pwc-archive/papers-with-abstracts huggingface dataset:
Analysis on the Influence of Synchronization Error on Fixed-filter Active Noise Control
Authors: Guo Yu
Date: 2023-10-06 00:00:00

The efficacy of active noise control technology in mitigating urban noise, particularly in relation to low-frequency components, has been well-established. In the realm of traditional academic research, adaptive algorithms, such as the filtered reference least mean square method, are extensively employed to achieve real-time noise reduction in many applications. Nevertheless, the utilization of this technology in commercial goods is often hindered by its significant computing complexity and inherent instability. In this particular scenario, the adoption of the fixed-filter strategy emerges as a viable alternative for addressing these challenges, albeit with a potential trade-off in terms of noise reduction efficacy. This work aims to conduct a theoretical investigation into the synchronization error of the digital Active Noise Control (ANC) system. Keywords: Fixed-filter, Active noise control, Multichannel active noise control.
Challenges and Insights: Exploring 3D Spatial Features and Complex Networks on the MISP Dataset
Authors: Yiwen Shao
Date: 2023-10-05 00:00:00
Tasks: speech-recognition, Speech Recognition

Multi-channel multi-talker speech recognition presents formidable challenges in the realm of speech processing, marked by issues such as background noise, reverberation, and overlapping speech. Overcoming these complexities requires leveraging contextual cues to separate target speech from a cacophonous mix, enabling accurate recognition. Among these cues, the 3D spatial feature has emerged as a cutting-edge solution, particularly when equipped with spatial information about the target speaker. Its exceptional ability to discern the target speaker within mixed audio, often rendering intermediate processing redundant, paves the way for the direct training of "All-in-one" ASR models. These models have demonstrated commendable performance on both simulated and real-world data. In this paper, we extend this approach to the MISP dataset to further validate its efficacy. We delve into the challenges encountered and insights gained when applying 3D spatial features to MISP, while also exploring preliminary experiments involving the replacement of these features with more complex input and models.
Audio Event-Relational Graph Representation Learning for Acoustic Scene Classification
Authors: Yuanbo Hou, Siyang Song, Chuang Yu, Wenwu Wang, Dick Botteldooren
Date: 2023-10-05 00:00:00
Tasks: Acoustic Scene Classification, Graph Representation Learning, Representation Learning, Scene Classification

Most deep learning-based acoustic scene classification (ASC) approaches identify scenes based on acoustic features converted from audio clips containing mixed information entangled by polyphonic audio events (AEs). However, these approaches have difficulties in explaining what cues they use to identify scenes. This paper conducts the first study on disclosing the relationship between real-life acoustic scenes and semantic embeddings from the most relevant AEs. Specifically, we propose an event-relational graph representation learning (ERGL) framework for ASC to classify scenes, and simultaneously answer clearly and straightly which cues are used in classifying. In the event-relational graph, embeddings of each event are treated as nodes, while relationship cues derived from each pair of nodes are described by multi-dimensional edge features. Experiments on a real-life ASC dataset show that the proposed ERGL achieves competitive performance on ASC by learning embeddings of only a limited number of AEs. The results show the feasibility of recognizing diverse acoustic scenes based on the audio event-relational graph. Visualizations of graph representations learned by ERGL are available here (https://github.com/Yuanbo2020/ERGL).
A Finite Element Method Approach for Trajectory Generation via Time-Optimal Control and Model Predictive Control Tracking
Authors: Jose A. Solano-Castellanos
Date: 2023-10-06 00:00:00
Tasks: Model Predictive Control

In this paper a framework for solving the time optimal control (TOC) using Galerkin's Weighted Residuals Method (GWRM) and Sequential Convex Programming (SCP) is proposed. The proposed method solves the two-point boundary value problem, avoiding the use of shooting methods that rely heavily on the appropriate initialization of the adjoint state and optimal time. Since TOC yields an open-loop controller, a Model Predictive Control (MPC) scheme is employed to track both the optimal trajectory and controller, allowing the system to reject disturbances. The approach is validated using the Dubins' car dynamics for optimal time trajectory generation.
Capturing Chronology and Extreme Values of Representative Days for Planning of Transmission Lines and Long-Term Energy Storage Systems
Authors: Mojtaba Moradi-Sepahvand, Simon H. Tindemans
Date: 2023-10-06 00:00:00
Tasks: Time Series

