pwc-archive/papers-with-abstracts huggingface dataset:
Distributed Traffic Signal Control via Coordinated Maximum Pressure-plus-Penalty
Authors: Vinzenz Tütsch, Zhiyu He, Florian Dörfler, Kenan Zhang
Date: 2024-04-30 00:00:00
Tasks: Computational Efficiency, Distributed Optimization, Traffic Signal Control

This paper develops an adaptive traffic control policy inspired by Maximum Pressure (MP) while imposing coordination across intersections. The proposed Coordinated Maximum Pressure-plus-Penalty (CMPP) control policy features a local objective for each intersection that consists of the total pressure within the neighborhood and a penalty accounting for the queue capacities and continuous green time for certain movements. The corresponding control task is reformulated as a distributed optimization problem and solved via two customized algorithms: one based on the alternating direction method of multipliers (ADMM) and the other follows a greedy heuristic augmented with a majority vote. CMPP not only provides a theoretical guarantee of queuing network stability but also outperforms several benchmark controllers in simulations on a large-scale real traffic network with lower average travel and waiting time per vehicle, as well as less network congestion. Furthermore, CPMM with the greedy algorithm enjoys comparable computational efficiency as fully decentralized controllers without significantly compromising the control performance, which highlights its great potential for real-world deployment.
ML-based handover prediction over a real O-RAN deployment using RAN Intelligent controller
Authors: Merim Dzaferagic, Bruno Missi Xavier, Diarmuid Collins, Vince D'Onofrio, Magnos Martinello, Marco Ruffini
Date: 2024-04-14 00:00:00

O-RAN introduces intelligent and flexible network control in all parts of the network. The use of controllers with open interfaces allow us to gather real time network measurements and make intelligent/informed decision. The work in this paper focuses on developing a use-case for open and reconfigurable networks to investigate the possibility to predict handover events and understand the value of such predictions for all stakeholders that rely on the communication network to conduct their business. We propose a Long-Short Term Memory Machine Learning approach that takes standard Radio Access Network measurements to predict handover events. The models were trained on real network data collected from a commercial O-RAN setup deployed in our OpenIreland testbed. Our results show that the proposed approach can be optimized for either recall or precision, depending on the defined application level objective. We also link the performance of the Machine Learning (ML) algorithm to the network operation cost. Our results show that ML-based matching between the required and available resources can reduce operational cost by more than 80%, compared to long term resource purchases.
Attention-Constrained Inference for Robust Decoder-Only Text-to-Speech
Authors: Hankun Wang, Chenpeng Du, Yiwei Guo, Shuai Wang, Xie Chen, Kai Yu
Date: 2024-04-30 00:00:00
Tasks: Decoder, text-to-speech, Text to Speech

Recent popular decoder-only text-to-speech models are known for their ability of generating natural-sounding speech. However, such models sometimes suffer from word skipping and repeating due to the lack of explicit monotonic alignment constraints. In this paper, we notice from the attention maps that some particular attention heads of the decoder-only model indicate the alignments between speech and text. We call the attention maps of those heads Alignment-Emerged Attention Maps (AEAMs). Based on this discovery, we propose a novel inference method without altering the training process, named Attention-Constrained Inference (ACI), to facilitate monotonic synthesis. It first identifies AEAMs using the Attention Sweeping algorithm and then applies constraining masks on AEAMs. Our experimental results on decoder-only TTS model VALL-E show that the WER of synthesized speech is reduced by up to 20.5% relatively with ACI while the naturalness and speaker similarity are comparable.
Deep low-latency joint speech transmission and enhancement over a gaussian channel
Authors: Mohammad Bokaei, Jesper Jensen, Simon Doclo, Jan Østergaard
Date: 2024-04-30 00:00:00
Tasks: Decoder, Speech Enhancement

