pwc-archive/papers-with-abstracts huggingface dataset:
Capacity Expansion Planning under Uncertainty subject to Expected Energy Not Served Constraints
Authors: Marilena Zampara, Daniel Ávila, Anthony Papavasiliou
Date: 2025-01-29 00:00:00

We present a method for solving a large-scale stochastic capacity expansion problem which explicitly considers reliability constraints, in particular constraints on expected energy not served. Our method tackles this problem by a Lagrange relaxation of the expected energy not served constraints. We solve the relaxed formulation in an iterative manner, using a subgradient-based method. Each iteration requires the solution of a stochastic capacity expansion problem, for which we implement a subgradient decomposition scheme in a high-performance computing infrastructure. We apply the proposed methodology on the Economic Viability Assessment model that is used by ENTSO-E in the annual European Resource Adequacy Assessment, extended to include explicit reliability constraints. The approach is able to solve this model achieving a 1.3% optimality gap. We compare our approach against accounting for reliability through penalizing load shedding at VOLL, and find that the former results in 1.6% savings in total cost. We are also able to quantify the cost savings from allowing some load curtailment in the capacity planning process, which ranges from 1.6 to 6% in the cases analyzed.
Floodgates up to contain the DeePC and limit extrapolation
Authors: Mohammad Ramadan, Evan Toler, Mihai Anitescu
Date: 2025-01-28 00:00:00

Behavioral data-enabled control approaches typically assume data-generating systems of linear dynamics. This may result in false generalization if the newly designed closed-loop system results in input-output distributional shifts beyond learning data. These shifts may compromise safety by activating harmful nonlinearities in the data-generating system not experienced previously in the data and/or not captured by the linearity assumption inherent in these approaches. This paper proposes an approach to slow down the distributional shifts and therefore enhance the safety of the data-enabled methods. This is achieved by introducing quadratic regularization terms to the data-enabled predictive control formulations. Slowing down the distributional shifts comes at the expense of slowing down the exploration, in a trade-off resembling the exploration vs exploitation balance in machine learning.
Optimal control over Markovian wireless communication channels under generalized packet dropout compensation
Authors: Yuriy Zacchia Lun, Francesco Smarra, Alessandro D'Innocenzo
Date: 2025-01-28 00:00:00
Tasks: State Estimation

Control loops closed over wireless links greatly benefit from accurate estimates of the communication channel condition. To this end, the finite-state Markov channel model allows for reliable channel state estimation. This paper develops a Markov jump linear system representation for wireless networked control with persistent channel state observation, stochastic message losses, and generalized packet dropout compensation. With this model, we solve the finite- and infinite-horizon linear quadratic regulation problems and introduce an easy-to-test stability condition for any given infinite-horizon control law. We also thoroughly analyze the impact of a scalar general dropout compensation factor on the stability and closed-loop performance of a rotary inverted pendulum controlled remotely through a wireless link. Finally, we validate the results numerically via extensive Monte Carlo simulations, showing the benefits of the proposed control strategy.
In-Circuit Characterization of Low-Frequency Stability Margins in Power Amplifiers
Authors: Jose Manuel Gonzalez, Nerea Otegi, Aitziber Anakabe, Libe Mori, Asier Barcenilla, Juan-Mari Collantes
Date: 2025-01-28 00:00:00

Low-frequency resonances with low stability margins affect video bandwidth characteristics of power amplifiers. In this work, a non-connectorized measurement technique is presented to obtain the low-frequency critical poles at internal nodes of a hybrid amplifier. The experimental setup uses a high impedance probe connected to a vector network analyzer (VNA) to obtain a fully calibrated closed-loop frequency response that is identified to get the poles of the device at low frequency. Compared to previous connectorized solutions, the approach avoids the ad-hoc insertion of extra RF connectors to access the low-frequency dynamics of the amplifier. In addition, it simplifies the characterization at multiple internal nodes, which is worthwhile for an efficient detection and fixing of critical low frequency dynamics in multistage power amplifiers. The technique is first applied to dc steady state regimes and compared to the connectorized approach on a single stage amplifier. Next, it is applied to a three-stage amplifier to show its potential to detect the origin of the undesired dynamics and the most effective way to increase stability margin. Finally, the technique has been extended to the large-signal case to increase its usefulness for the design and diagnosis of high power amplifiers.
Data-Efficient Extremum-Seeking Control Using Kernel-Based Function Approximation
Authors: Wouter Weekers, Alessandro Saccon, Nathan van de Wouw
Date: 2025-01-28 00:00:00

