pwc-archive/papers-with-abstracts huggingface dataset:
Insights from the application of nonlinear model predictive control to a cart-pendulum
Authors: Mark P. Balenzuela
Date: 2025-01-18 00:00:00
Tasks: Model Predictive Control

Inspired greatly by Mills et al. (2009) and the solution within, this paper aims to more clearly explain the mathematics and implementation details of such a powerful control algorithm. While the aforementioned paper is well written and of sound mathematics, it is extreamly dense and requires some time and patience to decipher, especially as it draws on many other sources to complete the algorithm. This dense property is a clear result of the paper being restricted to the brief form and important details being ommited as a result. We provide the much needed elaboration here for the benifit of the reader.
HOPS: High-order Polynomials with Self-supervised Dimension Reduction for Load Forecasting
Authors: Pengyang Song, Han Feng, Shreyashi Shukla, Jue Wang, Tao Hong
Date: 2025-01-18 00:00:00
Tasks: Computational Efficiency, Dimensionality Reduction, Load Forecasting

Load forecasting is a fundamental task in smart grid. Many techniques have been applied to developing load forecasting models. Due to the challenges such as the Curse of Dimensionality, overfitting, and limited computing resources, multivariate higher-order polynomial models have received limited attention in load forecasting, despite their desirable mathematical foundations and optimization properties. In this paper, we propose low rank approximation and self-supervised dimension reduction to address the aforementioned issues. To further improve computational efficiency, we also utilize a fast Conjugate Gradient based algorithm for the proposed polynomial models. Based on the load datasets from the ISO New England, the proposed method high-order polynomials with self-supervised dimension reduction (HOPS) demonstrates higher forecasting accuracy over several competitive models. Additionally, experimental results indicate that our approach alleviates redundant variable construction, achieving better forecasts with fewer input variables.
Assessing Markov Property in Driving Behaviors: Insights from Statistical Tests
Authors: Zheng Li, Haoming Meng, Chengyuan Ma, Ke Ma, Xiaopeng Li
Date: 2025-01-18 00:00:00
Tasks: Autonomous Vehicles

The Markov property serves as a foundational assumption in most existing work on vehicle driving behavior, positing that future states depend solely on the current state, not the series of preceding states. This study validates the Markov properties of vehicle trajectories for both Autonomous Vehicles (AVs) and Human-driven Vehicles (HVs). A statistical method used to test whether time series data exhibits Markov properties is applied to examine whether the trajectory data possesses Markov characteristics. t test and F test are additionally introduced to characterize the differences in Markov properties between AVs and HVs. Based on two public trajectory datasets, we investigate the presence and order of the Markov property of different types of vehicles through rigorous statistical tests. Our findings reveal that AV trajectories generally exhibit stronger Markov properties compared to HV trajectories, with a higher percentage conforming to the Markov property and lower Markov orders. In contrast, HV trajectories display greater variability and heterogeneity in decision-making processes, reflecting the complex perception and information processing involved in human driving. These results have significant implications for the development of driving behavior models, AV controllers, and traffic simulation systems. Our study also demonstrates the feasibility of using statistical methods to test the presence of Markov properties in driving trajectory data.
Automated Water Irrigation System
Authors: Matthew Okner, David Veksler
Date: 2025-01-17 00:00:00

This paper presents the design and implementation of an automated water irrigation system aimed at optimizing plant care through precision moisture monitoring and controlled water delivery. The system uses a capacitive soil moisture sensor, an ADC (analog-to-digital converter), and a relay-driven water pump to ensure plants receive adequate hydration based on real-time data. In addition, this work aims to build on existing applications for Raspberry Pi (4B) and Arduino-based automatic irrigation systems by integrating advanced calibration methods, employing optimized algorithms, and introducing new technologies to further enhance overall system efficiency and reliability.
Analytical Models of Frequency and Voltage in Large-Scale All-Inverter Power Systems
Authors: Marena Trujillo, Amir Sajadi, Bri-Mathias Hodge
Date: 2025-01-17 00:00:00
Tasks: All, Computational Efficiency