Ensuring intelligible speech communication for hearing assistive devices in low-latency scenarios presents significant challenges in terms of speech enhancement, coding and transmission. In this paper, we propose novel solutions for low-latency joint speech transmission and enhancement, leveraging deep neural networks (DNNs). Our approach integrates two state-of-the-art DNN architectures for low-latency speech enhancement and low-latency analog joint source-channel-based transmission, creating a combined low-latency system and jointly training both systems in an end-to-end approach. Due to the computational demands of the enhancement system, this order is suitable when high computational power is unavailable in the decoder, like hearing assistive devices. The proposed system enables the configuration of total latency, achieving high performance even at latencies as low as 3 ms, which is typically challenging to attain. The simulation results provide compelling evidence that a joint enhancement and transmission system is superior to a simple concatenation system in diverse settings, encompassing various wireless channel conditions, latencies, and background noise scenarios.
Audio-Visual Target Speaker Extraction with Reverse Selective Auditory Attention
Authors: Ruijie Tao, Xinyuan Qian, Yidi Jiang, Junjie Li, Jiadong Wang, Haizhou Li
Date: 2024-04-29 00:00:00
Tasks: Target Speaker Extraction

Audio-visual target speaker extraction (AV-TSE) aims to extract the specific person's speech from the audio mixture given auxiliary visual cues. Previous methods usually search for the target voice through speech-lip synchronization. However, this strategy mainly focuses on the existence of target speech, while ignoring the variations of the noise characteristics, i.e., interference speaker and the background noise. That may result in extracting noisy signals from the incorrect sound source in challenging acoustic situations. To this end, we propose a novel selective auditory attention mechanism, which can suppress interference speakers and non-speech signals to avoid incorrect speaker extraction. By estimating and utilizing the undesired noisy signal through this mechanism, we design an AV-TSE framework named Subtraction-and-ExtrAction network (SEANet) to suppress the noisy signals. We conduct abundant experiments by re-implementing three popular AV-TSE methods as the baselines and involving nine metrics for evaluation. The experimental results show that our proposed SEANet achieves state-of-the-art results and performs well for all five datasets. The code can be found in: https://github.com/TaoRuijie/SEANet.git
A Comparison of Differential Performance Metrics for the Evaluation of Automatic Speaker Verification Fairness
Authors: Oubaida Chouchane, Christoph Busch, Chiara Galdi, Nicholas Evans, Massimiliano Todisco
Date: 2024-04-27 00:00:00
Tasks: Face Recognition, Fairness, Speaker Verification

When decisions are made and when personal data is treated by automated processes, there is an expectation of fairness -- that members of different demographic groups receive equitable treatment. This expectation applies to biometric systems such as automatic speaker verification (ASV). We present a comparison of three candidate fairness metrics and extend previous work performed for face recognition, by examining differential performance across a range of different ASV operating points. Results show that the Gini Aggregation Rate for Biometric Equitability (GARBE) is the only one which meets three functional fairness measure criteria. Furthermore, a comprehensive evaluation of the fairness and verification performance of five state-of-the-art ASV systems is also presented. Our findings reveal a nuanced trade-off between fairness and verification accuracy underscoring the complex interplay between system design, demographic inclusiveness, and verification reliability.
Conceptual Mapping of Controversies
Authors: Claude Draude, Dominik Dürrschnabel, Johannes Hirth, Viktoria Horn, Jonathan Kropf, Jörn Lamla, Gerd Stumme, Markus Uhlmann
Date: 2024-04-25 00:00:00
Tasks: Articles, Diversity, Navigate

With our work, we contribute towards a qualitative analysis of the discourse on controversies in online news media. For this, we employ Formal Concept Analysis and the economics of conventions to derive conceptual controversy maps. In our experiments, we analyze two maps from different news journals with methods from ordinal data science. We show how these methods can be used to assess the diversity, complexity and potential bias of controversies. In addition to that, we discuss how the diagrams of concept lattices can be used to navigate between news articles.
Dual-Port Grid-Forming Interconnecting Power Converters in Hybrid AC/DC Grids
Authors: Josep Arévalo-Soler, Mehrdad Nahalparvari, Dominic Gross, Eduardo Prieto-Araujo, Staffan Norrga, Oriol Gomis-Bellmunt
Date: 2024-04-30 00:00:00

