pwc-archive/papers-with-abstracts huggingface dataset:
GNN-Based Joint Channel and Power Allocation in Heterogeneous Wireless Networks
Authors: Lili Chen, Jingge Zhu, Jamie Evans
Date: 2024-07-28 00:00:00
Tasks: Computational Efficiency, Graph Neural Network, Management

The optimal allocation of channels and power resources plays a crucial role in ensuring minimal interference, maximal data rates, and efficient energy utilisation. As a successful approach for tackling resource management problems in wireless networks, Graph Neural Networks (GNNs) have attracted a lot of attention. This article proposes a GNN-based algorithm to address the joint resource allocation problem in heterogeneous wireless networks. Concretely, we model the heterogeneous wireless network as a heterogeneous graph and then propose a graph neural network structure intending to allocate the available channels and transmit power to maximise the network throughput. Our proposed joint channel and power allocation graph neural network (JCPGNN) comprises a shared message computation layer and two task-specific layers, with a dedicated focus on channel and power allocation tasks, respectively. Comprehensive experiments demonstrate that the proposed algorithm achieves satisfactory performance but with higher computational efficiency compared to traditional optimisation algorithms.
A Comparison of Imitation Learning Algorithms for Bimanual Manipulation
Authors: Michael Drolet, Simon Stepputtis, Siva Kailas, Ajinkya Jain, Jan Peters, Stefan Schaal, Heni Ben Amor
Date: 2024-08-13 00:00:00
Tasks: Imitation Learning

Amidst the wide popularity of imitation learning algorithms in robotics, their properties regarding hyperparameter sensitivity, ease of training, data efficiency, and performance have not been well-studied in high-precision industry-inspired environments. In this work, we demonstrate the limitations and benefits of prominent imitation learning approaches and analyze their capabilities regarding these properties. We evaluate each algorithm on a complex bimanual manipulation task involving an over-constrained dynamics system in a setting involving multiple contacts between the manipulated object and the environment. While we find that imitation learning is well suited to solve such complex tasks, not all algorithms are equal in terms of handling environmental and hyperparameter perturbations, training requirements, performance, and ease of use. We investigate the empirical influence of these key characteristics by employing a carefully designed experimental procedure and learning environment. Paper website: https://bimanual-imitation.github.io/
Music2Latent: Consistency Autoencoders for Latent Audio Compression
Authors: Marco Pasini, Stefan Lattner, George Fazekas
Date: 2024-08-12 00:00:00
Tasks: Audio Compression, Information Retrieval, Music Information Retrieval

Efficient audio representations in a compressed continuous latent space are critical for generative audio modeling and Music Information Retrieval (MIR) tasks. However, some existing audio autoencoders have limitations, such as multi-stage training procedures, slow iterative sampling, or low reconstruction quality. We introduce Music2Latent, an audio autoencoder that overcomes these limitations by leveraging consistency models. Music2Latent encodes samples into a compressed continuous latent space in a single end-to-end training process while enabling high-fidelity single-step reconstruction. Key innovations include conditioning the consistency model on upsampled encoder outputs at all levels through cross connections, using frequency-wise self-attention to capture long-range frequency dependencies, and employing frequency-wise learned scaling to handle varying value distributions across frequencies at different noise levels. We demonstrate that Music2Latent outperforms existing continuous audio autoencoders in sound quality and reconstruction accuracy while achieving competitive performance on downstream MIR tasks using its latent representations. To our knowledge, this represents the first successful attempt at training an end-to-end consistency autoencoder model.
Extracting Urban Sound Information for Residential Areas in Smart Cities Using an End-to-End IoT System
Authors: Ee-Leng Tan, Furi Andi Karnapi, Linus Junjia Ng, Kenneth Ooi, Woon-Seng Gan
Date: 2024-08-11 00:00:00
Tasks: Descriptive

