pwc-archive/papers-with-abstracts huggingface dataset:
Digital twin with automatic disturbance detection for an expert-controlled SAG mill
Authors: Paulina Quintanilla, Francisco Fernández, Cristóbal Mancilla, Matías Rojas, Daniel Navia
Date: 2025-03-06 00:00:00

This study presents the development and validation of a digital twin for a semi-autogenous grinding (SAG) mill controlled by an expert system. The digital twin integrates three key components of the closed-loop operation: (1) fuzzy logic for expert control, (2) a state-space model for regulatory control, and (3) a recurrent neural network to simulate the SAG mill process. The digital twin is combined with a statistical framework for automatically detecting process disturbances (or critical operations), which triggers model retraining only when deviations from expected behaviour are identified, ensuring continuous updates with new data to enhance the SAG supervision. The model was trained with 68 hours of operational industrial data and validated with an additional 8 hours, allowing it to predict mill behaviour within a 2.5-minute horizon at 30-second intervals with errors smaller than 5%.
Formally Verified Neural Network Controllers for Incremental Input-to-State Stability of Unknown Discrete-Time Systems
Authors: Ahan Basu, Bhabani Shankar Dey, Pushpak Jagtap
Date: 2025-03-06 00:00:00

This work aims to synthesize a controller that ensures that an unknown discrete-time system is incrementally input-to-state stable ($\delta$-ISS). In this work, we introduce the notion of $\delta$-ISS control Lyapunov function ($\delta$-ISS-CLF), which, in conjunction with the controller, ensures that the closed-loop system is incrementally ISS. To address the unknown dynamics of the system, we parameterize the controller as well as the $\delta$-ISS-CLF as neural networks and learn them by utilizing the sampled data from the state space of the unknown system. To formally verify the obtained $\delta$-ISS-CLF, we develop a validity condition and incorporate the condition into the training framework to ensure a provable correctness guarantee at the end of the training process. Finally, the usefulness of the proposed approach is proved using multiple case studies - the first one is a scalar system with a non-affine non-polynomial structure, the second example is a one-link manipulator system, the third system is a nonlinear Moore-Grietzer model of the jet engine and the final one is a rotating rigid spacecraft model.
Quantitative Flow Approximation Properties of Narrow Neural ODEs
Authors: Karthik Elamvazhuthi
Date: 2025-03-06 00:00:00
Tasks: LEMMA

In this note, we revisit the problem of flow approximation properties of neural ordinary differential equations (NODEs). The approximation properties have been considered as a flow controllability problem in recent literature. The neural ODE is considered {\it narrow} when the parameters have dimension equal to the input of the neural network, and hence have limited width. We derive the relation of narrow NODEs in approximating flows of shallow but wide NODEs. Due to existing results on approximation properties of shallow neural networks, this facilitates understanding which kind of flows of dynamical systems can be approximated using narrow neural ODEs. While approximation properties of narrow NODEs have been established in literature, the proofs often involve extensive constructions or require invoking deep controllability theorems from control theory. In this paper, we provide a simpler proof technique that involves only ideas from ODEs and Gr{\"o}nwall's lemma. Moreover, we provide an estimate on the number of switches needed for the time dependent weights of the narrow NODE to mimic the behavior of a NODE with a single layer wide neural network as the velocity field.
Vibration Analysis and Mitigation in Semiconductor Motion Stages Using DMAIC Methodology- A Case Study
Authors: Yin Li, Hua Chen, Fugee Tsung
Date: 2025-03-06 00:00:00

Motion stages are critical in semiconductor manufacturing equipment for processes like die bonding, wafer loading, and chip packaging, as their performance must meet the industry's stringent precision requirements. Vibration, a significant yet often overlooked adversary to precision motion stages, is challenging to identify and mitigate due to its subtle nature. This study, conducted at a motion stage manufacturer facing frequent vibration-related complaints, proposes a novel approach to resolving vibration issues. By leveraging the DMAIC methodology, it introduces VIBGUARD, an active vibration monitoring and mitigation solution, instead of solely focusing on traditional hardware vibration control. This comprehensive strategy enhances value and competitiveness, increasing UPH (units per hour) by 15.3% from 8,500 to 9,800 and reducing downtime by 68.2% from 2.2 to 0.7 occurrences per month. This case study and the DMAIC methodology offer valuable resources for quality control and problem analysis in the semiconductor industry.
Synthesis of Functional Unknown Input Observers for LTV and MIMO LTI Systems with Arbitrary Relative Degree
Authors: Alexey A. Margun, Alexey A. Bobtsov, Denis V. Efimov, Alexandr D. Panin, Mariia O. Rassolova
Date: 2025-03-05 00:00:00
Tasks: parameter estimation

