pwc-archive/papers-with-abstracts huggingface dataset:
Variational Autoencoder for Channel Estimation: Real-World Measurement Insights
Authors: Michael Baur, Benedikt Böck, Nurettin Turan, Wolfgang Utschick
Date: 2023-12-06 00:00:00

This work utilizes a variational autoencoder for channel estimation and evaluates it on real-world measurements. The estimator is trained solely on noisy channel observations and parameterizes an approximation to the mean squared error-optimal estimator by learning observation-dependent conditional first and second moments. The proposed estimator significantly outperforms related state-of-the-art estimators on real-world measurements. We investigate the effect of pre-training with synthetic data and find that the proposed estimator exhibits comparable results to the related estimators if trained on synthetic data and evaluated on the measurement data. Furthermore, pre-training on synthetic data also helps to reduce the required measurement training dataset size.
On Topological Conditions for Enabling Transient Control in Leader-follower Networks
Authors: Fei Chen, Dimos V. Dimarogonas
Date: 2023-12-06 00:00:00

We derive necessary and sufficient conditions for leader-follower multi-agent systems such that we can further apply prescribed performance control to achieve the desired formation while satisfying certain transient constraints. A leader-follower framework is considered in the sense that a group of agents with external inputs are selected as leaders in order to drive the group of followers in a way that the entire system can achieve target formation within certain prescribed performance transient bounds. We first derive necessary conditions on the leader-follower graph topology under which the target formation together with the prescribed performance guarantees can be fulfilled. Afterwards, the derived necessary conditions are extended to necessary and sufficient conditions for leader-follower formation control under transient constraints. Finally, the proposed results are illustrated with simulation examples.
Diff-GO: Diffusion Goal-Oriented Communications to Achieve Ultra-High Spectrum Efficiency
Authors: Achintha Wijesinghe, Songyang Zhang, Suchinthaka Wanninayaka, Weiwei Wang, Zhi Ding
Date: 2023-11-13 00:00:00

The latest advances in artificial intelligence (AI) present many unprecedented opportunities to achieve much improved bandwidth saving in communications. Unlike conventional communication systems focusing on packet transport, rich datasets and AI makes it possible to efficiently transfer only the information most critical to the goals of message recipients. One of the most exciting advances in generative AI known as diffusion model presents a unique opportunity for designing ultra-fast communication systems well beyond language-based messages. This work presents an ultra-efficient communication design by utilizing generative AI-based on diffusion models as a specific example of the general goal-oriented communication framework. To better control the regenerated message at the receiver output, our diffusion system design includes a local regeneration module with finite dimensional noise latent. The critical significance of noise latent control and sharing residing on our Diff-GO is the ability to introduce the concept of "local generative feedback" (Local-GF), which enables the transmitter to monitor the quality and gauge the quality or accuracy of the message recovery at the semantic system receiver. To this end, we propose a new low-dimensional noise space for the training of diffusion models, which significantly reduces the communication overhead and achieves satisfactory message recovery performance. Our experimental results demonstrate that the proposed noise space and the diffusion-based generative model achieve ultra-high spectrum efficiency and accurate recovery of transmitted image signals. By trading off computation for bandwidth efficiency (C4BE), this new framework provides an important avenue to achieve exceptional computation-bandwidth tradeoff.
Uncertainty Propagation on Unimodular Matrix Lie Groups
Authors: Jikai Ye, Amitesh S. Jayaraman, Gregory S. Chirikjian
Date: 2023-12-06 00:00:00

This paper addresses uncertainty propagation on unimodular matrix Lie groups that have a surjective exponential map. We derive the exact formula for the propagation of mean and covariance in a continuous-time setting from the governing Fokker-Planck equation. Two approximate propagation methods are discussed based on the exact formula. One uses numerical quadrature and another utilizes the expansion of moments. A closed-form second-order propagation formula is derived. We apply the general theory to the joint attitude and angular momentum uncertainty propagation problem and numerical experiments demonstrate two approximation methods. These results show that our new methods have high accuracy while being computationally efficient.
Golden Gemini is All You Need: Finding the Sweet Spots for Speaker Verification
Authors: Tianchi Liu, Kong Aik Lee, Qiongqiong Wang, Haizhou Li
Date: 2023-12-06 00:00:00
Tasks: All, Speaker Verification