With rapid urbanization comes the increase of community, construction, and transportation noise in residential areas. The conventional approach of solely relying on sound pressure level (SPL) information to decide on the noise environment and to plan out noise control and mitigation strategies is inadequate. This paper presents an end-to-end IoT system that extracts real-time urban sound metadata using edge devices, providing information on the sound type, location and duration, rate of occurrence, loudness, and azimuth of a dominant noise in nine residential areas. The collected metadata on environmental sound is transmitted to and aggregated in a cloud-based platform to produce detailed descriptive analytics and visualization. Our approach to integrating different building blocks, namely, hardware, software, cloud technologies, and signal processing algorithms to form our real-time IoT system is outlined. We demonstrate how some of the sound metadata extracted by our system are used to provide insights into the noise in residential areas. A scalable workflow to collect and prepare audio recordings from nine residential areas to construct our urban sound dataset for training and evaluating a location-agnostic model is discussed. Some practical challenges of managing and maintaining a sensor network deployed at numerous locations are also addressed.
ADD 2023: Towards Audio Deepfake Detection and Analysis in the Wild
Authors: Jiangyan Yi, Chu Yuan Zhang, JianHua Tao, Chenglong Wang, Xinrui Yan, Yong Ren, Hao Gu, Junzuo Zhou
Date: 2024-08-09 00:00:00
Tasks: Audio Deepfake Detection, DeepFake Detection, Face Swapping

The growing prominence of the field of audio deepfake detection is driven by its wide range of applications, notably in protecting the public from potential fraud and other malicious activities, prompting the need for greater attention and research in this area. The ADD 2023 challenge goes beyond binary real/fake classification by emulating real-world scenarios, such as the identification of manipulated intervals in partially fake audio and determining the source responsible for generating any fake audio, both with real-life implications, notably in audio forensics, law enforcement, and construction of reliable and trustworthy evidence. To further foster research in this area, in this article, we describe the dataset that was used in the fake game, manipulation region location and deepfake algorithm recognition tracks of the challenge. We also focus on the analysis of the technical methodologies by the top-performing participants in each task and note the commonalities and differences in their approaches. Finally, we discuss the current technical limitations as identified through the technical analysis, and provide a roadmap for future research directions. The dataset is available for download at http://addchallenge.cn/downloadADD2023.
Assessing the Potential Impact of Direction-Dependent HRTF Selection on Sound Localization Accuracy
Authors: Sapir Goldring, Zamir Ben Hur, David Lou Alon, Boaz Rafaely
Date: 2024-08-08 00:00:00

This study investigates the approach of direction-dependent selection of Head-Related Transfer Functions (HRTFs) and its impact on sound localization accuracy. For applications such as virtual reality (VR) and teleconferencing, obtaining individualized HRTFs can be beneficial yet challenging, the objective of this work is therefore to assess whether incorporating HRTFs in a direction-dependent manner could improve localization precision without the need to obtain individualized HRTFs. A localization experiment conducted with a VR headset assessed localization errors, comparing an overall best HRTF from a set, against selecting the best HRTF based on average performance in each direction. The results demonstrate a substantial improvement in elevation localization error with the method motivated by direction-dependent HRTF selection, while revealing insignificant differences in azimuth errors.
A self-adaptive system of systems architecture to enable its ad-hoc scalability: Unmanned Vehicle Fleet -- Mission Control Center Case study
Authors: Ahmed R. Sadik, Bram Bolder, Pero Subasic
Date: 2024-08-01 00:00:00
Tasks: Management, Self Adaptive System

A System of Systems (SoS) comprises Constituent Systems (CSs) that interact to provide unique capabilities beyond any single CS. A key challenge in SoS is ad-hoc scalability, meaning the system size changes during operation by adding or removing CSs. This research focuses on an Unmanned Vehicle Fleet (UVF) as a practical SoS example, addressing uncertainties like mission changes, range extensions, and UV failures. The proposed solution involves a self-adaptive system that dynamically adjusts UVF architecture, allowing the Mission Control Center (MCC) to scale UVF size automatically based on performance criteria or manually by operator decision. A multi-agent environment and rule management engine were implemented to simulate and verify this approach.
Robust Model Predictive Control for Aircraft Intent-Aware Collision Avoidance
Authors: Arash Bahari Kordabad, Andrea Da Col, Arabinda Ghosh, Sybert Stroeve, Sadegh Soudjani
Date: 2024-08-13 00:00:00
Tasks: Collision Avoidance, Computational Efficiency, Model Predictive Control