Interconnecting power converters (IPC) are the main elements enabling the interconnection of multiple high-voltage alternating current (HVAC) and high-voltage direct current (HVDC) subgrids. These converters can be classified either as grid-forming or grid-following. These roles can be assigned to both ac and dc terminals. This work compares state-of-the-art single-port grid-forming and grid-following control schemes with a dual-port grid-forming control scheme, which can simultaneously form a stable voltage on the ac and the dc sides. The dual-port grid-forming small-signal stability and dynamic behavior under fluctuations in the power flow are studied and compared against state-of-the-art control architectures. Moreover, the dual-port control scheme is validated and tested on a down-scaled laboratory platform with several transient events.
Consensus + Innovations Approach for Online Distributed Multi-Area Inertia Estimation
Authors: Nicolai Lorenz-Meyer, Hans Würfel, Johannes Schiffer
Date: 2024-04-30 00:00:00

The reduction of overall system inertia in modern power systems due to the increasing deployment of distributed energy resources is generally recognized as a major issue for system stability. Consequently, real-time monitoring of system inertia is critical to ensure a reliable and cost-effective system operation. Large-scale power systems are typically managed by multiple transmission system operators, making it difficult to have a central entity with access to global measurement data, which is usually required for estimating the overall system inertia. We address this problem by proposing a fully distributed inertia estimation algorithm with rigorous analytical convergence guarantees. This method requires only peer-to-peer sharing of local parameter estimates between neighboring control areas, eliminating the need for a centralized collection of real-time measurements. We robustify the algorithm in the presence of typical power system disturbances and demonstrate its performance in simulations based on the well-known New England IEEE-39 bus system.
Passivation of Clustered DC Microgrids with Non-Monotone Loads
Authors: Albertus Johannes Malan, Joel Ferguson, Michele Cucuzzella, Jacquelien M. A. Scherpen, Sören Hohmann
Date: 2024-04-30 00:00:00

In this paper, we consider the problem of voltage stability in DC networks containing uncertain loads with non-monotone incremental impedances and where the steady-state power availability is restricted to a subset of the buses in the network. We propose controllers for powered buses that guarantee voltage regulation and output strictly equilibrium independent passivity (OS-EIP) of the controlled buses, while buses without power are equipped with controllers that dampen their transient behaviour. The OS-EIP of a cluster containing both bus types is verified through a linear matrix inequality (LMI) condition, and the asymptotic stability of the overall microgrid with uncertain, non-monotone loads is ensured by interconnecting the OS-EIP clusters. By further employing singular perturbation theory, we show that the OS-EIP property of the clusters is robust against certain network parameter and topology changes.
Two-Stage Robust Planning Model for Park-Level Integrated Energy System Considering Uncertain Equipment Contingency
Authors: Zuxun Xiong, Xinwei Shen, Hongbin Sun
Date: 2024-04-30 00:00:00

To enhance the reliability of Integrated Energy Systems (IESs) and address the research gap in reliability-based planning methods, this paper proposes a two-stage robust planning model specifically for park-level IESs. The proposed planning model considers uncertainties like load demand fluctuations and equipment contingencies, and provides a reliable scheme of equipment selection and sizing for IES investors. Inspired by the unit commitment problem, we formulate an equipment contingency uncertainty set to accurately describe the potential equipment contingencies which happen and can be repaired within a day. Then, a modified nested column-and-constraint generation algorithm is applied to solve this two-stage robust planning model with integer recourse efficiently. In the case study, the role of energy storage system for IES reliability enhancement is analyzed in detail. Computational results demonstrate the advantage of the proposed model over other planning models in terms of improving reliability.
Online Electricity Purchase for Data Center with Dynamic Virtual Battery from Flexibility Aggregation
Authors: Kekun Gao, Yuejun Yan, Yixuan Liu, Endong Liu, Pengcheng You
Date: 2024-04-30 00:00:00
Tasks: Scheduling