This article focuses on the development of functional unknown input observers for systems with arbitrary relative degree. Two distinct approaches are presented to address this challenge. The first approach is tailored to a class of time-varying systems expressed in a canonical controllable form. This method leverages the Generalized Parameter Estimation-Based Observer framework. The article derives the conditions for applicability of this solution and outlines the limitations on the number of estimable state variables. The second approach targets multi-input multi-output systems. In contrast to existing methods, the proposed solution is applicable to systems with arbitrary relative degree, significantly broadening its scope of application. The theoretical results are validated through simulation studies, which demonstrate the effectiveness of the proposed observers.
From Target Tracking to Targeting Track -- Part III: Stochastic Process Modeling and Online Learning
Authors: Tiancheng Li, Jingyuan Wang, Guchong Li, Dengwei Gao
Date: 2025-03-03 00:00:00

This is the third part of a series of studies that model the target trajectory, which describes the target state evolution over continuous time, as a sample path of a stochastic process (SP). By adopting a deterministic-stochastic decomposition framework, we decompose the learning of the trajectory SP into two sequential stages: the first fits the deterministic trend of the trajectory using a curve function of time, while the second estimates the residual stochastic component through parametric learning of either a Gaussian process (GP) or Student's-$t$ process (StP). This leads to a Markov-free data-driven tracking approach that produces the continuous-time trajectory with minimal prior knowledge of the target dynamics. Notably, our approach explicitly models both the temporal correlations of the state sequence and of measurement noises through the SP framework. It does not only take advantage of the smooth trend of the target but also makes use of the long-term temporal correlation of both the data noise and the model fitting error. Simulations in four maneuvering target tracking scenarios have demonstrated its effectiveness and superiority in comparison with existing approaches.
Automating Hot-Rolling: Designing an Integrated Mechatronics System for Enhanced Efficiency in Sheet Metal Production
Authors: Mostafa A. Mostafa, Mohamed Khaled, Abdelrahman Ali, Amr Mostafa, Mariam Mohamed, Omar Ahmed, Osama Khalil
Date: 2025-03-03 00:00:00

The hot-rolling process is a critical stage in sheet metal production within the heavy steel industry. Traditionally, parameter adjustments such as sheet metal velocity and roll gap are performed manually, leading to inefficiencies and limited precision. This project introduces an integrated mechatronics system designed to automate the control of rolling speed and sheet metal thickness, enhancing efficiency, consistency, and quality. The proposed system consists of a pair of rolls applying compression loads, with a mechanism for gap control, suitable motors and sensors, and dynamic modeling to optimize performance. Through simulation and practical implementation strategies, we demonstrate the feasibility of automating the hot-rolling process. By integrating mechatronics, this solution aims to modernize sheet metal production, improve productivity, and enhance product quality in the steel industry.
Fault Localization and State Estimation of Power Grid under Parallel Cyber-Physical Attacks
Authors: Junhao Ren, Kai Zhao, Guangxiao Zhang, Xinghua Liu, Chao Zhai, Gaoxi Xiao
Date: 2025-03-03 00:00:00
Tasks: Blocking, Fault Diagnosis, Fault localization, Graph Attention, State Estimation

Parallel cyber-physical attacks (PCPA) refer to those attacks on power grids by disturbing/cutting off physical transmission lines and meanwhile blocking transmission of measurement data to dwarf or delay the system protection and recovery actions. Such fierce hostile attacks impose critical threats to the modern power grids when there is a fusion of power grids and telecommunication technologies. In this paper, we investigate the fault diagnosis problem of faulty transmission lines under a broader spectrum of PCPA for a linearized (or DC) power flow model. The physical attack mechanism of PCPA includes not only disconnection but also admittance value modification on transmission lines, for example, by invading distributed flexible AC transmission system (D-FACTS). To tackle the problem, we first recover the information of voltage phase angles within the attacked area. Using the information of voltage phase angle and power injection of buses, a graph attention network-based fault localization (GAT-FL) algorithm is proposed to find the locations of the physical attacks. By capitalizing on the feature extraction capability of the GAT on graph data, the fault localization algorithm outperforms the existing results when under cyber attacks, e.g., denial of service (DoS) attacks. A line state identification algorithm is then developed to identify the states of the transmission lines within the attacked area. Specifically, the algorithm restores the power injection of buses within the attacked area and then identities the state of all the transmission lines within the attacked area by solving a linear programming (LP) problem. Experimental simulations are effectiveness of the proposed fault diagnosis algorithms.
Ro-To-Go! Robust Reactive Control with Signal Temporal Logic
Authors: Roland Ilyes, Lara Brudermüller, Nick Hawes, Bruno Lacerda
Date: 2025-02-28 00:00:00
Tasks: Decision Making, Model Predictive Control

