pwc-archive/papers-with-abstracts huggingface dataset:
Adaptive Compatible Performance Control for Spacecraft Attitude Control under Motion Constraints with Guaranteed Accuracy
Authors: Jiakun Lei, Tao Meng, Yang Zhu, Kun Wang, Weijia Wang
Date: 2023-05-31 00:00:00

This paper focuses on the problem of spacecraft attitude control in the presence of time-varying parameter uncertainties and multiple constraints, accounting for angular velocity limitation, performance requirements, and input saturation. To tackle this problem, we propose a modified framework called Compatible Performance Control (CPC), which integrates the Prescribed Performance Control (PPC) scheme with a contradiction detection and alleviation strategy. Firstly, by introducing the Zeroing Barrier Function (ZBF) concept, we propose a detection strategy to yield judgment on the compatibility between the angular velocity constraint and the performance envelope constraint. Subsequently, we propose a projection operator-governed dynamical system with a varying upper bound to generate an appropriate bounded performance envelope-modification signal if a contradiction exists, thereby alleviating the contradiction and promoting compatibility within the system. Next, a dynamical filter technique is introduced to construct a bounded reference velocity signal to address the angular velocity limitation. Furthermore, we employ a time-varying gain technique to address the challenge posed by time-varying parameter uncertainties, further developing an adaptive strategy that exhibits robustness on disturbance rejection. By utilizing the proposed CPC scheme and time-varying gain adaptive strategy, we construct an adaptive CPC controller, which guarantees the ultimate boundedness of the system, and all constraints are satisfied simultaneously during the whole control process. Finally, numerical simulation results are presented to show the effectiveness of the proposed framework.
Monitoring the evolution of dimensional accuracy and product properties in property-controlled forming processes
Authors: Sophie Charlotte Stebner, Juri Martschin, Bahman Arian, Stefan Dietrich, Martin Feistle, Sebastian Hütter, Rémi Lafarge, Robert Laue, Xinyang Li, Christopher Schulte, Daniel Spies, Ferdinand Thein, Frank Wendler, Malte Wrobel, Julian Rozo Vasquez, Michael Dölz, Sebastian Münstermann
Date: 2023-05-31 00:00:00

As recent trends in manufacturing engineering disciplines show a clear development in the sustainable as well as economically efficient design of forming processes, monitoring techniques have been gaining in relevance. In terms of monitoring of product properties, most processes are currently open-loop controlled, entailing that the microstructure evolution, which determines the final product properties, is not considered. However, a closed-loop control that can adjust and manipulate the process actuators according to the required product properties of the component will lead to a considerable increase in efficiency of the processes regarding resources and will decrease postproduction of the component. For most forming processes, one set of component dimensions will result in a certain set of product properties. However, to successfully establish closed-loop property controls for the processes, a systematic understanding of the reciprocity of the dimensions after forming and final product properties must be established. This work investigates the evolution of dimensional accuracy as well as product properties for a series of forming processes that utilize different degrees of freedom for process control.
Pulse Compression Probing for Tracking Distribution Feeder Models
Authors: Nicholas Piaquadio, N. Eva Wu, Morteza Sarailoo, Jianzhuang Huang
Date: 2023-05-31 00:00:00

A Pulse-Compression Probing (PCP) method is applied in time-domain to identify an equivalent circuit model of a distribution network as seen from the transmission grid. A Pseudo-Random Binary Pulse Train (PRBPT) is injected as a voltage signal at the input of the feeder and processed to recover the impulse response. A transfer function and circuit model is fitted to the response, allowing the feeder to be modeled as a quasi-steady-state sinusoidal (QSSS) source behind a network. The method is verified on the IEEE 13-Node Distribution Test System, identifying a second order circuit model with less than seven cycles latency and a signal to noise ratio of 15.07 dB in the input feeder current.
Machine Learning Applications in Cascading Failure Analysis in Power Systems: A Review
Authors: Naeem Md Sami, Mia Naeini
Date: 2023-05-30 00:00:00
Tasks: Survey