This paper presents the use of robust model predictive control for the design of an intent-aware collision avoidance system for multi-agent aircraft engaged in horizontal maneuvering scenarios. We assume that information from other agents is accessible in the form of waypoints or destinations. Consequently, we consider that other agents follow their optimal Dubin's path--a trajectory that connects their current state to their intended state--while accounting for potential uncertainties. We propose using scenario tree model predictive control as a robust approach that demonstrates computational efficiency. We demonstrate that the proposed method can easily integrate intent information and offer a robust scheme that handles different uncertainties. The method is illustrated through simulation results.
Optimization-Based Model Checking and Trace Synthesis for Complex STL Specifications
Authors: Sota Sato, Jie An, Zhenya Zhang, Ichiro Hasuo
Date: 2024-08-13 00:00:00

We present a bounded model checking algorithm for signal temporal logic (STL) that exploits mixed-integer linear programming (MILP). A key technical element is our novel MILP encoding of the STL semantics; it follows the idea of stable partitioning from the recent work on SMT-based STL model checking. Assuming that our (continuous-time) system models can be encoded to MILP -- typical examples are rectangular hybrid automata (precisely) and hybrid dynamics with closed-form solutions (approximately) -- our MILP encoding yields an optimization-based model checking algorithm that is scalable, is anytime/interruptible, and accommodates parameter mining. Experimental evaluation shows our algorithm's performance advantages especially for complex STL formulas, demonstrating its practical relevance e.g. in the automotive domain.
Verification of Diagnosability for Cyber-Physical Systems: A Hybrid Barrier Certificate Approach
Authors: Bingzhuo Zhong, Weijie Dong, Xiang Yin, Majid Zamani
Date: 2024-08-13 00:00:00

Diagnosability is a system theoretical property characterizing whether fault occurrences in a system can always be detected within a finite time. In this paper, we investigate the verification of diagnosability for cyber-physical systems with continuous state sets. We develop an abstraction-free and automata-based framework to verify (the lack of) diagnosability, leveraging a notion of hybrid barrier certificates. To this end, we first construct a (delta,K)-deterministic finite automaton that captures the occurrence of faults targeted for diagnosis. Then, the verification of diagnosability property is converted into a safety verification problem over a product system between the automaton and the augmented version of the dynamical system. We demonstrate that this verification problem can be addressed by computing hybrid barrier certificates for the product system. To this end, we introduce two systematic methods, leveraging sum-of-squares programming and counter-example guided inductive synthesis to search for such certificates. Additionally, if the system is found to be diagnosable, we propose methodologies to construct a diagnoser to identify fault occurrences online. Finally, we showcase the effectiveness of our methods through a case study.
On the Improvement of the Performance of Inexpensive Electromagnetic Skins by means of an Inverse Source Design Approach
Authors: Giacomo Oliveri, Francesco Zardi, Andrea Massa
Date: 2024-08-13 00:00:00

A new methodology for the improvement of the performance of inexpensive static passive electromagnetic skins (SP-EMSs) is presented. The proposed approach leverages on the non-uniqueness of the inverse source problem associated to the SP-EMS design by decomposing the induced surface current into pre-image (PI) and null-space (NS) components. Successively, the unknown EMS layout and NS expansion coefficients are determined by means of an alternate minimization of a suitable cost function. This latter quantifies the mismatch between the ideal surface current, which radiates the user-defined target field, and that actually induced on the EMS layout. Results from a representative set of numerical experiments, concerned with the design of EMSs reflecting pencil-beam as well as contoured target patterns, are reported to assess the feasibility and the effectiveness of the proposed method in improving the performance of inexpensive EMS realizations. The measurements on an EMS prototype, featuring a conductive ink pattern printed on a standard paper substrate, are also shown to prove the reliability of the synthesis process.
Synthesis of Wide-Angle Scanning Arrays through Array Power Control
Authors: Pietro Rosatti, Giacomo Oliveri, Andrea Massa
Date: 2024-08-13 00:00:00

