pwc-archive/papers-with-abstracts huggingface dataset:
Method of detecting the defects of the end face of steel coils
Authors: Liting Zhu, Kinbo Chen, Jinjun Rao, Mei Liu
Date: 2019-10-16 00:00:00
Proceeding: Seventh International Conference on Optical and Photonic Engineering (icOPEN 2019), Phuket, Thailand 2019 10
Tasks: Edge Detection, Image Segmentation, Semantic Segmentation

Due to the excessively dense steel coil layers on the end face of the rolled coil, it is difficult to extract the defect area of the end face of rolled steel by the image segmentation methods such as edge detection and threshold segmentation. Aiming at the dense texture features of the end face of rolled steel coil, a method of detecting the defects of the end face of steel coils was proposed. Based on the theoretical technology of machine vision, this paper proposed a double threshold method to extract potential defect area and eliminate the background area and the completely defect-free area. In the double threshold method, we utilize the Canny operator and the PPHT (Progressive Probabilistic Hough Transform) to adjust the direction of the image block on the end face of the steel coils, makes the texture direction on each image block consistent. Then, Gaussian steerable filter, followed by second Canny and PPHT, was applied to enhance image. After the double threshold method, projective integral of digital image was utilized to extract the feature of the potential defects area. Finally, the SVM (Support Vector Machine) is applied to determine the type of the defect. The results show that the method of detecting the defects of the end face of steel coils can accurately and quickly detect defects even on the end face image of the steel coils with dense layers.
A Finite-State Fixed-Corridor Model for UAS Traffic Management
Authors: Hamid Emadi, Ella Atkins, Hossein Rastgoftar
Date: 2022-04-12 00:00:00
Tasks: Management

This paper proposes a physics-inspired solution for low altitude Unmanned Aircraft System (UAS) Traffic Management (UTM) in urban areas. We decompose UTM into spatial and temporal planning problems. For the spatial planning problem, we use the principles of Eulerian continuum mechanics to safely and optimally allocate finite airspace to a UAS. To this end, the finite airspace is partitioned into planned and unplanned subspaces with unplanned subspace(s) or zone(s) enclosing buildings and restricted no-fly regions. The planned subspace is divided into navigable channels that safely wrap unplanned zone(s). We model the airspace planning problem as a Markov Decision Process (MDP) with states defined based on spatial and temporal airspace features and actions authorizing transitions between safe navigable channels. We apply the proposed traffic management solution to plan safe coordination of small UAS in the airspace above downtown Tucson, Arizona.
VoiceFixer: A Unified Framework for High-Fidelity Speech Restoration
Authors: Haohe Liu, Xubo Liu, Qiuqiang Kong, Qiao Tian, Yan Zhao, DeLiang Wang, Chuanzeng Huang, Yuxuan Wang
Date: 2022-04-12 00:00:00
Tasks: Speech Denoising, Speech Enhancement, Vocal Bursts Intensity Prediction

Speech restoration aims to remove distortions in speech signals. Prior methods mainly focus on a single type of distortion, such as speech denoising or dereverberation. However, speech signals can be degraded by several different distortions simultaneously in the real world. It is thus important to extend speech restoration models to deal with multiple distortions. In this paper, we introduce VoiceFixer, a unified framework for high-fidelity speech restoration. VoiceFixer restores speech from multiple distortions (e.g., noise, reverberation, and clipping) and can expand degraded speech (e.g., noisy speech) with a low bandwidth to 44.1 kHz full-bandwidth high-fidelity speech. We design VoiceFixer based on (1) an analysis stage that predicts intermediate-level features from the degraded speech, and (2) a synthesis stage that generates waveform using a neural vocoder. Both objective and subjective evaluations show that VoiceFixer is effective on severely degraded speech, such as real-world historical speech recordings. Samples of VoiceFixer are available at https://haoheliu.github.io/voicefixer.
Text-Driven Separation of Arbitrary Sounds
Authors: Kevin Kilgour, Beat Gfeller, Qingqing Huang, Aren Jansen, Scott Wisdom, Marco Tagliasacchi
Date: 2022-04-12 00:00:00