The growing penetration of renewable energy sources (RESs) is inevitable to reach net zero emissions. In this regard, optimal planning and operation of power systems are becoming more critical due to the need for modeling the short-term variability of RES output power and load demand. Considering hourly time steps of one or more years to model the operational details in a long-term expansion planning scheme can lead to a practically unsolvable model. Therefore, a clustering-based hybrid time series aggregation algorithm is proposed in this paper to capture both extreme values and temporal dynamics of input data by some extracted representatives. The proposed method is examined in a complex co-planning model for transmission lines, wind power plants (WPPs), short-term battery and long-term pumped hydroelectric energy storage systems. The effectiveness of proposed mixed-integer linear programming (MILP) model is evaluated using a modified 6-bus Garver test system. The simulation results confirm the proposed model efficacy, especially in modeling long-term energy storage systems.
Representative Days and Hours with Piecewise Linear Transitions for Power System Planning
Authors: Mojtaba Moradi-Sepahvand, Simon H. Tindemans
Date: 2023-10-06 00:00:00

Electric demand and renewable power are highly variable, and the solution of a planning model relies on capturing this variability. This paper proposes a hybrid multi-area method that effectively captures both the intraday and interday chronology of real data considering extreme values, using a limited number of representative days, and time points within each day. An optimization-based representative extraction method is proposed to improve intraday chronology capturing. It ensures higher precision in preserving data chronology and extreme values than hierarchical clustering methods. The proposed method is based on a piecewise linear demand and supply representation, which reduces approximation errors compared to the traditional piecewise constant formulation. Additionally, sequentially linked day blocks with identical representatives, created through a mapping process, are employed for interday chronology capturing. To evaluate the efficiency of the proposed method, a comprehensive expansion co-planning model is developed, including transmission lines, energy storage systems, and wind farms.
Sparse Identification of Nonlinear Dynamics with Side Information (SINDy-SI)
Authors: Gabriel F. Machado, Morgan Jones
Date: 2023-10-06 00:00:00

Modern societies have an abundance of data yet good system models are rare. Unfortunately, many of the current system identification and machine learning techniques fail to generalize outside of the training set, producing models that violate basic physical laws. This work proposes a novel method for the Sparse Identification of Nonlinear Dynamics with Side Information (SINDy-SI). SINDy-SI is an iterative method that uses Sum-of-Squares (SOS) programming to learn optimally fitted models while guaranteeing that the learned model satisfies side information, such as symmetry's and physical laws. Guided by the principle of Occam's razor, that the simplest or most regularized best fitted model is typically the superior choice, during each iteration SINDy-SI prunes the basis functions associated with small coefficients, yielding a sparse dynamical model upon termination. Through several numerical experiments we will show how the combination of side information constraints and sparse polynomial representation cultivates dynamical models that obey known physical laws while displaying impressive generalized performance beyond the training set.
Graph Neural Networks for Fast Contingency Analysis of Power Systems
Authors: Agnes M. Nakiganda, Spyros Chatzivasileiadis
Date: 2023-10-06 00:00:00
Tasks: Graph Neural Network

The successful integration of machine learning models into decision support tools for grid operation hinges on effectively capturing the topological changes in daily operations. Frequent grid reconfigurations and N-k security analyses have to be conducted to ensure a reliable and secure power grid, leading to a vast combinatorial space of possible topologies and operating states. This combinatorial complexity, which increases with grid size, poses a significant computational challenge for traditional solvers. In this paper, we combine Physics-Informed Neural Networks with graph-aware neural network architectures, i.e., a Guided-Dropout (GD) and an Edge-Varying Graph Neural Network (GNN) architecture to learn the set points for a grid that considers all probable single-line reconfigurations (all critical N-1 scenarios) and subsequently apply the trained models to N-k scenarios. We demonstrate how incorporating the underlying physical equations for the network equations within the training procedure of the GD and the GNN architectures performs with N-1, N-2, and N-3 case studies. Using the AC Power Flow as a guiding application, we test our methods on the 6-bus, 24-bus, 57-bus, and 118-bus systems. We find that GNN not only achieves the task of contingency screening with satisfactory accuracy but does this up to 400 times faster than the Newton-Raphson power flow solver. Moreover, our results provide a comparison of the GD and GNN models in terms of accuracy and computational speed and provide recommendations on their adoption for contingency analysis of power systems.
Robust Safety for Mixed-Autonomy Traffic with Delays and Disturbances
Authors: Chenguang Zhao, Huan Yu
Date: 2023-10-06 00:00:00