Existing extremum-seeking control (ESC) approaches typically rely on applying repeated perturbations to input parameters and performing measurements of the corresponding performance output. The required separation between the different timescales in the ESC loop means that performing these measurements can be time-consuming. Moreover, performing these measurements can be costly in practice, e.g., due to the use of resources. With these challenges in mind, it is desirable to reduce the number of measurements needed to optimize performance. Therefore, in this work, we present a sampled-data ESC approach aimed at reducing the number of measurements that need to be performed. In the proposed approach, we use input-output data obtained during regular operation of the extremum-seeking controller to construct online an approximation of the system's underlying cost function. By using this approximation to perform parameter updates when a decrease in the cost can be guaranteed, instead of performing additional measurements to perform this update, we make more efficient use of data collected during regular operation of the extremum-seeking controller. As a result, we indeed obtain a reduction in the required number of measurements to achieve optimization. We provide a stability analysis of the novel sampled-data ESC approach, and demonstrate the benefits of the synergy between kernel-based function approximation and standard ESC in simulation on a multi-input dynamical system.
Understanding the Effect of Long-Term Memory Model Parameters in Pole-Zero Identification for Stability Analysis of Power Amplifiers
Authors: Libe Mori, Aitziber Anakabe, Juan-Mari Collantes, Vincent Armengaud
Date: 2025-01-28 00:00:00

Understanding the nature of potential instabilities is indispensable for the stabilization of power amplifiers. Pole-zero identification is one of the techniques that can be used to determine the stability of a design in large-signal operation. In this work, the possible presence of poles at the fundamental frequency linked to the long-term memory parameters of the transistor's model (self-heating and traps) is presented and discussed. The paper shows how their effect on the identified frequency responses around the fundamental frequency may compromise the stability analysis results and the assessment of stability margins. The low observability of the poles at the fundamental frequency highlights the importance of an accurate identification of real poles in low-frequency bands. A specific algorithm for the automatic frequency domain identification of non-resonant frequency responses and a procedure for detecting and reducing overfitting of real poles is proposed in this article. The benefits of the proposed methodology to correctly detect and analyze real poles at low frequencies is demonstrated through Monte-Carlo sensitivity analyses of two different amplifier designs.
Toward Explainable NILM: Real-Time Event-Based NILM Framework for High-Frequency Data
Authors: Grigorii Gerasimov, Ilia Kamyshev, Sahar Moghimian Hoosh, Elena Gryazina, Henni Ouerdane
Date: 2025-01-28 00:00:00
Tasks: Non-Intrusive Load Monitoring

Non-Intrusive Load Monitoring (NILM) is an advanced, and cost-effective technique for monitoring appliance-level energy consumption. However, its adaptability is hindered by the lack of transparency and explainability. To address this challenge, this paper presents an explainable, real-time, event-based NILM framework specifically designed for high-frequency datasets. The proposed framework ensures transparency at every stage by integrating a z-score-based event detector, appliance signature estimation, Fourier-based feature extraction, an XG-Boost classifier, and post hoc SHAP analysis. The SHAP analysis further quantifies the contribution of individual features, such as cosine of specific harmonic phases, to appliance classification. The framework is trained and evaluated on the PLAID dataset, and achieved a classification accuracy of 90% while maintaining low computational requirements and a latency of less than one second.
Compression and Distillation of Data Quadruplets in Non-intrusive Reduced-order Modeling
Authors: Umair Zulfiqar
Date: 2025-01-28 00:00:00