Previous studies demonstrate the impressive performance of residual neural networks (ResNet) in speaker verification. The ResNet models treat the time and frequency dimensions equally. They follow the default stride configuration designed for image recognition, where the horizontal and vertical axes exhibit similarities. This approach ignores the fact that time and frequency are asymmetric in speech representation. In this paper, we address this issue and look for optimal stride configurations specifically tailored for speaker verification. We represent the stride space on a trellis diagram, and conduct a systematic study on the impact of temporal and frequency resolutions on the performance and further identify two optimal points, namely Golden Gemini, which serves as a guiding principle for designing 2D ResNet-based speaker verification models. By following the principle, a state-of-the-art ResNet baseline model gains a significant performance improvement on VoxCeleb, SITW, and CNCeleb datasets with 7.70%/11.76% average EER/minDCF reductions, respectively, across different network depths (ResNet18, 34, 50, and 101), while reducing the number of parameters by 16.5% and FLOPs by 4.1%. We refer to it as Gemini ResNet. Further investigation reveals the efficacy of the proposed Golden Gemini operating points across various training conditions and architectures. Furthermore, we present a new benchmark, namely the Gemini DF-ResNet, using a cutting-edge model.
Leveraging Laryngograph Data for Robust Voicing Detection in Speech
Authors: Yixuan Zhang, Heming Wang, DeLiang Wang
Date: 2023-12-05 00:00:00

Accurately detecting voiced intervals in speech signals is a critical step in pitch tracking and has numerous applications. While conventional signal processing methods and deep learning algorithms have been proposed for this task, their need to fine-tune threshold parameters for different datasets and limited generalization restrict their utility in real-world applications. To address these challenges, this study proposes a supervised voicing detection model that leverages recorded laryngograph data. The model is based on a densely-connected convolutional recurrent neural network (DC-CRN), and trained on data with reference voicing decisions extracted from laryngograph data sets. Pretraining is also investigated to improve the generalization ability of the model. The proposed model produces robust voicing detection results, outperforming other strong baseline methods, and generalizes well to unseen datasets. The source code of the proposed model with pretraining is provided along with the list of used laryngograph datasets to facilitate further research in this area.
Distributed Speech Dereverberation Using Weighted Prediction Error
Authors: Ziye Yang, Mengfei Zhang, Jie Chen
Date: 2023-12-05 00:00:00
Tasks: Prediction, Speech Dereverberation

Speech dereverberation aims to alleviate the negative impact of late reverberant reflections. The weighted prediction error (WPE) method is a well-established technique known for its superior performance in dereverberation. However, in scenarios where microphone nodes are dispersed, the centralized approach of the WPE method requires aggregating all observations for inverse filtering, resulting in a significant computational burden. This paper introduces a distributed speech dereverberation method that emphasizes low computational complexity at each node. Specifically, we leverage the distributed adaptive node-specific signal estimation (DANSE) algorithm within the multichannel linear prediction (MCLP) process. This approach empowers each node to perform local operations with reduced complexity while achieving the global performance through inter-node cooperation. Experimental results validate the effectiveness of our proposed method, showcasing its ability to achieve efficient speech dereverberation in dispersed microphone node scenarios.
Time-Domain Operational Metrics for Real-time Resilience Assessment in DC Microgrids
Authors: Maral Shadaei, Ali Hosseinipour, Javad Khazaei
Date: 2023-12-06 00:00:00
Tasks: Computational Efficiency