We propose a method of separating a desired sound source from a single-channel mixture, based on either a textual description or a short audio sample of the target source. This is achieved by combining two distinct models. The first model, SoundWords, is trained to jointly embed both an audio clip and its textual description to the same embedding in a shared representation. The second model, SoundFilter, takes a mixed source audio clip as an input and separates it based on a conditioning vector from the shared text-audio representation defined by SoundWords, making the model agnostic to the conditioning modality. Evaluating on multiple datasets, we show that our approach can achieve an SI-SDR of 9.1 dB for mixtures of two arbitrary sounds when conditioned on text and 10.1 dB when conditioned on audio. We also show that SoundWords is effective at learning co-embeddings and that our multi-modal training approach improves the performance of SoundFilter.
Low Latency Time Domain Multichannel Speech and Music Source Separation
Authors: Gerald Schuller
Date: 2022-04-12 00:00:00
Tasks: Music Source Separation

The Goal is to obtain a simple multichannel source separation with very low latency. Applications can be teleconferencing, hearing aids, augmented reality, or selective active noise cancellation. These real time applications need a very low latency, usually less than about 6 ms, and low complexity, because they usually run on small portable devices. For that we don't need the best separation, but "useful" separation, and not just on speech, but also music and noise. Usual frequency domain approaches have higher latency and complexity. Hence we introduce a novel probabilistic optimization method which we call "Random Directions", which can overcome local minima, applied to a simple time domain unmixing structure, and which is scalable for low complexity. Then it is compared to frequency domain approaches on separating speech and music sources, and using 3D microphone setups.
A Wav2vec2-Based Experimental Study on Self-Supervised Learning Methods to Improve Child Speech Recognition
Authors: Rishabh Jain, Andrei Barcovschi, Mariam Yiwere, Dan Bigioi, Peter Corcoran, Horia Cucu
Date: 2022-04-06 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Self-Supervised Learning, speech-recognition, Speech Recognition

Despite recent advancements in deep learning technologies, Child Speech Recognition remains a challenging task. Current Automatic Speech Recognition (ASR) models require substantial amounts of annotated data for training, which is scarce. In this work, we explore using the ASR model, wav2vec2, with different pretraining and finetuning configurations for self-supervised learning (SSL) toward improving automatic child speech recognition. The pretrained wav2vec2 models were finetuned using different amounts of child speech training data, adult speech data, and a combination of both, to discover the optimum amount of data required to finetune the model for the task of child ASR. Our trained model achieves the best Word Error Rate (WER) of 7.42 on the MyST child speech dataset, 2.99 on the PFSTAR dataset and 12.47 on the CMU KIDS dataset as compared to any other previous methods. Our models outperformed the wav2vec2 BASE 960 on child speech which is considered a state-of-the-art ASR model on adult speech by just using 10 hours of child speech data in finetuning. The analysis of different types of training data and their effect on inference is also provided by using a combination of datasets in pretraining, finetuning and inference.
Safety in Augmented Importance Sampling: Performance Bounds for Robust MPPI
Authors: Manan Gandhi, Hassan Almubarak, Yuichiro Aoyama, Evangelos Theodorou
Date: 2022-04-12 00:00:00
Tasks: Model Predictive Control, Motion Planning

This work explores the nature of augmented importance sampling in safety-constrained model predictive control problems. When operating in a constrained environment, sampling based model predictive control and motion planning typically utilizes penalty functions or expensive optimization based control barrier algorithms to maintain feasibility of forward sampling. In contrast the presented algorithm utilizes discrete embedded barrier states in augmented importance sampling to apply feedback with respect to a nominal state when sampling. We will demonstrate that this approach of safety of discrete embedded barrier states in augmented importance sampling is more sample efficient by metric of collision free trajectories, is computationally feasible to perform per sample, and results in better safety performance on a cluttered navigation task with extreme un-modeled disturbances. In addition, we will utilize the theoretical properties of augmented importance sampling and safety control to derive a new bound on the free energy of the system.
Analytical Uncertainty Propagation for Multi-Period Stochastic Optimal Power Flow
Authors: Rebecca Bauer, Tillmann Mühlpfordt, Nicole Ludwig, Veit Hagenmeyer
Date: 2022-04-12 00:00:00