Signal Temporal Logic (STL) robustness is a common objective for optimal robot control, but its dependence on history limits the robot's decision-making capabilities when used in Model Predictive Control (MPC) approaches. In this work, we introduce Signal Temporal Logic robustness-to-go (Ro-To-Go), a new quantitative semantics for the logic that isolates the contributions of suffix trajectories. We prove its relationship to formula progression for Metric Temporal Logic, and show that the robustness-to-go depends only on the suffix trajectory and progressed formula. We implement robustness-to-go as the objective in an MPC algorithm and use formula progression to efficiently evaluate it online. We test the algorithm in simulation and compare it to MPC using traditional STL robustness. Our experiments show that using robustness-to-go results in a higher success rate.
Collaborative Drill Alignment in Surgical Robotics
Authors: Daniel Larby, Joshua Kershaw, Matthew Allen, Fulvio Forni
Date: 2025-02-28 00:00:00

Robotic assistance allows surgeries to be reliably and accurately executed while still under direct supervision of the surgeon, combining the strengths of robotic technology with the surgeon's expertise. This paper describes a robotic system designed to assist in surgical procedures by implementing a virtual drill guide. The system integrates virtual-fixture functionality using a novel virtual-mechanism controller with additional visual feedback. The controller constrains the tool to the desired axis, while allowing axial motion to remain under the surgeon's control. Compared to prior virtual-fixture approaches -- which primarily perform pure energy-shaping and damping injection with linear springs and dampers -- our controller uses a virtual prismatic joint to which the robot is constrained by nonlinear springs, allowing us to easily shape the dynamics of the system. We detail the calibration procedures required to achieve sufficient precision, and describe the implementation of the controller. We apply this system to a veterinary procedure: drilling for transcondylar screw placement in dogs. The results of the trials on 3D-printed bone models demonstrate sufficient precision to perform the procedure and suggest improved angular accuracy and reduced exit translation errors compared to patient specific guides (PSG). Discussion and future improvements follow.
The optical and infrared are connected
Authors: Christian K. Jespersen, Peter Melchior, David N. Spergel, Andy D. Goulding, ChangHoon Hahn, Kartheik G. Iyer
Date: 2025-03-05 00:00:00

Galaxies are often modelled as composites of separable components with distinct spectral signatures, implying that different wavelength ranges are only weakly correlated. They are not. We present a data-driven model which exploits subtle correlations between physical processes to accurately predict infrared (IR) WISE photometry from a neural summary of optical SDSS spectra. The model achieves accuracies of $\chi^2_N \approx 1$ for all photometric bands in WISE, as well as good colors. We are also able to tightly constrain typically IR-derived properties, e.g. the bolometric luminosities of AGN and dust parameters such as $\mathrm{q_{PAH}}$. We find that current SED-fitting methods are incapable of making comparable predictions, and that model misspecification often leads to correlated biases in star-formation rates and AGN luminosities. To help improve SED models, we determine what features of the optical spectrum are responsible for our improved predictions, and identify several lines (CaII, SrII, FeI, [OII] and H$\alpha$), which point to the complex chronology of star formation and chemical enrichment being incorrectly modelled.
Image Data Augmentation for the TAIGA-IACT Experiment with Conditional Generative Adversarial Networks
Authors: Yu. Yu. Dubenskaya, A. P. Kryukov, E. O. Gres, S. P. Polyakov, E. B. Postnikov, P. A. Volchugov, A. A. Vlaskina, D. P. Zhurov
Date: 2025-03-06 00:00:00
Tasks: Data Augmentation

Modern Imaging Atmospheric Cherenkov Telescopes (IACTs) generate a huge amount of data that must be classified automatically, ideally in real time. Currently, machine learning-based solutions are increasingly being used to solve classification problems. However, these classifiers require proper training data sets to work correctly. The problem with training neural networks on real IACT data is that these data need to be pre-labeled, whereas such labeling is difficult and its results are estimates. In addition, the distribution of incoming events is highly imbalanced. Firstly, there is an imbalance in the types of events, since the number of detected gamma quanta is significantly less than the number of protons. Secondly, the energy distribution of particles of the same type is also imbalanced, since high-energy particles are extremely rare. This imbalance results in poorly trained classifiers that, once trained, do not handle rare events correctly. Using only conventional Monte Carlo event simulation methods to solve this problem is possible, but extremely resource-intensive and time-consuming. To address this issue, we propose to perform data augmentation with artificially generated events of the desired type and energy using conditional generative adversarial networks (cGANs), distinguishing classes by energy values. In the paper, we describe a simple algorithm for generating balanced data sets using cGANs. Thus, the proposed neural network model produces both imbalanced data sets for physical analysis as well as balanced data sets suitable for training other neural networks.
DeepGrav: Anomalous Gravitational-Wave Detection Through Deep Latent Features
Authors: Jianqi Yan, Alex P. Leung, Zhiyuan Pei, David C. Y. Hui, Sangin Kim
Date: 2025-03-05 00:00:00
Tasks: Anomaly Detection, Data Augmentation, Gravitational Wave Detection