Various control strategies and field experiments have been designed for connected and automated vehicles (CAVs) to stabilize mixed traffic that contains both CAVs and Human-driven Vehicles (HVs). The effect of these stabilizing CAV control strategies on traffic safety is still under investigation. In an effort to prioritize safety over stability, a safety-critical filter via control barrier functions (CBFs) can be designed by modifying the stabilizing nominal control input in a minimal fashion and imparting collision-free driving behaviors for CAVs and HVs. However, such formal safety guarantees can be violated if there are delays in the actuation and communication channels of the CAV. Considering both actuator and sensor delays, and disturbances, we propose robust safety-critical traffic control (RSTC) design to ensure ``robust safety'' of the mixed traffic. While predictor-based CBF has been developed to compensate for the actuator delay, uncertain speed disturbances from the head vehicle cause prediction error and require novel robust CBF design. Besides, safety-critical control with sensor delay also remains an open question. In RSTC, a state predictor with bounded error is designed, and robust CBF constraints are constructed to guarantee safety under actuator delay and disturbances. When there is a sensor delay, a state observer is designed and integrated with a predictor-based CBF to ensure robust safety. Numerical simulations demonstrate that the proposed RSTC avoids rear-end collisions for two unsafe traffic scenarios in the presence of actuator, sensor delays and disturbances.
Analysis of Age of Information in Non-terrestrial Networks
Authors: Yanwu Lu, Howard Yang, Nikolaos Pappas, Giovanni Geraci, Chuan Ma, Tony Q. S. Quek
Date: 2023-10-06 00:00:00

Non-terrestrial networks (NTN), particularly low Earth orbit (LEO) satellite networks, have emerged as a promising solution to overcome the limitations of traditional terrestrial networks in the context of next-generation (6G) wireless systems. In this paper, we focus on analyzing the timeliness of information delivery in NTN through the concept of Age of Information (AoI). We propose an on-off process to approximate the service process between LEO satellites and a source node located on the Earth's surface. By utilizing stochastic geometry, we derive a closed-form expression for the time-average AoI in an NTN. This expression also applies to on-off processes with one component following an exponential distribution while the other has its probability density function supported on a bounded interval. Numerical results validate the accuracy of our analysis and demonstrate the impact of source status update rate and satellite constellation density on the time-average AoI. Our work fills a gap in the literature by providing a comprehensive analysis of AoI in NTN and offers new insights into the performance of LEO satellite networks.
Surrogate-based Real-time Curbside Management for Ride-hailing and Delivery Operations
Authors: Suyash C. Vishnoi, Michele D. Simoni
Date: 2023-10-05 00:00:00
Tasks: Management, Model Predictive Control, regression

The present work investigates surrogate model-based optimization for real-time curbside traffic management operations. An optimization problem is formulated to minimize the congestion on roadway segments caused by vehicles stopping on the segment (e.g., ride-hailing or delivery operations) and implemented in a model predictive control framework. A hybrid simulation approach where main traffic flows interact with individually modeled stopping vehicles is adopted. Due to its non-linearity, the optimization problem is coupled with a meta-heuristic. However, because simulations are time expensive and hence unsuitable for real-time control, a trained surrogate model that takes the decision variables as inputs and approximates the objective function is employed to replace the simulation within the meta-heuristic algorithm. Several modeling techniques (i.e., linear regression, polynomial regression, neural network, radial basis network, regression tree ensemble, and Gaussian process regression) are compared based on their accuracy in reproducing solutions to the problem and computational tractability for real-time control under different configurations of simulation parameters. It is found that Gaussian process regression is the most suited for use as a surrogate model for the given problem. Finally, a realistic application with multiple ride-hailing vehicle operations is presented. The proposed approach for controlling the stop positions of vehicles is able to achieve an improvement of 20.65% over the uncontrolled case. The example shows the potential of the proposed approach in reducing the negative impacts of stopping vehicles and favorable computational properties.
Design Methodology for a Medium Voltage Single Stage LLC Resonant Solar PV Inverter
Authors: Parthkumar Bhuvela, Hooman Taghavi, Adel Nasiri
Date: 2023-10-05 00:00:00