Resilience is emerging as an evolving notion, reflecting a system's ability to endure and adapt to sudden and catastrophic changes and disruptions. This paper spotlights the significance of the quantitative resilience indices of medium-voltage DC (MVDC) distribution technology in marine vessels, notably naval ships. Given the intricate electrical requirements of modern naval ships, the need for a robust power supply underlines the imperative of resilient DC microgrids. Addressing this, our study introduces a novel quantitative metric for operational resilience of DC microgrids based on the measured voltage of main DC bus. This metric not only fuses real-time tracking, compatibility, and computational efficiency, but also adeptly monitors multiple event phases based on time-domain analysis of dc bus voltage dynamics. The intricacies of the dc bus voltage, including overshoots and undershoots, are meticulously accounted for in the algorithm design. With respect to existing research that typically focuses on offline resilience assessments, the proposed index provides valuable real-time information for microgrid operators and identifies whether microgrid resilience is deteriorating over time.
Nonlinear Model Predictive Control for Navy Microgrids with Stabilizing Terminal Ingredients
Authors: Saskia Putri, Xiaoyu Ge, Faegheh Moazeni, Javad Khazaei
Date: 2023-12-06 00:00:00
Tasks: energy management, Management, Model Predictive Control

This paper presents a novel control strategy for medium voltage DC (MVDC) naval shipboard microgrids (MGs), employing a nonlinear model predictive controller (NMPC) enhanced with stabilizing features and an intricate droop control architecture. This combination quickly regulates the output voltage and adeptly allocates supercapacitors for pulsed power loads (PPLs), while the battery energy storage system (BESS) and auxiliary generators handle the steady state loads. A key feature of this study is the formulation of terminal cost and constraints, providing recursive feasibility and closed-loop stability in the Lyapunov sense, that offers a more robust and effective approach to naval power and energy management. By comparing the proposed Lyapunov-based NMPC with conventional PI controller under fluctuating PPLs, the control robustness is validated.
Voltage Restoration in MVDC Shipboard Microgrids with Economic Nonlinear Model Predictive Control
Authors: Saskia Putri, Ali Hosseinipour, Xiaoyu Ge, Faegheh Moazeni, Javad Khazaei
Date: 2023-12-06 00:00:00
Tasks: Model Predictive Control

Future Naval Microgrids (MGs) will include hybrid energy storage systems (ESS), including battery and supercapacitors to respond to emerging constant power loads (CPLs) and fluctuating pulsed power loads (PPLs). Voltage regulation of naval microgrids and power sharing among these resources become critical for success of a mission. This paper presents a novel control strategy using nonlinear model predictive controller embedded with a complex droop control architecture for voltage restoration and power sharing in medium voltage DC (MVDC) Naval MGs. The complex droop control ensures allocating supercapacitors (SCs) for high-frequency loads (i.e., PPLs), while battery energy storage system (BESS) and auxiliary generators share the steady-state load (i.e., CPL). Compared to state-of-the-art control of the naval ship MGs that relies on linear models, the proposed method incorporates the nonlinear behavior of the MGs in the closed-loop control framework via nonlinear model predictive control (NMPC). A reduced order representation of the MVDC dynamic is employed as the prediction model, augmented with a multi-objective, constraints-based, optimal control formulation. The results demonstrate the effectiveness of the proposed control framework for voltage restoration and power sharing of resources in naval MGs.
A Mixed Integer Quadratic Program for Valuing the Impact of Price and Forecast Uncertainty for Wind Generators
Authors: Daniel Shen, Marija Ilic
Date: 2023-12-06 00:00:00