Low-order frequency response models for power systems have a decades-long history in optimization and control problems such as unit commitment, economic dispatch, and wide-area control. With a few exceptions, these models are built upon the Newtonian mechanics of synchronous generators, assuming that the frequency dynamics across a system are approximately homogeneous, and assume the dynamics of nodal voltages for most operating conditions are negligible, and thus are not directly computed at all buses. As a result, the use of system frequency models results in the systematic underestimation of frequency minimum nadir and maximum RoCoF, and provides no insight into the reactive power-voltage dynamics. This paper proposes a low-order model of both frequency and voltage response in grid-forming inverter-dominated power systems. The proposed model accounts for spatial-temporal variations in frequency and voltage behavior across a system and as a result, demonstrates the heterogeneity of frequency response in future renewable power systems. Electromagnetic transient (EMT) simulations are used to validate the utility, accuracy, and computational efficiency of these models, setting the basis for them to serve as fast, scalable alternatives to EMT simulation, especially when dealing with very large-scale systems, for both planning and operational studies.
ACCEPT: Diagnostic Forecasting of Battery Degradation Through Contrastive Learning
Authors: James Sadler, Rizwaan Mohammed, Michael Castle, Kotub Uddin
Date: 2025-01-17 00:00:00
Tasks: Contrastive Learning, Diagnostic

Modeling lithium-ion battery (LIB) degradation offers significant cost savings and enhances the safety and reliability of electric vehicles (EVs) and battery energy storage systems (BESS). Whilst data-driven methods have received great attention for forecasting degradation, they often demonstrate limited generalization ability and tend to underperform particularly in critical scenarios involving accelerated degradation, which are crucial to predict accurately. These methods also fail to elucidate the underlying causes of degradation. Alternatively, physical models provide a deeper understanding, but their complex parameters and inherent uncertainties limit their applicability in real-world settings. To this end, we propose a new model - ACCEPT. Our novel framework uses contrastive learning to map the relationship between the underlying physical degradation parameters and observable operational quantities, combining the benefits of both approaches. Furthermore, due to the similarity of degradation paths between LIBs with the same chemistry, this model transfers non-trivially to most downstream tasks, allowing for zero-shot inference. Additionally, since categorical features can be included in the model, it can generalize to other LIB chemistries. This work establishes a foundational battery degradation model, providing reliable forecasts across a range of battery types and operating conditions.
Optimizing a multi-state cold-standby system with multiple vacations in the repair and loss of units
Authors: Juan Eloy Ruiz-Castro
Date: 2025-01-14 00:00:00

A complex multi-state redundant system with preventive maintenance subject to multiple events is considered. The online unit can undergo several types of failures: internal and those provoked by external shocks. Multiple degradation levels are assumed so as internal and external. Degradation levels are observed by random inspections and if they are major, the unit goes to repair facility where preventive maintenance is carried out. This repair facility is composed of a single repairperson governed by a multiple vacation policy. This policy is set up according to the operational number of units. Two types of task can be performed by the repairperson, corrective repair and preventive maintenance. The times embedded in the system are phase type distributed and the model is built by using Markovian Arrival Processes with marked arrivals. Multiple performance measures besides of the transient and stationary distribution are worked out through matrix-analytic methods. This methodology enables us to express the main results and the global development in a matrix-algorithmic form. To optimize the model costs and rewards are included. A numerical example shows the versatility of the model.
Event-Based Impulsive Control for Spacecraft Rendezvous Hovering Phases
Authors: Julio C. Sanchez, Christophe Louembet, Francisco Gavilan, Rafael Vazquez
Date: 2025-01-13 00:00:00