As a critical component of modern infrastructure, data centers account for a huge amount of power consumption and greenhouse gas emission. This paper studies the electricity purchase strategy for a data center to lower its energy cost while integrating local renewable generation under uncertainty. To facilitate efficient and scalable decision-making, we propose a two-layer hierarchy where the lower layer consists of the operation of all electrical equipment in the data center and the upper layer determines the procurement and dispatch of electricity. At the lower layer, instead of device-level scheduling in real time, we propose to exploit the inherent flexibility in demand, such as thermostatically controlled loads and flexible computing tasks, and aggregate them into virtual batteries. By this means, the upper-layer decision only needs to take into account these virtual batteries, the size of which is generally small and independent of the data center scale. We further propose an online algorithm based on Lyapunov optimization to purchase electricity from the grid with a manageable energy cost, even though the prices, renewable availability, and battery specifications are uncertain and dynamic. In particular, we show that, under mild conditions, our algorithm can achieve bounded loss compared with the offline optimal cost, while strictly respecting battery operational constraints. Extensive simulation studies validate the theoretical analysis and illustrate the tradeoff between optimality and conservativeness.
A Concept for Semi-Automatic Configuration of Sufficiently Valid Simulation Setups for Automated Driving Systems
Authors: Niklas Braun, Markus Steimle, Martin Törngren, Markus Maurer
Date: 2024-04-30 00:00:00
Tasks: valid

As simulation is increasingly used in scenario-based approaches to test Automated Driving Systems, the credibility of simulation results is a major concern. Arguably, credibility depends on the validity of the simulation setup and simulation models. When selecting appropriate simulation models, a trade-off must be made between validity, often connected to the model's fidelity, and cost of computation. However, due to the large number of test cases, expert-based methods to create sufficiently valid simulation setups seem infeasible. We propose using design contracts in order to semi-automatically compose simulation setups for given test cases from simulation models and to derive requirements for the simulation models, supporting separation of concerns between simulation model developers and users. Simulation model contracts represent their validity domains by capturing a validity guarantee and the associated operating conditions in an assumption. We then require the composition of the simulation model contracts to refine a test case contract. The latter contract captures the operating conditions of the test case in its assumption and validity requirements in its guarantee. Based on this idea, we present a framework that supports the compositional configuration of simulation setups based on the contracts and a method to derive runtime monitors for these simulation setups.
mm-Wave and sub-THz Chip-to-Package Transitions for Communications Systems
Authors: Nima Baniasadi, Rami Hijab, Ali Niknejad
Date: 2024-04-30 00:00:00

This work presents mm-Wave and sub-THz chip-to-package transitions for communications systems. To date, reported transitions either have high loss, typically 3 to 4 dB, or require high cost packages to support very fine bump pitches and low loss materials. We analyze the impact of transitions on a high frequency, wide bandwidth communication system and present the design of a chip-to-package transition in two different commercial packaging technologies. The proposed transitions achieve <1 dB loss in both technologies, validating the design methodology.
Temporal Logic Resilience for Dynamical Systems
Authors: Adnane Saoud, Pushpak Jagtap, Sadegh Soudjani
Date: 2024-04-30 00:00:00