Owners of wind power plants are exposed to financial risk in wholesale electricity markets due to the uncertain nature of wind forecasts and price volatility. In the event of a wind shortfall, the plant may have to repurchase power at a higher price in the real-time market. However, reducing the power offered in the day-ahead market may also be interpreted by regulators as physical withholding. We formulate and solve a mixed-integer quadratic program (MIQP) that prices the uncertain portion of a wind generator's forecast to hedge against uncertainties and which addresses concerns around withholding. We exploit the structure of the MIQP inputs to introduce additional constraints to improve computation time. Additionally, we provide a qualitative approach for generators and regulators to interpret the results of the MIQP. Finally, we simulate a real-world application for a wind farm in New York using past wind forecasts and NYISO prices.
Vulnerability of Building Energy Management against Targeted False Data Injection Attacks:Model Predictive Control vs. Proportional Integral
Authors: Xiaoyu Ge, Kamelia Norouzi, Faegheh Moazeni, Mirel Sehic, Javad Khazaei, Parv Venkitasubramaniam, Rick Blum
Date: 2023-12-06 00:00:00
Tasks: energy management, Management, Model Predictive Control

Cybersecurity in building energy management is crucial for protecting infrastructure, ensuring data integrity, and preventing unauthorized access or manipulation. This paper investigates the energy efficiency and cybersecurity of building energy management systems (BMS) against false data injection (FDI) attacks using proportional-integral (PI) and model predictive control (MPC) methods. Focusing on a commercial building model with five rooms, vulnerability of PI-based BMS and nonlinear MPC-based BMS against FDIs on sensors and actuators is studied. The study aims to assess the effectiveness of these control strategies in maintaining system performance and lifespan, highlighting the potential of MPC in enhancing system resilience against cyber threats. Our case studies demonstrate that even a short term FDIA can cause a 12% reduction in lifetime of a heat-pump under an MPC controller, and cause a near thirty-fold overuse of flow valves under a PI controller.
Operational Resilience Assessment: A Frequency-Domain Approach for DC Microgrids
Authors: Ali Hosseinipour, Maral Shadaei, Javad Khazaei
Date: 2023-12-06 00:00:00

DC shipboard microgrids (SMGs) are highly dynamic systems susceptible to failure due to various cyber-physical disturbances, such as extreme weather and mission operations during wartime. In this paper, the real-time operational resilience (OR) evaluation of DC SMGs against dynamic disturbances is proposed using frequency-domain metrics. To this end, first the drawbacks of time-domain OR evaluation using an energy imbalance index is discussed. As the time-domain energy imbalance index is shown to be incapable of real-time OR assessment, particularly in the context of droop-controlled DC SMGs without secondary voltage restoration control, the $\mathcal{H}_2$ and $\mathcal{H_\infty}$ norms of candidate transfer functions (TFs) of the system are proposed as measures of resilience. It is shown that the proposed norms calculated for the bus impedance TF of the system provides intuitive results in terms of energy imbalance and can be computed in real time. The case studies conducted for the study DC SMG under pulsed power load (PPL) disturbances demonstrate the shortcoming of the time-domain OR evaluation and the capability of the proposed frequency-domain metrics in intuitive OR evaluation of DC SMGs.
Inverse Optimal Control as an Errors-in-Variables Problem
Authors: Rahel Rickenbach, Anna Scampicchio, Melanie N. Zeilinger
Date: 2023-12-06 00:00:00

Inverse optimal control (IOC) is about estimating an unknown objective of interest given its optimal control sequence. However, truly optimal demonstrations are often difficult to obtain, e.g., due to human errors or inaccurate measurements. This paper presents an IOC framework for objective estimation from multiple sub-optimal demonstrations in constrained environments. It builds upon the Karush-Kuhn-Tucker optimality conditions, and addresses the Errors-In-Variables problem that emerges from the use of sub-optimal data. The approach presented is applied to various systems in simulation, and consistency guarantees are provided for linear systems with zero mean additive noise, polytopic constraints, and objectives with quadratic features.
An efficient data-based off-policy Q-learning algorithm for optimal output feedback control of linear systems
Authors: Mohammad Alsalti, Victor G. Lopez, Matthias A. Müller
Date: 2023-12-06 00:00:00
Tasks: Q-Learning