The increase in renewable energy sources (RESs), like wind or solar power, results in growing uncertainty also in transmission grids. This affects grid stability through fluctuating energy supply and an increased probability of overloaded lines. One key strategy to cope with this uncertainty is the use of distributed energy storage systems (ESSs). In order to securely operate power systems containing renewables and use storage, optimization models are needed that both handle uncertainty and apply ESSs. This paper introduces a compact dynamic stochastic chance-constrained optimal power flow (CC-OPF) model, that minimizes generation costs and includes distributed ESSs. Assuming Gaussian uncertainty, we use affine policies to obtain a tractable, analytically exact reformulation as a second-order cone problem (SOCP). We test the new model on five different IEEE networks with varying sizes of 5, 39, 57, 118 and 300 nodes and include complexity analysis. The results show that the model is computationally efficient and robust with respect to constraint violation risk. The distributed energy storage system leads to more stable operation with flattened generation profiles. Storage absorbed RES uncertainty, and reduced generation cost.
Fault Detection and Localization in Active Distribution Networks using Optimally Placed Phasor Measurements Units
Authors: Francesco Conte, Fabio D'Agostino, Bruno Gabriele, Giacomo-Piero Schiapparelli, Federico Silvestro
Date: 2022-04-12 00:00:00
Tasks: Fault Detection, Fault localization

This paper introduces an algorithm able to detect and localize the occurrance of a fault in an Active Distribution Network, using the measurements collected by Phasor Measurement Units (PMUs). First, a basic algorithm that works under the assumption that all grid buses are equipped with a PMU is designed. Then, formal observability conditions that allow detection and localization with a reduced number of PMUs are provided. Based on these conditions, the algorithm is extended to perform correctly when not all network buses are monitored. Moreover, an Optimal Positioning Algorithm, always based on the observability conditions, is designed. This algorithm allows the user to customize the fault localization resolution. The approach is validated through simulations carried out on a benchmark active distribution network.
Hybrid Simulation-based Resource Planning and Constructability Analysis of RCC Pavement Projects
Authors: Mohammad Shakerian, Mohammad Sadra Rajabi, Mohammad Tajik, Hosein Taghaddos
Date: 2022-04-12 00:00:00

One of the critical challenges in infrastructural constructions is designing and planning operations and their related resources. The complex interlinked composition of different factors and variables affecting resource productivity has made simulation a powerful approach for operational planning. The construction sector has recently seen a notable surge in applying various simulation tools to enhance further the quality of projects planning, particularly in large-scale infrastructure developments (e.g., highway construction). Due to possible cost overruns in improper resource allocation, optimizing the design and construction planning stages of megaprojects such as massive pavement projects is essential. Recent studies aimed to build a simulation-based strategy in construction designing and planning by combining various simulation approaches (e.g., discrete-event simulation, system dynamics, agent-based simulation, and hybrid simulation) to enhance the planning phase. This paper introduces an evolving real-time hybrid simulation technique regarding the projects intrinsic time-varying inputs and factors to optimize the planning of Roller Compacted Concrete (RCC) pavement projects. Several scenarios are investigated using various resource combinations to achieve the best execution method for delivering concrete to the project. An actual highway project case study validates the proposed model and its application for future projects. This studys findings exhibit the proficiencies of the simulation-based approach in resource planning of RCC pavement projects within the time and cost constraints and their related regulations.
Towards Optimal Kron-based Reduction Of Networks (Opti-KRON) for the Electric Power Grid
Authors: Samuel Chevalier, Mads R. Almassalkhi
Date: 2022-04-12 00:00:00