This work presents an event-triggered controller for spacecraft rendezvous hovering phases. The goal is to maintain the chaser within a bounded region with respect to the target. The main assumption is that the chaser vehicle has impulsive thrusters. These are assumed to be orientable at any direction and are constrained by dead-zone and saturation bounds. The event-based controller relies on trigger rules deciding when a suitable control law is applied. The local control law consists on a single impulse; therefore the trigger rules design is based on the instantaneous reachability to the admissible set. The final outcome is a very efficient algorithm from both computational burden and footprint perspectives. Because the proposed methodology is based on a single impulse control, the controller invariance is local and assessed through impulsive systems theory. Finally, numerical results are shown and discussed.
Chance-constrained Model Predictive Control for Near Rectilinear Halo Orbit spacecraft rendezvous
Authors: Julio C. Sanchez, Francisco Gavilan, Rafael Vazquez
Date: 2025-01-13 00:00:00
Tasks: Model Predictive Control, valid

This work presents a robust Model Predictive Controller (MPC) to solve the problem of spacecraft rendezvous in the context of the restricted three-body problem (R3BP) as will be required to dock with space stations in cislunar space. The employed methodology is both valid for chemical and electric thrusters. By exploiting the state transition matrix and using a chance-constrained approach, the robust MPC assures constraints satisfaction under the presence of disturbances in a probabilistic sense. The perturbations parameters are computed on-line using a disturbance estimator. The robust controller is tested for a rendezvous scenario with a target placed in an Earth-Moon Near-Rectilinear Halo Orbit. Numerical results are shown and discussed.
A flatness-based predictive controller for six-degrees of freedom spacecraft rendezvous
Authors: Julio C. Sanchez, Francisco Gavilan, Rafael Vazquez, Christophe Louembet
Date: 2025-01-13 00:00:00

This work presents a closed-loop guidance algorithm for six-degrees of freedom spacecraft rendezvous with a passive target flying in an eccentric orbit. The main assumption is that the chaser vehicle has an attitude control system, based on reaction wheels, providing the necessary torque to change its orientation whereas the number of thrusters is arbitrary. The goal is to design fuel optimal maneuvers while satisfying operational constraints and rejecting disturbances. The proposed method is as follows; first, the coupled translational and angular dynamics are transformed to equivalent algebraic relations using the relative translational states transition matrix and the attitude flatness property. Then, a direct transcription method, based on B-splines parameterization and discretization of time continuous constraints, is developed to obtain a tractable static program. Finally, a Model Predictive Controller, based on linearization around the previously computed solution, is considered to handle disturbances. Numerical results are shown and discussed.
Accurate and thermodynamically consistent hydrogen equation of state for planetary modeling with flow matching
Authors: Hao Xie, Saburo Howard, Guglielmo Mazzola
Date: 2025-01-17 00:00:00

Accurate determination of the equation of state of dense hydrogen is essential for understanding gas giants. Currently, there is still no consensus on methods for calculating its entropy, which play a fundamental role and can result in qualitatively different predictions for Jupiter's interior. Here, we investigate various aspects of entropy calculation for dense hydrogen based on ab initio molecular dynamics simulations. Specifically, we employ the recently developed flow matching method to validate the accuracy of the traditional thermodynamic integration approach. We then clearly identify pitfalls in previous attempts and propose a reliable framework for constructing the hydrogen equation of state, which is accurate and thermodynamically consistent across a wide range of temperature and pressure conditions. This allows us to conclusively address the long-standing discrepancies in Jupiter's adiabat among earlier studies, demonstrating the potential of our approach for providing reliable equations of state of diverse materials.
Enhancing the Reliability in Machine Learning for Gravitational Wave Parameter Estimation with Attention-Based Models
Authors: Hibiki Iwanaga, Mahoro Matsuyama, Yousuke Itoh
Date: 2025-01-17 00:00:00
Tasks: parameter estimation

We introduce a technique to enhance the reliability of gravitational wave parameter estimation results produced by machine learning. We develop two independent machine learning models based on the Vision Transformer to estimate effective spin and chirp mass from spectrograms of gravitational wave signals from binary black hole mergers. To enhance the reliability of these models, we utilize attention maps to visualize the areas our models focus on when making predictions. This approach enables demonstrating that both models perform parameter estimation based on physically meaningful information. Furthermore, by leveraging these attention maps, we demonstrate a method to quantify the impact of glitches on parameter estimation. We show that as the models focus more on glitches, the parameter estimation results become more strongly biased. This suggests that attention maps could potentially be used to distinguish between cases where the results produced by the machine learning model are reliable and cases where they are not.
Discontinuous phase transition of feature detection in lateral predictive coding
Authors: Zhen-Ye Huang, Weikang Wang, Hai-Jun Zhou
Date: 2025-01-21 00:00:00