In this paper, we present a Q-learning algorithm to solve the optimal output regulation problem for discrete-time LTI systems. This off-policy algorithm only relies on using persistently exciting input-output data, measured offline. No model knowledge or state measurements are needed and the obtained optimal policy only uses past input-output information. Moreover, our formulation of the proposed algorithm renders it computationally efficient. We provide conditions that guarantee the convergence of the algorithm to the optimal solution. Finally, the performance of our method is compared to existing algorithms in the literature.
Adaptive flexibility function in smart energy systems: A linearized price-demand mapping approach
Authors: Seyed Shahabaldin Tohidi, Henrik Madsen, Georgios Tsaousoglou, Tobias K. S. Ritschel
Date: 2023-12-06 00:00:00

This paper proposes an adaptive mechanism for price signal generation using a piecewise linear approximation of a flexibility function with unknown parameters. In this adaptive approach, the price signal is parameterized and the parameters are changed adaptively such that the output of the flexibility function follows the reference demand signal provided by the involved aggregator. This is guaranteed using the Lyapunov stability theorem. The proposed method does not require an estimation algorithm for unknown parameters, that eliminates the need for persistency of excitation of signals, and consequently, simplifies offering the flexibility services. Furthermore, boundedness of the price signal is ensured using a projection algorithm in the adaptive system. We present simulation results that demonstrate the price generation results using the proposed approaches.
Beacon-enabled TDMA Ultraviolet Communication Network System Design and Realization
Authors: Yuchen Pan, Fei Long, Ping Li, Haotian Shi, Jiazhao Shi, Hanlin Xiao, Chen Gong, Zhengyuan Xu
Date: 2023-12-06 00:00:00

Nonline of sight (NLOS) ultraviolet (UV) scattering communication can serve as a good candidate for outdoor optical wireless communication (OWC) in the cases of non-perfect transmitter-receiver alignment and radio silence. We design and demonstrate a NLOS UV scattering communication network system in this paper, where a beacon-enabled time division multiple access (TDMA) scheme is adopted. In our system, LED and PMT are employed for transmitter and receiver devices, repectivey. Furthermore, we design algorithms for beacon transmission, beacon reception, time compensation, and time slot transition for hardware realization in field-programmable gate array (FPGA) board based on master-slave structure, where master node periodically transmits beacon signals to slave nodes. Experimental results are provided to evaluate the time synchronization error and specify the system key parameters for real-time implementation. We perform field tests for real-time communication network with the transmission range over 110 multiplied by 90 square meters, where the system throughput reaches 800kbps.
The Role of Net-Zero Energy Residential Buildings in Florida's Energy Transition: Economic Analysis and Technical Benefits
Authors: Hamed Haggi, James M. Fenton
Date: 2023-12-05 00:00:00

Net-zero emission policies, coupled with declining costs of renewables, battery storage, and electric vehicles, require both direct and indirect electrification (such as green hydrogen) of the energy infrastructure to address climate change impacts, improve resilience, and lower energy expenses. In this paper, a comprehensive techno-economic analysis was performed that integrated net-zero energy buildings within the context of Florida energy transition by the year 2050. The analysis compares the monthly cost savings of owning photovoltaic (PV) on the roof, battery storage, and Electric Vehicle (EV) to the cost of buying electricity from the grid for both existing and newly built Orlando homes. The impact of income tax credits (ITC) and energy efficiency improvements on monthly savings was taken into account. The levelized cost of electricity for residential PV and PV + battery systems was determined and extended to provide a cost equivalent to gasoline for an average EV. Rooftop solar fueling an EV for 10,000 miles saves \$100 per month over purchasing gasoline. Today, Florida residents can save on their monthly costs of electricity and gasoline if they have both PV on their roof and a battery system (sized for average daily residence load), and benefit from the federal ITC. Florida residents can cost-effectively transition to their own solar, battery storage, and electric vehicle to home systems. Allowing utility-scale solar to be used for on-site EV fast-charging and hydrogen production through electrolysis for fuel cell vehicles or blending with natural gas, minimizing the need for electricity transmission and distribution costs across the power grids.