descriptors, process location, and description information, representing a 25% improvement over the previous benchmark of 61%; XAI analysis further highlighted the significant role of process title embeddings in enhancing model predictions. To enhance interpretability, we employed a Kolmogorov-Arnold Network (KAN) to derive a symbolic formula for GWP prediction, capturing key molecular and process features and providing a transparent, interpretable alternative to black-box models, enabling users to gain insights into the molecular and process factors influencing GWP. Error analysis showed that the model performs reliably in densely populated data ranges, with increased uncertainty for higher GWP values. This analysis allows users to manage prediction uncertainty effectively, supporting data-driven decision-making in chemical and process design. Our results suggest that integrating both molecular and process-level information in GWP prediction models yields substantial gains in accuracy and interpretability, offering a valuable tool for sustainability assessments. Future work may extend this approach to additional environmental impact categories and refine the model to further enhance its predictive reliability.
Cyclic Reformulation Based System Identification for Periodically Time-varying Systems
Authors: Hiroshi Okajima, Yusuke Fujimoto, Hiroshi Oku, Haruto Kondo
Date: 2024-11-01 00:00:00

This paper addresses a system identification for linear periodically time-varying plants in the discrete-time setting. A system identification algorithm for linear, periodically time-varying plants is introduced based on a cyclic reformulation and a state coordinate transformation of the cycled system. By using our system identification algorithm, the high-accuracy model of the periodically time-varying plant can be obtained without using specific periodic input signals. The effectiveness of the proposed algorithm is demonstrated with numerical examples.
A New Switched Reluctance Motor with Embedded Permanent Magnets for Transportation Electrification
Authors: Gholamreza Davarpanah, Sajjad Mohammadi
Date: 2024-10-31 00:00:00

A new three-phase hybrid-excited multi-tooth switched reluctance motor with embedded permanent magnets is proposed, capable of achieving higher torque density for transportation electrification applications. Operating principles and design considerations are discussed. A magnetic equivalent circuit is developed. Finite element method is employed in the field analysis. The advantages of the proposed topology over existing designs for switched reluctance motors and flux switching motors are presented. Finally, the optimized design is prototyped to experimentally confirm the design and simulation results.
Learning Optimal Interaction Weights in Multi-Agents Systems
Authors: Sara Honarvar, Yancy Diaz-Mercado
Date: 2024-10-31 00:00:00

This paper presents a spatio-temporal inverse optimal control framework for understanding interactions in multi-agent systems (MAS). We employ a graph representation approach and model the dynamics of interactions between agents as state-dependent edge weights in a consensus algorithm, incorporating both spatial and temporal dynamics. Our method learns these edge weights from trajectory observations, such as provided by expert demonstrations, which allows us to capture the complexity of nonlinear, distributed interaction behaviors. We derive necessary and sufficient conditions for the optimality of these interaction weights, explaining how the network topology affects MAS coordination. The proposed method is demonstrated on a multi-agent formation control problem, where we show its effectiveness in recovering the interaction weights and coordination patterns from sample trajectory data.
MESS+: Energy-Optimal Inferencing in Language Model Zoos with Service Level Guarantees
Authors: Ryan Zhang, Herbert Woisetschläger, Shiqiang Wang, Hans Arno Jacobsen
Date: 2024-10-31 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, Model Selection, Stochastic Optimization

Open-weight large language model (LLM) zoos allow users to quickly integrate state-of-the-art models into systems. Despite increasing availability, selecting the most appropriate model for a given task still largely relies on public benchmark leaderboards and educated guesses. This can be unsatisfactory for both inference service providers and end users, where the providers usually prioritize cost efficiency, while the end users usually prioritize model output quality for their inference requests. In commercial settings, these two priorities are often brought together in Service Level Agreements (SLA). We present MESS+, an online stochastic optimization algorithm for energy-optimal model selection from a model zoo, which works on a per-inference-request basis. For a given SLA that requires high accuracy, we are up to 2.5x more energy efficient with MESS+ than with randomly selecting an LLM from the zoo while maintaining SLA quality constraints.
Neural Network Prediction of Strong Lensing Systems with Domain Adaptation and Uncertainty Quantification
Authors: Shrihan Agarwal, Aleksandra Ćiprijanović, Brian D. Nord
Date: 2024-10-23 00:00:00
Tasks: Domain Adaptation, Uncertainty Quantification, Unsupervised Domain Adaptation