This paper introduces a quadrature-free, data-driven approach to balanced truncation for both continuous-time and discrete-time systems. The method non-intrusively constructs reduced-order models using available transfer function samples from the right half of the $s$-plane. It is highlighted that the proposed data-driven balanced truncation and existing quadrature-based balanced truncation algorithms share a common feature: both compress their respective data quadruplets to derive reduced-order models. Additionally, it is demonstrated that by using different compression strategies, these quadruplets can be utilized to develop three data-driven formulations of the IRKA for both continuous-time and discrete-time systems. These formulations non-intrusively generate reduced models using transfer function samples from the $j\omega$-axis or the right half of the $s$-plane, or impulse response samples. Notably, these IRKA formulations eliminate the necessity of computing new transfer function samples as IRKA iteratively updates the interpolation points. The efficacy of the proposed algorithms is validated through numerical examples, which show that the proposed data-driven approaches perform comparably to their intrusive counterparts.
Network Risk Estimation: A Risk Estimation Paradigm for Cyber Networks
Authors: Arda Bayer, David Maluf, Behnaam Aazhang
Date: 2025-01-27 00:00:00

Cyber networks are fundamental to many organization's infrastructure, and the size of cyber networks is increasing rapidly. Risk measurement of the entities/endpoints that make up the network via available knowledge about possible threats has been the primary tool in cyber network security. However, the dynamic behavior of the entities and the sparsity of risk-measurable points are limiting factors for risk measurement strategies, which results in poor network visibility considering the volatility of cyber networks. This work proposes a new probabilistic risk estimation approach to network security, NRE, which operates on top of existing risk measurements. The proposed method NRE extracts relationships among system components from the network connection data, models risk propagation based on the learned relationships and refines the estimates whenever risk measurements are provided. In this work, (i) the risk estimation scheme is proposed, (ii) an application of quantitative risk estimates is devised, (iii) descriptiveness of the risk estimates are compared to a pure risk measurement alternative and (iv) low computational complexity of the proposed method is illustrated capable of real-time deployment. The proposed method, NRE, is ultimately a quantitative data-driven risk assessment tool that can be used to add security aspects to existing network functions, such as routing, and it provides a robust description of the network state in the presence of threats, capable of running in real-time.
Sensitivity Analysis of the Laser Power Control System to Measurement Noise in SLS 3D Printers
Authors: Hamid Toshani, Janith Petangoda, Chatura Samarakoon, Phillip Stanley-Marbell
Date: 2025-01-27 00:00:00
Tasks: Sensitivity

Uniform temperature distribution in Selective Laser Sintering (SLS) is essential for producing durable 3D prints. Achieving uniformity requires a laser power control system that minimises deviation of the printing temperatures from the target temperature. Because the estimate of the actual process temperature is an input to the laser power control, uncertainty in the estimate of the actual temperature can lead to fluctuations in laser power that affect the thermal performance of the SLS. This article investigates the sensitivity of a laser power control system to temperature measurement uncertainty. This article evaluates the effectiveness of two methods for quantifying the effect of input uncertainty on a SLS laser power control system: a recent innovation in uncertainty-tracked architecture and traditional Monte Carlo simulation. We show that recent advances in computer architecture for arithmatic on probability distributions make it possible for the first time, to perform control system uncertainty analysis with latencies under 30 ms, while achieving the same level of uncertainty analysis as Monte Carlo methods with latencies that are two orders of magnitude slower.
Foundation Models for CPS-IoT: Opportunities and Challenges
Authors: Ozan Baris, Yizhuo Chen, Gaofeng Dong, Liying Han, Tomoyoshi Kimura, Pengrui Quan, Ruijie Wang, Tianchen Wang, Tarek Abdelzaher, Mario Bergés, Paul Pu Liang, Mani Srivastava
Date: 2025-01-22 00:00:00

