pwc-archive/papers-with-abstracts huggingface dataset:
Utilization of G-Programming Language for Educational Control Application: Case Study of Magnetic Levitation of Elastic Beam
Authors: Abdallah Amr, Mostafa Eshra, Ayman A. Nada
Date: 2024-06-20 00:00:00

This paper presents the practical employment of G-Programming tools to demonstrate, design, and implement traditional control algorithms upon magnetic levitation system. The complexity of controlling this type of fast dynamic and sensitive system is vital for highlighting the capabilities of LabVIEW G-programming in control education. PID and Lead-Lag controllers are designed and implemented within the LabVIEW environment, with the ability to tune and optimize the controllers utilizing the Virtual Instruments (VIs) of the control design and simulation toolkit. The paper enables the reader to understand the modelling, testing the control action, and dynamic simulation of the system. Then, deploying the control law in real time. It can be concluded that the G programming shows a suitable and easy tool for facilitating hands-on, experiential learning and validation in control systems engineering.
Damping Wind Farm Resonances with Current Based Model Predictive Pulse Pattern Control
Authors: Orcun Karaca, Ioannis Tsoumas, Tinus Dorfling, Ran Chen, Lennart Harnefors
Date: 2024-06-21 00:00:00

It is well-established that a proportional current control gain emulates a resistor in the converter output impedance. Even though this resistance can provide additional damping to grid resonances, its effect for traditional linear current controllers is known to be rather limited. Moreover, for medium-voltage systems, high switching frequencies are not an option due to the high switching losses. To meet the harmonic standards, it is expedient to use optimized pulse patterns. This further exacerbates the problems with the resistance of classical controllers, since an additional filtering would be required so that the current controller acts only on the fundamental component (and not on the ripple component). Such a design limits the damping effect not only in its amplitude but also in the frequency range where it is active. This paper shows that a high-bandwidth current-based model predictive pulse pattern controller can alleviate these limitations. The pulse pattern control approach can achieve a high gain even at low switching frequencies, while controlling directly the instantaneous currents (i.e., the fundamental component and the ripple together). With a fast implementation cycle, the frequency range where this damping effect is active can be further extended. Numerical studies showcase these benefits for a multi-phase medium-voltage wind power conversion system.
Generalized Vector Locus Transformation for Unbalanced Three-Phase Systems
Authors: Maitraya Avadhut Desai, Francisco Escobar, Gabriela Hug
Date: 2024-06-21 00:00:00

Coordinate transformations significantly simplify power systems computations. Most notably, the classical Clarke and dq0 transformations are widely used in three-phase systems, as together they transform balanced abc quantities into constant-valued signals. However, during unbalanced operation, the utility of these transformations diminishes, since a null 0 coordinate cannot be ensured and oscillating signals emerge. While recently proposed transformations ensure a null 0 coordinate, they still do not lead to constant-valued signals in the dq0 domain. In this letter, we propose a generalized vector locus transformation that ensures both a null 0 coordinate and constant-valued signals. Moreover, we show that, in the balanced case, the classical amplitude-invariant Clarke transformation is an instance of the proposed transformation.
Model Predictive Control of the Neural Manifold
Authors: Christof Fehrman, C. Daniel Meliza
Date: 2024-06-21 00:00:00
Tasks: model, Model Predictive Control

Neural manifolds are an attractive theoretical framework for characterizing the complex behaviors of neural populations. However, many of the tools for identifying these low-dimensional subspaces are correlational and provide limited insight into the underlying dynamics. The ability to precisely control this latent activity would allow researchers to investigate the structure and function of neural manifolds. Employing techniques from the field of optimal control, we simulate controlling the latent dynamics of a neural population using closed-loop, dynamically generated sensory inputs. Using a spiking neural network (SNN) as a model of a neural circuit, we find low-dimensional representations of both the network activity (the neural manifold) and a set of salient visual stimuli. With a data-driven latent dynamics model, we apply model predictive control (MPC) to provide anticipatory, optimal control over the trajectory of the circuit in a latent space. We are able to control the latent dynamics of the SNN to follow several reference trajectories despite observing only a subset of neurons and with a substantial amount of unknown noise injected into the network. These results provide a framework to experimentally test for causal relationships between manifold dynamics and other variables of interest such as organismal behavior and BCI performance.
Optimal estimation in spatially distributed systems: how far to share measurements from?
Authors: Juncal Arbelaiz, Bassam Bamieh, Anette E. Hosoi, Ali Jadbabaie
Date: 2024-06-20 00:00:00