Modeling strong gravitational lenses is computationally expensive for the complex data from modern and next-generation cosmic surveys. Deep learning has emerged as a promising approach for finding lenses and predicting lensing parameters, such as the Einstein radius. Mean-variance Estimators (MVEs) are a common approach for obtaining aleatoric (data) uncertainties from a neural network prediction. However, neural networks have not been demonstrated to perform well on out-of-domain target data successfully - e.g., when trained on simulated data and applied to real, observational data. In this work, we perform the first study of the efficacy of MVEs in combination with unsupervised domain adaptation (UDA) on strong lensing data. The source domain data is noiseless, and the target domain data has noise mimicking modern cosmology surveys. We find that adding UDA to MVE increases the accuracy on the target data by a factor of about two over an MVE model without UDA. Including UDA also permits much more well-calibrated aleatoric uncertainty predictions. Advancements in this approach may enable future applications of MVE models to real observational data.
A proposed signal discovery method in interstellar communication
Authors: William J. Crilly Jr
Date: 2024-11-04 00:00:00

Experiments conducted since 2018, using three geographically spaced synchronized radio telescopes, and a radio interferometer, indicate the presence of anomalous narrow bandwidth pulse pairs, conjectured to be sourced from a celestial direction near the star Rigel. Many explanatory hypotheses are possible. In the current work, a measurement method is proposed and implemented to attempt to provide high levels of statistical power of narrowband pulse pair observations, while minimizing interferometer instrument adjustments that might bias the experiment. Using the proposed method, twenty pulse pairs having statistical power at 5 to 10 standard deviations, and one pulse pair at 22 standard deviations of mean shift of pulse pair count, were observed in the prior celestial direction range, while using other celestial directions as an experimental comparison group. High levels of standard deviations of pulse pair count mean shift, observed in this 92 day experiment, imply the falsification of a Gaussian noise hypothesis. Alternate and auxiliary hypotheses, and further experiments, are sought to try to explain the narrow bandwidth pulsed signals.
Segmentation algorithms and modeling of recurrent bursting events in neuronal and glial time series
Authors: Lou Zonca, Elena Dossi, Nathalie Rouach, D. Holcman
Date: 2024-11-01 00:00:00
Tasks: Time Series

Long-time series of neuronal recordings are resulting from the activity of connected neuronal networks. Yet how neuronal properties can be extracted remains empirical. We review here the data analysis based on network models to recover physiological parameters from electrophysiological and calcium recordings in neurons and astrocytes. After, we present the recording techniques and activation events, such as burst and interburst and Up and Down states. We then describe time-serie segmentation methods developed to detect and to segment these events. To interpret the statistics extracted from time series, we present computational models of neuronal populations based on synaptic short-term plasticity and After hyperpolarization. We discuss how these models are calibrated so that they can reproduce the statistics observed in the experimental time series. They serve to extract specific parameters by comparing numerical and experimental statistical moment or entire distributions. Finally, we discus cases where calibrated models are used to predict the selective impact of some parameters on the circuit behavior, properties that would otherwise be difficult to dissect experimentally.
In-situ Self-optimization of Quantum Dot Emission for Lasers by Machine-Learning Assisted Epitaxy
Authors: Chao Shen, Wenkang Zhan, Shujie Pan, Hongyue Hao, Ning Zhuo, Kaiyao Xin, Hui Cong, Chi Xu, Bo Xu, Tien Khee Ng, Siming Chen, Chunlai Xue, Fengqi Liu, Zhanguo Wang, Chao Zhao
Date: 2024-11-01 00:00:00
Tasks: Surface Reconstruction