Methods from machine learning (ML) have transformed the implementation of Perception-Cognition-Communication-Action loops in Cyber-Physical Systems (CPS) and the Internet of Things (IoT), replacing mechanistic and basic statistical models with those derived from data. However, the first generation of ML approaches, which depend on supervised learning with annotated data to create task-specific models, faces significant limitations in scaling to the diverse sensor modalities, deployment configurations, application tasks, and operating dynamics characterizing real-world CPS-IoT systems. The success of task-agnostic foundation models (FMs), including multimodal large language models (LLMs), in addressing similar challenges across natural language, computer vision, and human speech has generated considerable enthusiasm for and exploration of FMs and LLMs as flexible building blocks in CPS-IoT analytics pipelines, promising to reduce the need for costly task-specific engineering. Nonetheless, a significant gap persists between the current capabilities of FMs and LLMs in the CPS-IoT domain and the requirements they must meet to be viable for CPS-IoT applications. In this paper, we analyze and characterize this gap through a thorough examination of the state of the art and our research, which extends beyond it in various dimensions. Based on the results of our analysis and research, we identify essential desiderata that CPS-IoT domain-specific FMs and LLMs must satisfy to bridge this gap. We also propose actions by CPS-IoT researchers to collaborate in developing key community resources necessary for establishing FMs and LLMs as foundational tools for the next generation of CPS-IoT systems.
Finite Sample Analysis of Subspace Identification Methods
Authors: Jiabao He, Ingvar Ziemann, Cristian R. Rojas, S. Joe Qin, Håkan Hjalmarsson
Date: 2025-01-17 00:00:00
Tasks: State Space Models

As one of the mainstream approaches in system identification, subspace identification methods (SIMs) are known for their simple parameterization for MIMO systems and robust numerical properties. However, a comprehensive statistical analysis of SIMs remains an open problem. Amid renewed focus on identifying state-space models in the non-asymptotic regime, this work presents a finite sample analysis for a large class of open-loop SIMs. It establishes high-probability upper bounds for system matrices obtained via SIMs, and reveals that convergence rates for estimating Markov parameters and system matrices are $\mathcal{O}(1/\sqrt{N})$ up to logarithmic terms, in line with classical asymptotic results. Following the key steps of SIMs, we arrive at the above results by a three-step procedure. In Step 1, we begin with a parsimonious SIM (PARSIM) that uses least-squares regression to estimate multiple high-order ARX models in parallel. Leveraging a recent analysis of an individual ARX model, we obtain a union error bound for a bank of ARX models. Step 2 involves model reduction via weighted singular value decomposition (SVD), where we consider different data-dependent weighting matrices and use robustness results for SVD to obtain error bounds on extended controllability and observability matrices, respectively. The final Step 3 focuses on deriving error bounds for system matrices, where two different realization algorithms, the MOESP type and the Larimore type, are considered. Although our study initially focuses on PARSIM, the methodologies apply broadly across many variants of SIMs.
Dispersal-induced instability in complex ecosystems
Authors: Joseph W. Baron, Tobias Galla
Date: 2020-03-09 00:00:00
Tasks: Diversity

In his seminal work in the 1970s, Robert May suggested that there is an upper limit to the number of species that can be sustained in stable equilibrium by an ecosystem. This deduction was at odds with both intuition and the observed complexity of many natural ecosystems. The so-called stability-diversity debate ensued, and the discussion about the factors contributing to ecosystem stability or instability continues to this day. We show in this work that dispersal can be a destabilising influence. To do this, we combine ideas from Alan Turing's work on pattern formation with May's random-matrix approach. We demonstrate how a stable equilibrium in a complex ecosystem with trophic structure can become unstable with the introduction of dispersal in space, and we discuss the factors which contribute to this effect. Our work highlights that adding more details to the model of May can give rise to more ways for an ecosystem to become unstable. Making May's simple model more realistic is therefore unlikely to entirely remove the upper bound on complexity.
Trends and Reversion in Financial Markets on Time Scales from Minutes to Decades
Authors: Sara A. Safari, Christof Schmidhuber
Date: 2025-01-28 00:00:00