We consider the centralized optimal estimation problem in spatially distributed systems. We use the setting of spatially invariant systems as an idealization for which concrete and detailed results are given. Such estimators are known to have a degree of spatial localization in the sense that the estimator gains decay in space, with the spatial decay rates serving as a proxy for how far measurements need to be shared in an optimal distributed estimator. In particular, we examine the dependence of spatial decay rates on problem specifications such as system dynamics, measurement and process noise variances, as well as their spatial autocorrelations. We propose non-dimensional parameters that characterize the decay rates as a function of problem specifications. In particular, we find an interesting matching condition between the characteristic lengthscale of the dynamics and the measurement noise correlation lengthscale for which the optimal centralized estimator is completely decentralized. A new technique - termed the Branch Point Locus - is introduced to quantify spatial decay rates in terms of analyticity regions in the complex spatial frequency plane. Our results are illustrated through two case studies of systems with dynamics modeled by diffusion and the Swift-Hohenberg equation, respectively.
Reachability and Controllability Analysis of the State Covariance for Linear Stochastic Systems
Authors: Fengjiao Liu, Panagiotis Tsiotras
Date: 2024-06-20 00:00:00

This paper studies the set of terminal state covariances that are reachable over a finite time horizon from a given initial state covariance for a linear stochastic system with additive noise. For discrete-time systems, a complete characterization of the set of reachable state covariances is given. For continuous-time systems, we present an upper bound on the set of reachable state covariances. Moreover, for both linear discrete-time and continuous-time systems, necessary and sufficient conditions are provided for the controllability of the state covariance over a finite horizon.
Deformation monitoring with Sentinel-1 Wave mode data
Authors: Piyush S. Agram, Matthew T. Calef, Kelly M. Olsen, Kimberly Carlson, Scott Arko
Date: 2024-06-20 00:00:00

We describe the salient characteristics of Sentinel-1 wave (WV) mode vignettes. We describe our approach for working with WV mode data that enables vignette-based data access and processing, thereby eliminating the Sentinel-1 Single Look Complex (SLC) data packaging and current archive metadata conventions as a bottleneck to large scale processing. We discuss the spatial and temporal coverage of Sentinel-1 WV mode data and show that a large volume of data has been acquired over land masses in this mode, thus allowing us to use it for land monitoring applications as well as ocean applications. For targeted infrastructure monitoring studies, we are able to generate coregistered, geocoded stacks of WV mode SLCs for any area of interest (AOI) with sufficient wave mode coverage, in a few minutes. We demonstrate the applicability of using WV mode data for deformation monitoring applications. Finally, we discuss the benefits and limitations of working with Sentinel-1 WV mode data.
Deep-learning-assisted reconfigurable metasurface antenna for real-time holographic beam steering
Authors: Hyunjun Ma, Jin-Soo Kim, Jong-Ho Choe, Q-Han Park
Date: 2024-06-19 00:00:00
Tasks: Decoder

We propose a metasurface antenna capable of real time holographic beam steering. An array of reconfigurable dipoeles can generate on demand far field patterns of radiation through the specific encoding of meta atomic states. i.e., the configuration of each dipole. Suitable states for the generation of the desired patterns can be identified using iteartion, but this is very slow and needs to be done for each far field pattern. Here, we present a deep learning based method for the control of a metasurface antenna with point dipole elements that vary in their state using dipole polarizability. Instead of iteration, we adopt a deep learning algorithm that combines an autoencoder with an electromagnetic scattering equation to determin the states required for a target far field pattern in real time. The scattering equation from Born approximation is used as the decoder in training the neural network, and analytic Green's function calculation is used to check the validity of Born approximation. Our learning based algorithm requires a computing time of within in 200 microseconds to determine the meta atomic states, thus enabling the real time opeartion of a holographic antenna.
Difference-in-Differences when Parallel Trends Holds Conditional on Covariates
Authors: Carolina Caetano, Brantly Callaway
Date: 2024-06-21 00:00:00
Tasks: regression