We consider the notion of resilience for cyber-physical systems, that is, the ability of the system to withstand adverse events while maintaining acceptable functionality. We use finite temporal logic to express the requirements on the acceptable functionality and define the resilience metric as the maximum disturbance under which the system satisfies the temporal requirements. We fix a parameterized template for the set of disturbances and form a robust optimization problem under the system dynamics and the temporal specifications to find the maximum value of the parameter. Additionally, we introduce two novel classes of specifications: closed and convex finite temporal logics specifications, offering a comprehensive analysis of the resilience metric within these specific frameworks. From a computational standpoint, we present an exact solution for linear systems and exact-time reachability and finite-horizon safety, complemented by an approximate solution for finite-horizon reachability. Extending our findings to nonlinear systems, we leverage linear approximations and SMT-based approaches to offer viable computational methodologies. The theoretical results are demonstrated on the temperature regulation of buildings, adaptive cruise control and DC motors.
Assessing the safety benefits of CACC+ based coordination of connected and autonomous vehicle platoons in emergency braking scenarios
Authors: Guoqi Ma, Prabhakar R. Pagilla, Swaroop Darbha
Date: 2024-04-30 00:00:00
Tasks: Autonomous Vehicles

Ensuring safety is the most important factor in connected and autonomous vehicles, especially in emergency braking situations. As such, assessing the safety benefits of one information topology over other is a necessary step towards evaluating and ensuring safety. In this paper, we compare the safety benefits of a cooperative adaptive cruise control which utilizes information from one predecessor vehicle (CACC) with the one that utilizes information from multiple predecessors (CACC+) for the maintenance of spacing under an emergency braking scenario. A constant time headway policy is employed for maintenance of spacing (that includes a desired standstill spacing distance and a velocity dependent spacing distance) between the vehicles in the platoon. The considered emergency braking scenario consists of braking of the leader vehicle of the platoon at its maximum deceleration and that of the following vehicles to maintain the spacing as per CACC or CACC+. By focusing on the standstill spacing distance and utilizing Monte Carlo simulations, we assess the safety benefits of CACC+ over CACC by utilizing the following safety metrics: (1) probability of collision, (2) expected number of collisions, and (3) severity of collision (defined as the relative velocity of the two vehicles at impact). We present and provide discussion of these results.
Data-Driven Min-Max MPC for Linear Systems: Robustness and Adaptation
Authors: Yifan Xie, Julian Berberich, Frank Allgöwer
Date: 2024-04-29 00:00:00
Tasks: Model Predictive Control

Data-driven controllers design is an important research problem, in particular when data is corrupted by the noise. In this paper, we propose a data-driven min-max model predictive control (MPC) scheme using noisy input-state data for unknown linear time-invariant (LTI) system. The unknown system matrices are characterized by a set-membership representation using the noisy input-state data. Leveraging this representation, we derive an upper bound on the worst-case cost and determine the corresponding optimal state-feedback control law through a semidefinite program (SDP). We prove that the resulting closed-loop system is robustly stabilized and satisfies the input and state constraints. Further, we propose an adaptive data-driven min-max MPC scheme which exploits additional online input-state data to improve closed-loop performance. Numerical examples show the effectiveness of the proposed methods.
Adaptive Regulated Sparsity Promoting Approach for Data-Driven Modeling and Control of Grid-Connected Solar Photovoltaic Generation
Authors: Zhongtian Zhang, Javad Khazaei, Rick S. Blum
Date: 2024-04-29 00:00:00
Tasks: regression

This paper aims to introduce a new statistical learning technique based on sparsity promoting for data-driven modeling and control of solar photovoltaic (PV) systems. Compared with conventional sparse regression techniques that might introduce computational complexities when the number of candidate functions increases, an innovative algorithm, named adaptive regulated sparse regression (ARSR) is proposed that adaptively regulates the hyperparameter weights of candidate functions to best represent the dynamics of PV systems. Utilizing this algorithm, open-loop and closed-loop models of single-stage and two-stage PV systems are obtained from measurements and are utilized for control design purposes. Moreover, it is demonstrated that the proposed data-driven approach can successfully be employed for fault analysis studies, which distinguishes its capabilities compared with other data-driven techniques. Finally, the proposed approach is validated through real-time simulations.