We empirically analyze the reversion of financial market trends with time horizons ranging from minutes to decades. The analysis covers equities, interest rates, currencies and commodities and combines 14 years of futures tick data, 30 years of daily futures prices, 330 years of monthly asset prices, and yearly financial data since medieval times. Across asset classes, we find that markets are in a trending regime on time scales that range from a few hours to a few years, while they are in a reversion regime on shorter and longer time scales. In the trending regime, weak trends tend to persist, which can be explained by herding behavior of investors. However, in this regime trends tend to revert before they become strong enough to be statistically significant, which can be interpreted as a return of asset prices to their intrinsic value. In the reversion regime, we find the opposite pattern: weak trends tend to revert, while those trends that become statistically significant tend to persist. Our results provide a set of empirical tests of theoretical models of financial markets. We interpret them in the light of a recently proposed lattice gas model, where the lattice represents the social network of traders, the gas molecules represent the shares of financial assets, and efficient markets correspond to the critical point. If this model is accurate, the lattice gas must be near this critical point on time scales from 1 hour to a few days, with a correlation time of a few years.
Quantifying system-environment synergistic information by effective information decomposition
Authors: Mingzhe Yang, Linli Pan, Jiang Zhang
Date: 2025-01-28 00:00:00

What is the most crucial characteristic of a system with life activity? Currently, many theories have attempted to explain the most essential difference between living systems and general systems, such as the self-organization theory and the free energy principle, but there is a lack of a reasonable indicator that can measure to what extent a system can be regarded as a system with life characteristics, especially the lack of attention to the dynamic characteristics of life systems. In this article, we propose a new indicator at the level of dynamic mechanisms to measure the ability of a system to flexibly respond to the environment. We proved that this indicator satisfies the axiom system of multivariate information decomposition in the partial information decomposition (PID) framework. Through further disassembly and analysis of this indicator, we found that it is determined by the degree of entanglement between system and environmental variables in the dynamics and the magnitude of noise. We conducted measurements on cellular automata (CA), random Boolean networks, and real gene regulatory networks (GRN), verified its relationship with the type of CA and the Langton parameter, and identified that the feedback loops have high abilities to flexibly respond to the environment on the GRN. We also combined machine learning technology to prove that this framework can be applied in the case of unknown dynamics.
Sensitivity of Quantitative Susceptibility Mapping in Clinical Brain Research
Authors: Fahad Salman, Abhisri Ramesh, Thomas Jochmann, Mirjam Prayer, Ademola Adegbemigun, Jack A. Reeves, Gregory E. Wilding, Junghun Cho, Dejan Jakimovski, Niels Bergsland, Michael G. Dwyer, Robert Zivadinov, Ferdinand Schweser
Date: 2025-01-28 00:00:00
Tasks: Sensitivity

Background: Quantitative susceptibility mapping (QSM) of the brain is an advanced MRI technique for assessing tissue characteristics based on magnetic susceptibility, which varies with the composition of the tissue, such as iron, calcium, and myelin levels. QSM consists of multiple processing steps, with various choices for each step. Despite its increasing application in detecting and monitoring neurodegenerative diseases, the impact of algorithmic choices in QSM's workflow on clinical outcomes has not been thoroughly quantified. Objective: This study aimed to evaluate how choices in background field removal (BFR), dipole inversion algorithms, and anatomical referencing impact the sensitivity and reproducibility error of QSM in detecting group-level and longitudinal changes in deep gray matter susceptibility in a clinical setting. Methods: We compared 378 different QSM pipelines using a 10-year follow-up dataset of healthy adults. We analyzed the sensitivity of pipelines to detect known aging-related susceptibility changes in the DGM over time. Results: We found high variability in the sensitivity of QSM pipelines to detect susceptibility changes. The study highlighted that while most pipelines could detect changes reliably, the choice of BFR algorithm and the referencing strategy substantially influenced the outcome reproducibility error and sensitivity. Notably, pipelines using RESHARP with AMP-PE, HEIDI or LSQR inversion showed the highest overall sensitivity. Conclusions: The findings underscore the critical influence of algorithmic choices in QSM processing on the accuracy and reliability of detecting physiological changes in the brain. This has profound implications for clinical research and trials where QSM is used as a biomarker for disease progression, highlighting that careful consideration should be given to pipeline configuration to optimize clinical outcomes.