In this paper, we study difference-in-differences identification and estimation strategies when the parallel trends assumption holds after conditioning on covariates. We consider empirically relevant settings where the covariates can be time-varying, time-invariant, or both. We uncover a number of weaknesses of commonly used two-way fixed effects (TWFE) regressions in this context, even in applications with only two time periods. In addition to some weaknesses due to estimating linear regression models that are similar to cases with cross-sectional data, we also point out a collection of additional issues that we refer to as \textit{hidden linearity bias} that arise because the transformations used to eliminate the unit fixed effect also transform the covariates (e.g., taking first differences can result in the estimating equation only including the change in covariates over time, not their level, and also drop time-invariant covariates altogether). We provide simple diagnostics for assessing how susceptible a TWFE regression is to hidden linearity bias based on reformulating the TWFE regression as a weighting estimator. Finally, we propose simple alternative estimation strategies that can circumvent these issues.
MIDAS-QR with 2-Dimensional Structure
Authors: Tibor Szendrei, Arnab Bhattacharjee, Mark E. Schaffer
Date: 2024-06-21 00:00:00

Mixed frequency data has been shown to improve the performance of growth-at-risk models in the literature. Most of the research has focused on imposing structure on the high-frequency lags when estimating MIDAS-QR models akin to what is done in mean models. However, only imposing structure on the lag-dimension can potentially induce quantile variation that would otherwise not be there. In this paper we extend the framework by introducing structure on both the lag dimension and the quantile dimension. In this way we are able to shrink unnecessary quantile variation in the high-frequency variables. This leads to more gradual lag profiles in both dimensions compared to the MIDAS-QR and UMIDAS-QR. We show that this proposed method leads to further gains in nowcasting and forecasting on a pseudo-out-of-sample exercise on US data.
Network-Based Optimal Control of Pollution Growth
Authors: Fausto Gozzi, Marta Leocata, Giulia Pucci
Date: 2024-06-21 00:00:00

This paper studies a model for the optimal control (by a centralized economic agent which we call the planner) of pollution diffusion over time and space. The controls are the investments in production and depollution and the goal is to maximize an intertemporal utility function. The main novelty is the fact that the spatial component has a network structure. Moreover, in such a time-space setting we also analyze the trade-off between the use of green or non-green technologies: this also seems to be a novelty in such a setting. Extending methods of previous papers, we can solve explicitly the problem in the case of linear costs of pollution.
Prompting Whisper for QA-driven Zero-shot End-to-end Spoken Language Understanding
Authors: Mohan Li, Simon Keizer, Rama Doddipatla
Date: 2024-06-21 00:00:00
Tasks: Cross-corpus, Decoder, Question Answering, slot-filling, Slot Filling, Spoken Language Understanding

Zero-shot spoken language understanding (SLU) enables systems to comprehend user utterances in new domains without prior exposure to training data. Recent studies often rely on large language models (LLMs), leading to excessive footprints and complexity. This paper proposes the use of Whisper, a standalone speech processing model, for zero-shot end-to-end (E2E) SLU. To handle unseen semantic labels, SLU tasks are integrated into a question-answering (QA) framework, which prompts the Whisper decoder for semantics deduction. The system is efficiently trained with prefix-tuning, optimising a minimal set of parameters rather than the entire Whisper model. We show that the proposed system achieves a 40.7% absolute gain for slot filling (SLU-F1) on SLURP compared to a recently introduced zero-shot benchmark. Furthermore, it performs comparably to a Whisper-GPT-2 modular system under both in-corpus and cross-corpus evaluation settings, but with a relative 34.8% reduction in model parameters.
Exploring Audio-Visual Information Fusion for Sound Event Localization and Detection In Low-Resource Realistic Scenarios
Authors: Ya Jiang, Qing Wang, Jun Du, Maocheng Hu, Pengfei Hu, Zeyan Liu, Shi Cheng, Zhaoxu Nian, Yuxuan Dong, Mingqi Cai, Xin Fang, Chin-Hui Lee
Date: 2024-06-21 00:00:00
Tasks: Data Augmentation, Sound Event Localization and Detection