The brain adopts the strategy of lateral predictive coding (LPC) to construct optimal internal representations for salient features in input sensory signals to reduce the energetic cost of information transmission. Here we consider the task of distinguishing a non-Gaussian signal by LPC from $(N-1)$ Gaussian background signals of the same magnitude, which is intractable by principal component decomposition. We study the emergence of feature detection function from the perspective of statistical mechanics, and formulate a thermodynamic free energy to implement the tradeoff between energetic cost $E$ and information robustness. We define $E$ as the mean $L_1$-norm of the internal state vectors, and quantify the level of information robustness by an entropy measure $S$. We demonstrate that energy--information tradeoff may induce a discontinuous phase transition of the optimal matrix, from a very weak one with $S \approx 0$ to a functional LPC system with moderate synaptic weights in which a single unit responds selectively to the input non-Gaussian feature with high signal-to-noise ratio.
Using Space-Filling Curves and Fractals to Reveal Spatial and Temporal Patterns in Neuroimaging Data
Authors: Jacek Grela, Zbigniew Drogosz, Jakub Janarek, Jeremi K. Ochab, Ignacio Cifre, Ewa Gudowska-Nowak, Maciej A. Nowak, Paweł Oświęcimka, Dante R. Chialvo
Date: 2025-01-21 00:00:00
Tasks: Computational Efficiency, Dimensionality Reduction, Gaussian Processes

We present a novel method, Fractal Space-Curve Analysis (FSCA), which combines Space-Filling Curve (SFC) mapping for dimensionality reduction with fractal Detrended Fluctuation Analysis (DFA). The method is suitable for multidimensional geometrically embedded data, especially for neuroimaging data which is highly correlated temporally and spatially. We conduct extensive feasibility studies on diverse, artificially generated data with known fractal characteristics: the fractional Brownian motion, Cantor sets, and Gaussian processes. We compare the suitability of dimensionality reduction via Hilbert SFC and a data-driven alternative. FSCA is then successfully applied to real-world magnetic resonance imaging (MRI) and functional MRI (fMRI) scans. The method utilizing Hilbert curves is optimized for computational efficiency, proven robust against boundary effects typical in experimental data analysis, and resistant to data sub-sampling. It is able to correctly quantify and discern correlations in both stationary and dynamic two-dimensional images. In MRI Alzheimer's dataset, patients reveal a progression of the disease associated with a systematic decrease of the Hurst exponent. In fMRI recording of breath-holding task, the change in the exponent allows distinguishing different experimental phases. This study introduces a robust method for fractal characterization of spatial and temporal correlations in many types of multidimensional neuroimaging data. Very few assumptions allow it to be generalized to more dimensions than typical for neuroimaging and utilized in other scientific fields. The method can be particularly useful in analyzing fMRI experiments to compute markers of pathological conditions resulting from neurodegeneration. We also showcase its potential for providing insights into brain dynamics in task-related experiments.
Ultrasonic monitoring of carbonation in Portland cements
Authors: A. Villarreal, P. F. J. Cano-Barrita, F. M. Leon-Martinez, L. Medina, F. Castellanos
Date: 2025-01-19 00:00:00