Cascading failures pose a significant threat to power grids and have garnered considerable research interest in the power system domain. The inherent uncertainty and severe impact associated with cascading failures have raised concerns, prompting the development of various techniques to study these complex phenomena. In recent years, advancements in monitoring technologies and the availability of large volumes of data from power systems, coupled with the emergence of intelligent algorithms, have made machine learning (ML) techniques increasingly attractive for addressing cascading failure problems. This survey provides a comprehensive overview of ML-based techniques for analyzing cascading failures in power systems. The survey categorizes these techniques based on the evolutionary phases of the cascade process in power systems, as well as studies focusing on cascade resiliency before the occurrence of cascades and problems related to cascades after their termination. By organizing and presenting these works into relevant categories, this survey aims to offer insights and a systematic understanding the role of ML in mitigating cascading failures in power systems.
Bearing-Constrained Leader-Follower Formation of Single-Integrators with Disturbance Rejection: Adaptive Variable-Structure Approaches
Authors: Thanh Truong Nguyen, Dung Van Vu, Tuynh Van Pham, Minh Hoang Trinh
Date: 2023-05-30 00:00:00

This paper studies the problem of stabilizing a leader-follower formation specified by a set of bearing constraints and being disturbed by some unknown uniformly bounded disturbance{s}. A set of leaders are positioned at their desired positions, while each follower is modeled by a single integrator with an additive time-varying disturbance. Adaptive variable-structure control laws using displacements or only bearing vectors are provided to stabilize the desired formation. Thanks to the adaptive mechanisms, the proposed control laws require neither information of the bearing Laplacian nor the disturbances' directions and upper bounds. It is further proved that when the leaders are moving with a same bounded uniformly continuous velocity, the moving target formation can still be achieved under the proposed control laws. Simulation results are also given to support the stability analysis.
Deep Neural Network Estimation in Panel Data Models
Authors: Ilias Chronopoulos, Katerina Chrysikou, George Kapetanios, James Mitchell, Aristeidis Raftapostolos
Date: 2023-05-31 00:00:00
Tasks: Time Series

In this paper we study neural networks and their approximating power in panel data models. We provide asymptotic guarantees on deep feed-forward neural network estimation of the conditional mean, building on the work of Farrell et al. (2021), and explore latent patterns in the cross-section. We use the proposed estimators to forecast the progression of new COVID-19 cases across the G7 countries during the pandemic. We find significant forecasting gains over both linear panel and nonlinear time series models. Containment or lockdown policies, as instigated at the national-level by governments, are found to have out-of-sample predictive power for new COVID-19 cases. We illustrate how the use of partial derivatives can help open the "black-box" of neural networks and facilitate semi-structural analysis: school and workplace closures are found to have been effective policies at restricting the progression of the pandemic across the G7 countries. But our methods illustrate significant heterogeneity and time-variation in the effectiveness of specific containment policies.
Quasi-Score Matching Estimation for Spatial Autoregressive Model with Random Weights Matrix and Regressors
Authors: Xuan Liang, Tao Zou
Date: 2023-05-31 00:00:00
Tasks: Econometrics

With the rapid advancements in technology for data collection, the application of the spatial autoregressive (SAR) model has become increasingly prevalent in real-world analysis, particularly when dealing with large datasets. However, the commonly used quasi-maximum likelihood estimation (QMLE) for the SAR model is not computationally scalable to handle the data with a large size. In addition, when establishing the asymptotic properties of the parameter estimators of the SAR model, both weights matrix and regressors are assumed to be nonstochastic in classical spatial econometrics, which is perhaps not realistic in real applications. Motivated by the machine learning literature, this paper proposes quasi-score matching estimation for the SAR model. This new estimation approach is developed based on the likelihood, but significantly reduces the computational complexity of the QMLE. The asymptotic properties of parameter estimators under the random weights matrix and regressors are established, which provides a new theoretical framework for the asymptotic inference of the SAR-type models. The usefulness of the quasi-score matching estimation and its asymptotic inference is illustrated via extensive simulation studies and a case study of an anti-conflict social network experiment for middle school students.
A Simple Method for Predicting Covariance Matrices of Financial Returns
Authors: Kasper Johansson, Mehmet Giray Ogut, Markus Pelger, Thomas Schmelzer, Stephen Boyd
Date: 2023-05-31 00:00:00
Tasks: Portfolio Optimization