This study presents an audio-visual information fusion approach to sound event localization and detection (SELD) in low-resource scenarios. We aim at utilizing audio and video modality information through cross-modal learning and multi-modal fusion. First, we propose a cross-modal teacher-student learning (TSL) framework to transfer information from an audio-only teacher model, trained on a rich collection of audio data with multiple data augmentation techniques, to an audio-visual student model trained with only a limited set of multi-modal data. Next, we propose a two-stage audio-visual fusion strategy, consisting of an early feature fusion and a late video-guided decision fusion to exploit synergies between audio and video modalities. Finally, we introduce an innovative video pixel swapping (VPS) technique to extend an audio channel swapping (ACS) method to an audio-visual joint augmentation. Evaluation results on the Detection and Classification of Acoustic Scenes and Events (DCASE) 2023 Challenge data set demonstrate significant improvements in SELD performances. Furthermore, our submission to the SELD task of the DCASE 2023 Challenge ranks first place by effectively integrating the proposed techniques into a model ensemble.
DExter: Learning and Controlling Performance Expression with Diffusion Models
Authors: huan zhang, Shreyan Chowdhury, Carlos Eduardo Cancino-Chacón, Jinhua Liang, Simon Dixon, Gerhard Widmer
Date: 2024-06-21 00:00:00
Tasks: Music Performance Rendering

In the pursuit of developing expressive music performance models using artificial intelligence, this paper introduces DExter, a new approach leveraging diffusion probabilistic models to render Western classical piano performances. In this approach, performance parameters are represented in a continuous expression space and a diffusion model is trained to predict these continuous parameters while being conditioned on the musical score. Furthermore, DExter also enables the generation of interpretations (expressive variations of a performance) guided by perceptually meaningful features by conditioning jointly on score and perceptual feature representations. Consequently, we find that our model is useful for learning expressive performance, generating perceptually steered performances, and transferring performance styles. We assess the model through quantitative and qualitative analyses, focusing on specific performance metrics regarding dimensions like asynchrony and articulation, as well as through listening tests comparing generated performances with different human interpretations. Results show that DExter is able to capture the time-varying correlation of the expressive parameters, and compares well to existing rendering models in subjectively evaluated ratings. The perceptual-feature-conditioned generation and transferring capabilities of DExter are verified by a proxy model predicting perceptual characteristics of differently steered performances.
Towards Intelligent Speech Assistants in Operating Rooms: A Multimodal Model for Surgical Workflow Analysis
Authors: Kubilay Can Demir, Belen Lojo Rodriguez, Tobias Weise, Andreas Maier, Seung Hee Yang
Date: 2024-06-17 00:00:00
Tasks: Surgical phase recognition

To develop intelligent speech assistants and integrate them seamlessly with intra-operative decision-support frameworks, accurate and efficient surgical phase recognition is a prerequisite. In this study, we propose a multimodal framework based on Gated Multimodal Units (GMU) and Multi-Stage Temporal Convolutional Networks (MS-TCN) to recognize surgical phases of port-catheter placement operations. Our method merges speech and image models and uses them separately in different surgical phases. Based on the evaluation of 28 operations, we report a frame-wise accuracy of 92.65 $\pm$ 3.52% and an F1-score of 92.30 $\pm$ 3.82%. Our results show approximately 10% improvement in both metrics over previous work and validate the effectiveness of integrating multimodal data for the surgical phase recognition task. We further investigate the contribution of individual data channels by comparing mono-modal models with multimodal models.
A Deep Learning System for Rapid and Accurate Warning of Acute Aortic Syndrome on Non-contrast CT in China
Authors: Yujian Hu, Yilang Xiang, Yan-Jie Zhou, Yangyan He, Dehai Lang, Shifeng Yang, Xiaolong Du, Chunlan Den, Youyao Xu, Gaofeng Wang, Zhengyao Ding, Jingyong Huang, Wenjun Zhao, Xuejun Wu, Donglin Li, Qianqian Zhu, Zhenjiang Li, Chenyang Qiu, Ziheng Wu, Yunjun He, Chen Tian, Yihui Qiu, Zuodong Lin, Xiaolong Zhang, Yuan He, Zhenpeng Yuan, Xiaoxiang Zhou, Rong Fan, Ruihan Chen, Wenchao Guo, Jianpeng Zhang, Tony C. W. Mok, Zi Li, Mannudeep K. Kalra, Le Lu, Wenbo Xiao, Xiaoqiang Li, Yun Bian, Chengwei Shao, Guofu Wang, Wei Lu, Zhengxing Huang, Minfeng Xu, HongKun Zhang
Date: 2024-06-14 00:00:00
Tasks: Anatomy, Diagnostic, Specificity