A new methodology for the synthesis of wide-angle scanning arrays is proposed. It is based on the formulation of the array design problem as a multi-objective one where, for each scan angle, both the radiated power density in the scan direction and the total reflected power are accounted for. A set of numerical results from full-wave simulated examples - dealing with different radiators, arrangements, frequencies, and number of elements - is reported to show the features of the proposed approach as well as to assess its potentialities.
Residual Deep Reinforcement Learning for Inverter-based Volt-Var Control
Authors: Qiong Liu, Ye Guo, Lirong Deng, Haotian Liu, Dongyu Li, Hongbin Sun
Date: 2024-08-13 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning

A residual deep reinforcement learning (RDRL) approach is proposed by integrating DRL with model-based optimization for inverter-based volt-var control in active distribution networks when the accurate power flow model is unknown. RDRL learns a residual action with a reduced residual action space, based on the action of the model-based approach with an approximate model. RDRL inherits the control capability of the approximate-model-based optimization and enhances the policy optimization capability by residual policy learning. Additionally, it improves the approximation accuracy of the critic and reduces the search difficulties of the actor by reducing residual action space. To address the issues of "too small" or "too large" residual action space of RDRL and further improve the optimization performance, we extend RDRL to a boosting RDRL approach. It selects a much smaller residual action space and learns a residual policy by using the policy of RDRL as a base policy. Simulations demonstrate that RDRL and boosting RDRL improve the optimization performance considerably throughout the learning stage and verify their rationales point-by-point, including 1) inheriting the capability of the approximate model-based optimization, 2) residual policy learning, and 3) learning in a reduced action space.
Designing Consensus-Based Distributed Filtering over Directed Graphs
Authors: Xiaoxu Lyu, Guanghui Wen, Yuezu Lv, Zhisheng Duan, Ling Shi
Date: 2024-08-13 00:00:00

This paper proposes a novel consensus-on-only-measurement distributed filter over directed graphs under the collectively observability condition. First, the distributed filter structure is designed with an augmented leader-following measurement fusion strategy. Subsequently, two parameter design methods are presented, and the consensus gain parameter is devised utilizing local information exclusively rather than global information. Additionally, the lower bound of the fusion step is derived to guarantee a uniformly upper bound of the estimation error covariance. Moreover, the lower bounds of the convergence rates of the steady-state performance gap between the proposed algorithm and the centralized filter are provided with the fusion step approaching infinity. The analysis demonstrates that the convergence rate is, at a minimum, as rapid as exponential convergence under the spectral norm condition of the communication graph. The transient performance is also analyzed with the fusion step tending to infinity. The inherent trade-off between the communication cost and the filtering performance is revealed from the analysis of the steady-state performance and the transient performance. Finally, the theoretical results are substantiated through the validation of two simulation examples.
On the Effects of Modeling Errors on Distributed Continuous-time Filtering
Authors: Xiaoxu Lyu, Shilei Li, Dawei Shi, Ling Shi
Date: 2024-08-13 00:00:00