We consider the well-studied problem of predicting the time-varying covariance matrix of a vector of financial returns. Popular methods range from simple predictors like rolling window or exponentially weighted moving average (EWMA) to more sophisticated predictors such as generalized autoregressive conditional heteroscedastic (GARCH) type methods. Building on a specific covariance estimator suggested by Engle in 2002, we propose a relatively simple extension that requires little or no tuning or fitting, is interpretable, and produces results at least as good as MGARCH, a popular extension of GARCH that handles multiple assets. To evaluate predictors we introduce a novel approach, evaluating the regret of the log-likelihood over a time period such as a quarter. This metric allows us to see not only how well a covariance predictor does over all, but also how quickly it reacts to changes in market conditions. Our simple predictor outperforms MGARCH in terms of regret. We also test covariance predictors on downstream applications such as portfolio optimization methods that depend on the covariance matrix. For these applications our simple covariance predictor and MGARCH perform similarly.
Paradoxical Oddities in Two Multiwinner Elections from Scotland
Authors: Adam Graham-Squire, David McCune
Date: 2023-05-31 00:00:00

Ranked-choice voting anomalies such as monotonicity paradoxes have been extensively studied through creating hypothetical examples and generating elections under various models of voter behavior. However, very few real-world examples of such voting paradoxes have been found and analyzed. We investigate two single-transferable vote elections from Scotland that demonstrate upward monotonicity, downward monotonicity, no-show, and committee size paradoxes. These paradoxes are rarely observed in real-world elections, and this article is the first case study of such paradoxes in multiwinner elections.
FN-SSL: Full-Band and Narrow-Band Fusion for Sound Source Localization
Authors: Yabo Wang, Bing Yang, Xiaofei Li
Date: 2023-05-31 00:00:00
Tasks: Sound Source Localization

Extracting direct-path spatial features is critical for sound source localization in adverse acoustic environments. This paper proposes a full-band and narrow-band fusion network for estimating direct-path inter-channel phase difference (DP-IPD) from microphone signals. The alternating full-band and narrow-band layers are responsible for learning the full-band correlation and narrow-band extraction of DP-IPD, respectively. Experiments show that the proposed network noticeably outperforms other advanced methods on both simulated and real-world data.
Few-Shot Speaker Identification Using Lightweight Prototypical Network with Feature Grouping and Interaction
Authors: Yanxiong Li, Hao Chen, Wenchang Cao, Qisheng Huang, Qianhua He
Date: 2023-05-31 00:00:00
Tasks: Speaker Identification

Existing methods for few-shot speaker identification (FSSI) obtain high accuracy, but their computational complexities and model sizes need to be reduced for lightweight applications. In this work, we propose a FSSI method using a lightweight prototypical network with the final goal to implement the FSSI on intelligent terminals with limited resources, such as smart watches and smart speakers. In the proposed prototypical network, an embedding module is designed to perform feature grouping for reducing the memory requirement and computational complexity, and feature interaction for enhancing the representational ability of the learned speaker embedding. In the proposed embedding module, audio feature of each speech sample is split into several low-dimensional feature subsets that are transformed by a recurrent convolutional block in parallel. Then, the operations of averaging, addition, concatenation, element-wise summation and statistics pooling are sequentially executed to learn a speaker embedding for each speech sample. The recurrent convolutional block consists of a block of bidirectional long short-term memory, and a block of de-redundancy convolution in which feature grouping and interaction are conducted too. Our method is compared to baseline methods on three datasets that are selected from three public speech corpora (VoxCeleb1, VoxCeleb2, and LibriSpeech). The results show that our method obtains higher accuracy under several conditions, and has advantages over all baseline methods in computational complexity and model size.
Few-shot Class-incremental Audio Classification Using Dynamically Expanded Classifier with Self-attention Modified Prototypes
Authors: Yanxiong Li, Wenchang Cao, Wei Xie, Jialong Li, Emmanouil Benetos
Date: 2023-05-31 00:00:00
Tasks: Audio Classification, Classification