The accurate and timely diagnosis of acute aortic syndromes (AAS) in patients presenting with acute chest pain remains a clinical challenge. Aortic CT angiography (CTA) is the imaging protocol of choice in patients with suspected AAS. However, due to economic and workflow constraints in China, the majority of suspected patients initially undergo non-contrast CT as the initial imaging testing, and CTA is reserved for those at higher risk. In this work, we present an artificial intelligence-based warning system, iAorta, using non-contrast CT for AAS identification in China, which demonstrates remarkably high accuracy and provides clinicians with interpretable warnings. iAorta was evaluated through a comprehensive step-wise study. In the multi-center retrospective study (n = 20,750), iAorta achieved a mean area under the receiver operating curve (AUC) of 0.958 (95% CI 0.950-0.967). In the large-scale real-world study (n = 137,525), iAorta demonstrated consistently high performance across various non-contrast CT protocols, achieving a sensitivity of 0.913-0.942 and a specificity of 0.991-0.993. In the prospective comparative study (n = 13,846), iAorta demonstrated the capability to significantly shorten the time to correct diagnostic pathway. For the prospective pilot deployment that we conducted, iAorta correctly identified 21 out of 22 patients with AAS among 15,584 consecutive patients presenting with acute chest pain and under non-contrast CT protocol in the emergency department (ED) and enabled the average diagnostic time of these 21 AAS positive patients to be 102.1 (75-133) mins. Last, the iAorta can help avoid delayed or missed diagnosis of AAS in settings where non-contrast CT remains the unavoidable the initial or only imaging test in resource-constrained regions and in patients who cannot or did not receive intravenous contrast.
Model-Based Learning for Network Clock Synchronization in Half-Duplex TDMA Networks
Authors: Itay Zino, Ron Dabora, H. Vincent Poor
Date: 2024-06-21 00:00:00

Supporting increasingly higher rates in wireless networks requires highly accurate clock synchronization across the nodes. Motivated by this need, in this work we consider distributed clock synchronization for half-duplex (HD) TDMA wireless networks. We focus on pulse-coupling (PC)-based synchronization as it is practically advantageous for high-speed networks using low-power nodes. Previous works on PC-based synchronization for TDMA networks assumed full-duplex communications, and focused on correcting the clock phase at each node, without synchronizing clocks' frequencies. However, as in the HD regime corrections are temporally sparse, uncompensated clock frequency differences between the nodes result in large phase drifts between updates. Moreover, as the clocks determine the processing rates at the nodes, leaving the clocks' frequencies unsynchronized results in processing rates mismatch between the nodes, leading to a throughput reduction. Our goal in this work is to synchronize both clock frequency and clock phase across the clocks in HD TDMA networks, via distributed processing. The key challenges are the coupling between frequency correction and phase correction, and the lack of a computationally efficient analytical framework for determining the optimal correction signal at the nodes. We address these challenges via a DNN-aided nested loop structure in which the DNN are used for generating the weights applied to the loop input for computing the correction signal. This loop is operated in a sequential manner which decouples frequency and phase compensations, thereby facilitating synchronization of both parameters. Performance evaluation shows that the proposed scheme significantly improves synchronization accuracy compared to the conventional approaches.