This paper offers a comprehensive performance analysis of the distributed continuous-time filtering in the presence of modeling errors. First, we introduce two performance indices, namely the nominal performance index and the estimation error covariance. By leveraging the nominal performance index and the Frobenius norm of the modeling deviations, we derive the bounds of the estimation error covariance and the lower bound of the nominal performance index. Specifically, we reveal the effect of the consensus parameter on both bounds. We demonstrate that, under specific conditions, an incorrect process noise covariance can lead to the divergence of the estimation error covariance. Moreover, we investigate the properties of the eigenvalues of the error dynamical matrix. Furthermore, we explore the magnitude relations between the nominal performance index and the estimation error covariance. Finally, we present some numerical simulations to validate the effectiveness of the theoretical results.
A hybrid neural network for real-time OD demand calibration under disruptions
Authors: Takao Dantsuji, Dong Ngoduy, Ziyuan Pu, Seunghyeon Lee, Hai L. Vu
Date: 2024-08-13 00:00:00
Tasks: Computational Efficiency, Management

Existing automated urban traffic management systems, designed to mitigate traffic congestion and reduce emissions in real time, face significant challenges in effectively adapting to rapidly evolving conditions. Predominantly reactive, these systems typically respond to incidents only after they have transpired. A promising solution lies in implementing real-time traffic simulation models capable of accurately modelling environmental changes. Central to these real-time traffic simulations are origin-destination (OD) demand matrices. However, the inherent variability, stochasticity, and unpredictability of traffic demand complicate the precise calibration of these matrices in the face of disruptions. This paper introduces a hybrid neural network (NN) architecture specifically designed for real-time OD demand calibration to enhance traffic simulations' accuracy and reliability under both recurrent and non-recurrent traffic conditions. The proposed hybrid NN predicts the OD demand to reconcile the discrepancies between actual and simulated traffic patterns. To facilitate real-time updating of the internal parameters of the NN, we develop a metamodel-based backpropagation method by integrating data from real-world traffic systems and simulated environments. This ensures precise predictions of the OD demand even in the case of abnormal or unpredictable traffic patterns. Furthermore, we incorporate offline pre-training of the NN using the metamodel to improve computational efficiency. Validation through a toy network and a Tokyo expressway corridor case study illustrates the model's ability to dynamically adjust to shifting traffic patterns across various disruption scenarios. Our findings underscore the potential of advanced machine learning techniques in developing proactive traffic management strategies, offering substantial improvements over traditional reactive systems.
Physics-Informed Kolmogorov-Arnold Networks for Power System Dynamics
Authors: Hang Shuai, Fangxing Li
Date: 2024-08-13 00:00:00
Tasks: Kolmogorov-Arnold Networks

This paper presents, for the first time, a framework for Kolmogorov-Arnold Networks (KANs) in power system applications. Inspired by the recently proposed KAN architecture, this paper proposes physics-informed Kolmogorov-Arnold Networks (PIKANs), a novel KAN-based physics-informed neural network (PINN) tailored to efficiently and accurately learn dynamics within power systems. The PIKANs present a promising alternative to conventional Multi-Layer Perceptrons (MLPs) based PINNs, achieving superior accuracy in predicting power system dynamics while employing a smaller network size. Simulation results on a single-machine infinite bus system and a 4-bus 2- generator system underscore the accuracy of the PIKANs in predicting rotor angle and frequency with fewer learnable parameters than conventional PINNs. Furthermore, the simulation results demonstrate PIKANs capability to accurately identify uncertain inertia and damping coefficients. This work opens up a range of opportunities for the application of KANs in power systems, enabling efficient determination of grid dynamics and precise parameter identification.
Mathematical Optimization of Resolution Improvement in Structured Light data by Periodic Scanning Motion: Application for Feedback during Lunar Landing
Authors: Tarek A. Elsharhawy, P. James Schuck, Shuo Liu, Luc Saikali
Date: 2024-08-13 00:00:00

This research explores the enhancement of lunar landing precision through an advanced structured light system, integrating machine learning, Iterative Learning Control (ILC) and Structured Illumination Microscopy (SIM) techniques. By employing Moire fringe patterns for high-precision scanning maneuvers, the study addresses the limitations of conventional structured light systems. A nonlinear mathematical optimization model is developed to refine the world model, optimizing oscillation frequency and amplitude to improve resolution. The findings suggest that this approach can double the conventional resolution, promising significant advancements in the accuracy of lunar landings, with potential real-time application.