An inverter is generally employed with MV LFT to connect to the grid in a grid-tied PV system. However, in some single-stage topologies, the LFTs are replaced by HFT combined with an unfolder inverter. Generally, these topologies have limited use at high-power MV grids due to high switching losses on the primary side. This study proposes an LLC resonant converter-based single-stage inverter design procedure. Resonant converters make use of ZVS to reduce switching losses. The design includes both the resonant tank as well as output filter components. The design is verified by simulations in MATLAB/Simulink for various loads and input voltages at 13.8kV grid output voltage. THD simulations validate the filter design.
Optimal Control of District Cooling Energy Plant with Reinforcement Learning and MPC
Authors: Zhong Guo, Aditya Chaudhari, Austin R. Coffman, Prabir Barooah
Date: 2023-10-05 00:00:00
Tasks: Model Predictive Control, Q-Learning, Reinforcement Learning (RL)

We consider the problem of optimal control of district cooling energy plants (DCEPs) consisting of multiple chillers, a cooling tower, and a thermal energy storage (TES), in the presence of time-varying electricity price. A straightforward application of model predictive control (MPC) requires solving a challenging mixed-integer nonlinear program (MINLP) because of the on/off of chillers and the complexity of the DCEP model. Reinforcement learning (RL) is an attractive alternative since its real-time control computation is much simpler. But designing an RL controller is challenging due to myriad design choices and computationally intensive training. In this paper, we propose an RL controller and an MPC controller for minimizing the electricity cost of a DCEP, and compare them via simulations. The two controllers are designed to be comparable in terms of objective and information requirements. The RL controller uses a novel Q-learning algorithm that is based on least-squares policy iteration. We describe the design choices for the RL controller, including the choice of state space and basis functions, that are found to be effective. The proposed MPC controller does not need a mixed integer solver for implementation, but only a nonlinear program (NLP) solver. A rule-based baseline controller is also proposed to aid in comparison. Simulation results show that the proposed RL and MPC controllers achieve similar savings over the baseline controller, about 17%.
Hidden Markov Models for Stock Market Prediction
Authors: Luigi Catello, Ludovica Ruggiero, Lucia Schiavone, Mario Valentino
Date: 2023-10-05 00:00:00
Tasks: Prediction, Stock Market Prediction

The stock market presents a challenging environment for accurately predicting future stock prices due to its intricate and ever-changing nature. However, the utilization of advanced methodologies can significantly enhance the precision of stock price predictions. One such method is Hidden Markov Models (HMMs). HMMs are statistical models that can be used to model the behavior of a partially observable system, making them suitable for modeling stock prices based on historical data. Accurate stock price predictions can help traders make better investment decisions, leading to increased profits. In this article, we trained and tested a Hidden Markov Model for the purpose of predicting a stock closing price based on its opening price and the preceding day's prices. The model's performance has been evaluated using two indicators: Mean Average Prediction Error (MAPE), which specifies the average accuracy of our model, and Directional Prediction Accuracy (DPA), a newly introduced indicator that accounts for the number of fractional change predictions that are correct in sign.
Physics-assisted machine learning for THz spectroscopy: sensing moisture on plant leaves
Authors: Milan Koumans, Daan Meulendijks, Haiko Middeljans, Djero Peeters, Jacob C. Douma, Dook van Mechelen
Date: 2023-10-06 00:00:00