This work introduces a novel deep learning-based approach for gravitational wave anomaly detection, aiming to overcome the limitations of traditional matched filtering techniques in identifying unknown waveform gravitational wave signals. We introduce a modified convolutional neural network architecture inspired by ResNet that leverages residual blocks to extract high-dimensional features, effectively capturing subtle differences between background noise and gravitational wave signals. This network architecture learns a high-dimensional projection while preserving discrepancies with the original input, facilitating precise identification of gravitational wave signals. In our experiments, we implement an innovative data augmentation strategy that generates new data by computing the arithmetic mean of multiple signal samples while retaining the key features of the original signals. In the NSF HDR A3D3: Detecting Anomalous Gravitational Wave Signals competition, it is honorable for us (group name: easonyan123) to get to the first place at the end with our model achieving a true negative rate (TNR) of 0.9708 during development/validation phase and 0.9832 on an unseen challenge dataset during final/testing phase, the highest among all competitors. These results demonstrate that our method not only achieves excellent generalization performance but also maintains robust adaptability in addressing the complex uncertainties inherent in gravitational wave anomaly detection.
Improving the Temporal Resolution of SOHO/MDI Magnetograms of Solar Active Regions Using a Deep Generative Model
Authors: Jialiang Li, Vasyl Yurchyshyn, Jason T. L. Wang, Haimin Wang, Yasser Abduallah, Khalid A. Alobaid, Chunhui Xu, Ruizhu Chen, Yan Xu
Date: 2025-03-05 00:00:00
Tasks: Super-Resolution

We present a novel deep generative model, named GenMDI, to improve the temporal resolution of line-of-sight (LOS) magnetograms of solar active regions (ARs) collected by the Michelson Doppler Imager (MDI) on board the Solar and Heliospheric Observatory (SOHO). Unlike previous studies that focus primarily on spatial super-resolution of MDI magnetograms, our approach can perform temporal super-resolution, which generates and inserts synthetic data between observed MDI magnetograms, thus providing finer temporal structure and enhanced details in the LOS data. The GenMDI model employs a conditional diffusion process, which synthesizes images by considering both preceding and subsequent magnetograms, ensuring that the generated images are not only of high-quality, but also temporally coherent with the surrounding data. Experimental results show that the GenMDI model performs better than the traditional linear interpolation method, especially in ARs with dynamic evolution in magnetic fields.
Beyond Disorder: Unveiling Cooperativeness in Multidirectional Associative Memories
Authors: Andrea Alessandrelli, Adriano Barra, Andrea Ladiana, Andrea Lepre, Federico Ricci-Tersenghi
Date: 2025-03-06 00:00:00
Tasks: Retrieval

By leveraging tools from the statistical mechanics of complex systems, in these short notes we extend the architecture of a neural network for hetero-associative memory (called three-directional associative memories, TAM) to explore supervised and unsupervised learning protocols. In particular, by providing entropic-heterogeneous datasets to its various layers, we predict and quantify a new emergent phenomenon -- that we term {\em layer's cooperativeness} -- where the interplay of dataset entropies across network's layers enhances their retrieval capabilities Beyond those they would have without reciprocal influence. Naively we would expect layers trained with less informative datasets to develop smaller retrieval regions compared to those pertaining to layers that experienced more information: this does not happen and all the retrieval regions settle to the same amplitude, allowing for optimal retrieval performance globally. This cooperative dynamics marks a significant advancement in understanding emergent computational capabilities within disordered systems.
A Materials Map Integrating Experimental and Computational Data via Graph-Based Machine Learning for Enhanced Materials Discovery
Authors: Yusuke Hashimoto, Xue Jia, Hao Li, Takaaki Tomai
Date: 2025-03-10 00:00:00
Tasks: Attribute, Property Prediction

Materials informatics (MI), emerging from the integration of materials science and data science, is expected to significantly accelerate material development and discovery. The data used in MI are derived from both computational and experimental studies; however, their integration remains challenging. In our previous study, we reported the integration of these datasets by applying a machine learning model that is trained on the experimental dataset to the compositional data stored in the computational database. In this study, we use the obtained datasets to construct materials maps, which visualize the relationships between material properties and structural features, aiming to support experimental researchers. The materials map is constructed using the MatDeepLearn (MDL) framework, which implements materials property prediction using graph-based representations of material structure and deep learning modeling. Through statistical analysis, we find that the MDL framework using the message passing neural network (MPNN) architecture efficiently extracts features reflecting the structural complexity of materials. Moreover, we find that this advantage does not necessarily translate into improved accuracy in the prediction of material properties. We attribute this unexpected outcome to the high learning performance inherent in MPNN, which can contribute to the structuring of data points within the materials map.