Traditional methods for optimizing light source emissions rely on a time-consuming trial-and-error approach. While in-situ optimization of light source gain media emission during growth is ideal, it has yet to be realized. In this work, we integrate in-situ reflection high-energy electron diffraction (RHEED) with machine learning (ML) to correlate the surface reconstruction with the photoluminescence (PL) of InAs/GaAs quantum dots (QDs), which serve as the active region of lasers. A lightweight ResNet-GLAM model is employed for the real-time processing of RHEED data as input, enabling effective identification of optical performance. This approach guides the dynamic optimization of growth parameters, allowing real-time feedback control to adjust the QDs emission for lasers. We successfully optimized InAs QDs on GaAs substrates, with a 3.2-fold increase in PL intensity and a reduction in full width at half maximum (FWHM) from 36.69 meV to 28.17 meV under initially suboptimal growth conditions. Our automated, in-situ self-optimized lasers with 5-layer InAs QDs achieved electrically pumped continuous-wave operation at 1240 nm with a low threshold current of 150 A/cm2 at room temperature, an excellent performance comparable to samples grown through traditional manual multi-parameter optimization methods. These results mark a significant step toward intelligent, low-cost, and reproductive light emitters production.
Super-Resolution without High-Resolution Labels for Black Hole Simulations
Authors: Thomas Helfer, Thomas D. P. Edwards, Jessica Dafflon, Kaze W. K. Wong, Matthew Lyle Olson
Date: 2024-11-03 00:00:00
Tasks: Super-Resolution

Generating high-resolution simulations is key for advancing our understanding of one of the universe's most violent events: Black Hole mergers. However, generating Black Hole simulations is limited by prohibitive computational costs and scalability issues, reducing the simulation's fidelity and resolution achievable within reasonable time frames and resources. In this work, we introduce a novel method that circumvents these limitations by applying a super-resolution technique without directly needing high-resolution labels, leveraging the Hamiltonian and momentum constraints-fundamental equations in general relativity that govern the dynamics of spacetime. We demonstrate that our method achieves a reduction in constraint violation by one to two orders of magnitude and generalizes effectively to out-of-distribution simulations.
A Universal Quantum Computer From Relativistic Motion
Authors: Philip A. LeMaitre, T. Rick Perche, Marius Krumm, Hans J. Briegel
Date: 2024-10-31 00:00:00
Tasks: valid

We present an explicit construction of a relativistic quantum computing architecture using a variational quantum circuit approach that is shown to allow for universal quantum computing. The variational quantum circuit consists of tunable single-qubit rotations and entangling gates that are implemented successively. The single qubit rotations are parameterized by the proper time intervals of the qubits' trajectories and can be tuned by varying their relativistic motion in spacetime. The entangling layer is mediated by a relativistic quantum field instead of through direct coupling between the qubits. Within this setting, we give a prescription for how to use quantum field-mediated entanglement and manipulation of the relativistic motion of qubits to obtain a universal gate set, for which compact non-perturbative expressions that are valid for general spacetimes are also obtained. We also derive a lower bound on the channel fidelity that shows the existence of parameter regimes in which all entangling operations are effectively unitary, despite the noise generated from the presence of a mediating quantum field. Finally, we consider an explicit implementation of the quantum Fourier transform with relativistic qubits.
An Integer-N Frequency Synthesizer for Flexible On-Chip Clock Generation
Authors: Soumyajit Mandal, Piotr Maj, Grzegorz W. Deptuch
Date: 2024-11-03 00:00:00

A low-power integer-N frequency synthesizer for flexible on-chip clock generation has been designed in 65 nm CMOS technology. The circuit can be programmed to generate two independent low-jitter clocks between 30 MHz and 3 GHz that are locked a 10-50 MHz reference input. The design uses a phase-locked loop (PLL) with a dual-tuned LC voltage-controlled oscillator (VCO), programmable feedback divider, and dual output dividers. The total power consumption from 1.2 V and 0.8 V supplies is 4.0 mW. Experimental results confirm the functionality of the proposed synthesizer over a wide range of output frequencies.