Most existing methods for audio classification assume that the vocabulary of audio classes to be classified is fixed. When novel (unseen) audio classes appear, audio classification systems need to be retrained with abundant labeled samples of all audio classes for recognizing base (initial) and novel audio classes. If novel audio classes continue to appear, the existing methods for audio classification will be inefficient and even infeasible. In this work, we propose a method for few-shot class-incremental audio classification, which can continually recognize novel audio classes without forgetting old ones. The framework of our method mainly consists of two parts: an embedding extractor and a classifier, and their constructions are decoupled. The embedding extractor is the backbone of a ResNet based network, which is frozen after construction by a training strategy using only samples of base audio classes. However, the classifier consisting of prototypes is expanded by a prototype adaptation network with few samples of novel audio classes in incremental sessions. Labeled support samples and unlabeled query samples are used to train the prototype adaptation network and update the classifier, since they are informative for audio classification. Three audio datasets, named NSynth-100, FSC-89 and LS-100 are built by choosing samples from audio corpora of NSynth, FSD-MIX-CLIP and LibriSpeech, respectively. Results show that our method exceeds baseline methods in average accuracy and performance dropping rate. In addition, it is competitive compared to baseline methods in computational complexity and memory requirement. The code for our method is given at https://github.com/vinceasvp/FCAC.
MERLIon CCS Challenge Evaluation Plan
Authors: Leibny Paola Garcia Perera, Y. H. Victoria Chua, Hexin Liu, Fei Ting Woon, Andy W. H. Khong, Justin Dauwels, Sanjeev Khudanpur, Suzy J. Styles
Date: 2023-05-31 00:00:00
Tasks: Language Identification, Task 2

This paper introduces the inaugural Multilingual Everyday Recordings- Language Identification on Code-Switched Child-Directed Speech (MERLIon CCS) Challenge, focused on developing robust language identification and language diarization systems that are reliable for non-standard, accented, spontaneous code-switched, child-directed speech collected via Zoom. Aligning closely with Interspeech 2023 theme, the main objectives of this inaugural challenge are to present a unique first-of-its-kind Zoom videocall dataset featuring English-Mandarin spontaneous code-switched child-directed speech, benchmark the current and novel language identification and language diarization systems in a code-switching scenario including extremely short utterances, and test the robustness of such systems under accented speech. The MERLIon CCS challenge features two task: language identification (Task 1) and language diarization (Task 2). Two tracks, open and closed, are available for each task, differing by the volume of data systems can be trained on. This paper describes the dataset, dataset annotation protocol, challenge tasks, open and closed tracks, evaluation metrics, and evaluation protocol.
Exploiting Mechanics-Based Priors for Lateral Displacement Estimation in Ultrasound Elastography
Authors: Md Ashikuzzaman, Ali K. Z. Tehrani, Hassan Rivaz
Date: 2023-05-31 00:00:00

Tracking the displacement between the pre- and post-deformed radio-frequency (RF) frames is a pivotal step of ultrasound elastography, which depicts tissue mechanical properties to identify pathologies. Due to ultrasound's poor ability to capture information pertaining to the lateral direction, the existing displacement estimation techniques fail to generate an accurate lateral displacement or strain map. The attempts made in the literature to mitigate this well-known issue suffer from one of the following limitations: 1) Sampling size is substantially increased, rendering the method computationally and memory expensive. 2) The lateral displacement estimation entirely depends on the axial one, ignoring data fidelity and creating large errors. This paper proposes exploiting the effective Poisson's ratio (EPR)-based mechanical correspondence between the axial and lateral strains along with the RF data fidelity and displacement continuity to improve the lateral displacement and strain estimation accuracies. We call our techniques MechSOUL (Mechanically-constrained Second-Order Ultrasound eLastography) and L1-MechSOUL (L1-norm-based MechSOUL), which optimize L2- and L1-norm-based penalty functions, respectively. Extensive validation experiments with simulated, phantom, and in vivo datasets demonstrate that MechSOUL and L1-MechSOUL's lateral strain and EPR estimation abilities are substantially superior to those of the recently-published elastography techniques. We have published the MATLAB codes of MechSOUL and L1-MechSOUL at http://code.sonography.ai.
Asymptotic Performance Analysis of Large-Scale Active IRS-Aided Wireless Network
Authors: Yan Wang, Feng Shu, Zhihong Zhuang, Rongen Dong, Qi Zhang, Di wu, Liang Yang, Jiangzhou Wang
Date: 2023-05-31 00:00:00
Tasks: Quantization