Chemical reactions resulting from the ingress of carbonates into the cement matrix modify the properties of its pore solution, as well as its pore distribution and size. These changes lead to corrosion of the steel in reinforced concrete. The nature of conventional testing for the estimation of carbonation in cement-based materials is time-consuming and destructive. This paper presents a set of non-destructive ultrasound-based indexes, obtained solely from non-linear and linear analyses of ultrasonic signals, for measuring the carbonation of Portland cement pastes. Class 30RS cement pastes with three water/cement ratios by weight (0.4, 0.5, and 0.6) were considered. Carbonation was carried out for 120 days with a constant CO2 level of 4% by volume under controlled temperature and humidity, considering a unidirectional carbonation, parallel to the longitudinal axis of the samples. The level of carbonation was validated by FTIR measurements. From these analyses, different indexes with high correlation were obtained, estimated only from the ultrasonic signals and as a function of the days of exposure to carbonation, as well as of the percentage of carbonation. Further study is required for the evaluation of the reliability of these promising indexes for the determination of carbonation in cement-based materials.
Exploring the interplay between small and large scales movements in a neotropical small mammal
Authors: E. Brigatti, B. Ríos-Uzeda, M. V. Vieira
Date: 2025-01-23 00:00:00

We record and analyze the movement patterns of the marsupial {\it Didelphis aurita} at different temporal scales. Animals trajectories are collected at a daily scale by using spool-and-line techniques, and with the help of radio-tracking devices animals traveled distances are estimated at intervals of weeks. Small-scale movements are well described by truncated L\'evy flight, while large-scale movements produce a distribution of distances which is compatible with a Brownian motion. A model of the movement behavior of these animals, based on a truncated L\'evy flight calibrated on the small scale data, converges towards a Brownian behavior after a short time interval of the order of one week. These results show that whether L\'evy flight or Brownian motion behaviors apply, will depend on the scale of aggregation of the animals paths. In this specific case, as the effect of the rude truncation present in the daily data generates a fast convergence towards Brownian behaviors, L\'evy flights become of scarce interest for describing the local dispersion properties of these animals, which result well approximated by a normal diffusion process and not a fast, anomalous one. Interestingly, we are able to describe two movement phases as the consequence of a statistical effect generated by aggregation, without the necessity of introducing ecological constraints or mechanisms operating at different spatio-temporal scales. This result is of general interest, as it can be a key element for describing movement phenomenology at distinct spatio-temporal scales across different taxa and in a variety of systems.
Reaction kinetics of membrane receptors: a spatial modeling approach
Authors: Anil Cengiz, Sean D Lawley
Date: 2025-01-23 00:00:00

The interactions between diffusing molecules and membrane-bound receptors drive numerous cellular processes. In this work, we develop a spatial model of molecular interactions with membrane receptors by homogenizing the cell membrane and describing the evolution of both molecular diffusion and molecule-receptor interactions. By analyzing a resulting partial differential equation coupled to ordinary differential equations, we derive analytical expressions for the steady-state molecular influx rate in four prototypical interaction scenarios: Michaelis-Menten kinetics, Substrate Competition, Competitive Inhibition, and Uncompetitive Inhibition. For each scenario, we show how to modify the classical well-mixed reaction rate theory to resolve spatial features inherent to receptors bound to cell membranes. We find that naive well-mixed calculations significantly overestimate reaction rates in certain biophysical parameter regimes.
Stochastic resonance in Schmitt trigger and its application towards weak signal detection
Authors: Yoonkang Kim, Donghyeok Seo
Date: 2025-01-05 00:00:00

This study explores stochastic resonance (SR) in a Schmitt trigger circuit and its application to weak signal detection. SR, a phenomenon where noise synchronizes with weak signals to enhance detectability, was demonstrated using a custom-designed bi-stable Schmitt trigger system. The circuit's bi-stability was validated through hysteresis curve analysis, confirming its suitability for SR studies. Experimental results revealed SR behavior by analyzing signal-to-noise ratio (SNR) responses to noise amplitude variations. Detection experiments were conducted to determine frequency and amplitude of damping sinusoidal pulses. Frequency detection proved effective, albeit with limitations at low frequencies, while amplitude detection faced challenges due to mathematical complexities. Nonetheless, the study highlights SR's potential for weak signal detection, with proposed enhancements to improve detection accuracy. This work underscores the adaptability of classical SR principles to practical detection systems and suggests future applications in advanced detection technologies, including quantum systems.