For fast timescales or long prediction horizons, the AC optimal power flow (OPF) problem becomes a computational challenge for large-scale, realistic AC networks. To overcome this challenge, this paper presents a novel network reduction methodology that leverages an efficient mixed-integer linear programming (MILP) formulation of a Kron-based reduction that is optimal in the sense that it balances the degree of the reduction with resulting modeling errors in the reduced network. The method takes as inputs the full AC network and a pre-computed library of AC load flow data and uses the graph Laplacian to constraint nodal reductions to only be feasible for neighbors of non-reduced nodes. This results in a highly effective MILP formulation which is embedded within an iterative scheme to successively improve the Kron-based network reduction until convergence. The resulting optimal network reduction is, thus, grounded in the physics of the full network. The accuracy of the network reduction methodology is then explored for a 100+ node medium-voltage radial distribution feeder example across a wide range of operating conditions. It is finally shown that a network reduction of 25-85% can be achieved within seconds and with worst-case voltage magnitude deviation errors within any super node cluster of less than 0.01pu. These results illustrate that the proposed optimization-based approach to Kron reduction of networks is viable for larger networks and suitable for use within various power system applications.
OptimizedDP: An Efficient, User-friendly Library For Optimal Control and Dynamic Programming
Authors: Minh Bui, George Giovanis, Mo Chen, Arrvindh Shriraman
Date: 2022-04-12 00:00:00

This paper introduces OptimizedDP, a high-performance software library that solves time-dependent Hamilton-Jacobi partial differential equation (PDE), computes backward reachable sets with application in robotics, and contains value iterations algorithm implementation for continuous action-state space Markov Decision Process (MDP) while leveraging user-friendliness of Python for different problem specifications without sacrificing efficiency of the core computation. These algorithms are all based on dynamic programming, and hence can both be challenging to implement and have bad execution runtime due to the large high-dimensional tabular arrays. Although there are existing toolboxes for level set methods that are used to solve the HJ PDE, our toolbox makes solving the PDE at higher dimensions possible as well as having an order of magnitude improvement in execution times compared to other toolboxes while keeping the interface easy to specify different dynamical systems description. Our toolbox is available online at https://github.com/SFU-MARS/optimized_dp.
Active power control of wind farms: an instantaneous approach on waked conditions
Authors: Jean Gonzalez Silva, Bart Matthijs Doekemeijer, Riccardo Ferrari, Jan-Willem van Wingerden
Date: 2022-04-11 00:00:00

This paper presents a closed-loop controller for wind farms to provide active power control services using a high-fidelity computational fluid dynamics based wind plant simulator. The proposed design enhances power tracking stability and allows for simple understanding, where each turbine is considered as a pure time-delay system. The paper investigates the control performance with different nominal power distributions in a fully waked condition and limited power availability. Results demonstrate the improvement in power production obtained by closing the control loop, compared to greedy operation. Additionally, power tracking capabilities are enhanced with a nominal power distribution favored by axial-induction, as well as the occurrence of turbine saturation and the distribution of loads.
A Switching Thrust Tracking Controller for Load Constrained Wind Turbines
Authors: Jean Gonzalez Silva, Daan van der Hoek, Sebastiaan Paul Mulders, Riccardo Ferrari, Jan-Willem van Wingerden
Date: 2022-04-11 00:00:00

Wind turbines are prone to structural degradation, particularly in offshore locations. Based on the structural health condition of the tower, power de-rating strategies can be used to reduce structural loads at the cost of power losses.This paper introduces a novel closed-loop switching control architecture to constrain the thrust in individual turbines. By taking inspiration from developments in the field of reference governors, an existing demanded power tracking controller is extended by a thrust tracking controller. The latter is activated only when a user-defined constraint on fore-aft thrust force is exceeded, which can be set based on the actual damage status of the turbine. Having a down-regulation with monotonic aerodynamic load response, a simple linear thrust tracking controller is proposed. Such a scheme can reduce aerodynamic loads while incurring acceptable losses on power production which, in a wind farm setting, can be compensated for by other turbines. Large eddy simulations demonstrate the performance of the proposed scheme on satisfying thrust constraints.
A Concurrent Switching Model for Traffic Congestion Control
Authors: Hossein Rastgoftar, Xun Liu, Jean-Baptiste Jeannin
Date: 2022-04-11 00:00:00
Tasks: model