In this paper, the dominant factor affecting the performance of active intelligent reflecting surface (IRS) aided wireless communication networks in Rayleigh fading channel, namely the average signal-to-noise ratio (SNR) $\gamma_0$ at IRS, is studied. Making use of the weak law of large numbers, its simple asymptotic expression is derived as the number $N$ of IRS elements goes to medium-scale and large-scale. When $N$ tends to large-scale, the asymptotic received SNR at user is proved to be a linear increasing function of a product of $\gamma_0$ and $N$. Subsequently, when the BS transmit power is fixed, there exists an optimal limited reflective power at IRS. At this point, more IRS reflect power will degrade the SNR performance. Additionally, under the total power sum constraint of the BS transmit power and the power reflected by the IRS, an optimal power allocation (PA) strategy is derived and shown to achieve 0.83 bit rate gain over equal PA. Finally, an IRS with finite phase shifters being taken into account, generates phase quantization errors, and further leads to a degradation of receive performance. The corresponding closed-form performance loss expressions for user's asymptotic SNR, achievable rate (AR), and bit error rate (BER) are derived for active IRS. Numerical simulation results show that a 3-bit discrete phase shifter is required to achieve a trivial performance loss for a large-scale active IRS.
Verifying the Smoothness of Graph Signals: A Graph Signal Processing Approach
Authors: Lital Dabush, Tirza Routtenberg
Date: 2023-05-31 00:00:00
Tasks: Graph Neural Network

Graph signal processing (GSP) deals with the representation, analysis, and processing of structured data, i.e. graph signals that are defined on the vertex set of a generic graph. A crucial prerequisite for applying various GSP and graph neural network (GNN) approaches is that the examined signals are smooth graph signals with respect to the underlying graph, or, equivalently, have low graph total variation (TV). In this paper, we develop GSP-based approaches to verify the validity of the smoothness assumption of given signals (data) and an associated graph. The proposed approaches are based on the representation of network data as the output of a graph filter with a given graph topology. In particular, we develop two smoothness detectors for the graph-filter-output model: 1) the likelihood ratio test (LRT) for known model parameters; and 2) a semi-parametric detector that estimates the graph filter and then validates its smoothness. The properties of the proposed GSP-based detectors are investigated, and some special cases are discussed. The performance of the GSP-based detectors is evaluated using synthetic data, data from the IEEE 14-bus power system, and measurements from a network of light intensity sensors, under different setups. The results demonstrate the effectiveness of the proposed approach and its robustness to different generating models, noise levels, and number of samples.
Spatial IDFT for Squint-Free Massive Arrays
Authors: Hesham Beshary, Ali Niknejad
Date: 2023-05-31 00:00:00

This paper presents a novel technique to build squint-free massive phased arrays. This is accomplished by explicitly implementing a spatial IDFT to cancel out the DFT imposed by the array nature which causes beam squint. In addition, the paper analyzes the beam-squint issue, which arises from two mechanisms: the coherent bandwidth limitations and the systematic delay spread in the array. These mechanisms reduce the signal-to-noise ratio and cause inter-symbol interference. This work also highlights the importance of utilizing OFDM modulation to enhance signal quality by mitigating the self-interference issue. A numerical solver is used to simulate and verify the IDFT squint-free implementation and to estimate the signal quality limitations in massive arrays.