Signal processing techniques are of vital importance to bring THz spectroscopy to a maturity level to reach practical applications. In this work, we illustrate the use of machine learning techniques for THz time-domain spectroscopy assisted by domain knowledge based on light-matter interactions. We aim at the potential agriculture application to determine the amount of free water on plant leaves, so-called leaf wetness. This quantity is important for understanding and predicting plant diseases that need leaf wetness for disease development. The overall transmission of a moist plant leaf for 12,000 distinct water patterns was experimentally acquired using THz time-domain spectroscopy. We report on key insights of applying decision trees and convolutional neural networks to the data using physics-motivated choices. Eventually, we discuss the generalizability of these models to determine leaf wetness after testing them on cases with increasing deviations from the training set.
Health diagnosis and recuperation of aged Li-ion batteries with data analytics and equivalent circuit modeling
Authors: Riko I Made, Jing Lin, Jintao Zhang, Yu Zhang, Lionel C. H. Moh, Zhaolin Liu, Ning Ding, Sing Yang Chiam, Edwin Khoo, Xuesong Yin, Guangyuan Wesley Zheng
Date: 2023-09-21 00:00:00

Battery health assessment and recuperation play a crucial role in the utilization of second-life Li-ion batteries. However, due to ambiguous aging mechanisms and lack of correlations between the recovery effects and operational states, it is challenging to accurately estimate battery health and devise a clear strategy for cell rejuvenation. This paper presents aging and reconditioning experiments of 62 commercial high-energy type lithium iron phosphate (LFP) cells, which supplement existing datasets of high-power LFP cells. The relatively large-scale data allow us to use machine learning models to predict cycle life and identify important indicators of recoverable capacity. Considering cell-to-cell inconsistencies, an average test error of $16.84\% \pm 1.87\%$ (mean absolute percentage error) for cycle life prediction is achieved by gradient boosting regressor given information from the first 80 cycles. In addition, it is found that some of the recoverable lost capacity is attributed to the lateral lithium non-uniformity within the electrodes. An equivalent circuit model is built and experimentally validated to demonstrate how such non-uniformity can be accumulated, and how it can give rise to recoverable capacity loss. SHapley Additive exPlanations (SHAP) analysis also reveals that battery operation history significantly affects the capacity recovery.
Bespoke scapegoats: Scientific advisory bodies and blame avoidance in the Covid-19 pandemic and beyond
Authors: Roger Koppl, Kira Pronin, Nick Cowen, Marta Podemska-Mikluch, Pablo Paniagua Prieto
Date: 2023-10-06 00:00:00

Scholars have not asked why so many governments created ad hoc scientific advisory bodies (ahSABs) to address the Covid-19 pandemic instead of relying on existing public health infrastructure. We address this neglected question with an exploratory study of the US, UK, Sweden, Italy, Poland, and Uganda. Drawing on our case studies and the blame-avoidance literature, we find that ahSABs are created to excuse unpopular policies and take the blame should things go wrong. Thus, membership typically represents a narrow range of perspectives. An ahSAB is a good scapegoat because it does little to reduce government discretion and has limited ability to deflect blame back to government. Our explanation of our deviant case of Sweden, that did not create and ahSAB, reinforces our general principles. We draw the policy inference that ahSAB membership should be vetted by the legislature to ensure broad membership.
A New Weighted Food CPI from Scanner Big Data in China
Authors: Zhenkun Zhou, Zikun Song, Tao Ren
Date: 2023-10-06 00:00:00

Scanner big data has potential to construct Consumer Price Index (CPI). The study introduces a new weighted price index called S-FCPIw, which is constructed using scanner big data from retail sales in China. We address the limitations of China's CPI especially for its high cost and untimely release, and demonstrate the reliability of S-FCPIw by comparing it with existing price indices. S-FCPIw can not only reflect the changes of goods prices in higher frequency and richer dimension, and the analysis results show that S-FCPIw has a significant and strong relationship with CPI and Food CPI. The findings suggest that scanner big data can supplement traditional CPI calculations in China and provide new insights into macroeconomic trends and inflation prediction. We have made S-FCPIw publicly available and update it on a weekly basis to facilitate further study in this field.