We introduce a new conservation-based approach for traffic coordination modeling and control in a network of interconnected roads (NOIR) with switching movement phase rotations at every NOIR junction. For modeling of traffic evolution, we first assume that the movement phase rotation is cyclic at every NOIR junction, but the duration of each movement phase can be arbitrarily commanded by traffic signals. Then, we propose a novel concurrent switching dynamics (CSD) with deterministic transitions among a finite number of states, representing the NOIR movement phases. We define the CSD control as a cyclic receding horizon optimization problem with periodic quadratic cost and constraints. More specifically, the cost is defined so that the traffic density is minimized and the boundary inflow is uniformly distributed over the boundary inlet roads, whereas the cost parameters are periodically changed with time. The constraints are linear and imposed by a trapezoidal fundamental diagram at every NOIR road so that traffic feasibility is assured and traffic over-saturation is avoided. The success of the proposed traffic boundary control is demonstrated by simulation of traffic congestion control in Downtown Phoenix.
Stochastic Multi-armed Bandits with Non-stationary Rewards Generated by a Linear Dynamical System
Authors: Jonathan Gornet, Mehdi Hosseinzadeh, Bruno Sinopoli
Date: 2022-04-06 00:00:00
Tasks: Decision Making, Multi-Armed Bandits

The stochastic multi-armed bandit has provided a framework for studying decision-making in unknown environments. We propose a variant of the stochastic multi-armed bandit where the rewards are sampled from a stochastic linear dynamical system. The proposed strategy for this stochastic multi-armed bandit variant is to learn a model of the dynamical system while choosing the optimal action based on the learned model. Motivated by mathematical finance areas such as Intertemporal Capital Asset Pricing Model proposed by Merton and Stochastic Portfolio Theory proposed by Fernholz that both model asset returns with stochastic differential equations, this strategy is applied to quantitative finance as a high-frequency trading strategy, where the goal is to maximize returns within a time period.
A Machine Learning and Computer Vision Approach to Geomagnetic Storm Forecasting
Authors: Kyle Domico, Ryan Sheatsley, Yohan Beugin, Quinn Burke, Patrick McDaniel
Date: 2022-04-04 00:00:00
Tasks: BIG-bench Machine Learning

Geomagnetic storms, disturbances of Earth's magnetosphere caused by masses of charged particles being emitted from the Sun, are an uncontrollable threat to modern technology. Notably, they have the potential to damage satellites and cause instability in power grids on Earth, among other disasters. They result from high sun activity, which are induced from cool areas on the Sun known as sunspots. Forecasting the storms to prevent disasters requires an understanding of how and when they will occur. However, current prediction methods at the National Oceanic and Atmospheric Administration (NOAA) are limited in that they depend on expensive solar wind spacecraft and a global-scale magnetometer sensor network. In this paper, we introduce a novel machine learning and computer vision approach to accurately forecast geomagnetic storms without the need of such costly physical measurements. Our approach extracts features from images of the Sun to establish correlations between sunspots and geomagnetic storm classification and is competitive with NOAA's predictions. Indeed, our prediction achieves a 76% storm classification accuracy. This paper serves as an existence proof that machine learning and computer vision techniques provide an effective means for augmenting and improving existing geomagnetic storm forecasting methods.
Retrieval from Mixed Sampling Frequency: Generic Identifiability in the Unit Root VAR
Authors: Philipp Gersing, Leopold Soegner, Manfred Deistler
Date: 2022-04-12 00:00:00
Tasks: Blocking, Retrieval

The "REtrieval from MIxed Sampling" (REMIS) approach based on blocking developed in Anderson et al. (2016a) is concerned with retrieving an underlying high frequency model from mixed frequency observations. In this paper we investigate parameter-identifiability in the Johansen (1995) vector error correction model for mixed frequency data. We prove that from the second moments of the blocked process after taking differences at lag N (N is the slow sampling rate), the parameters of the high frequency system are generically identified. We treat the stock and the flow case as well as deterministic terms.