pwc-archive/papers-with-abstracts huggingface dataset:
Optimizing tiny colorless feedback delay networks
Authors: Gloria Dal Santo, Karolina Prawda, Sebastian J. Schlecht, Vesa Välimäki
Date: 2024-02-17 00:00:00

A common bane of artificial reverberation algorithms is spectral coloration in the synthesized sound, typically manifesting as metallic ringing, leading to a degradation in the perceived sound quality. In delay network methods, coloration is more pronounced when fewer delay lines are used. This paper presents an optimization framework in which a tiny differentiable feedback delay network, with as few as four delay lines, is used to learn a set of parameters to iteratively reduce coloration. The parameters under optimization include the feedback matrix, as well as the input and output gains. The optimization objective is twofold: to maximize spectral flatness through a spectral loss while maintaining temporal density by penalizing sparseness in the parameter values. A favorable narrow distribution of modal excitation is achieved while maintaining the desired impulse response density. In a subjective assessment, the new method proves effective in reducing perceptual coloration of late reverberation. Compared to the author's previous work, which serves as the baseline and utilizes a sparsity loss in the time domain, the proposed method achieves computational savings while maintaining performance. The effectiveness of this work is demonstrated through two application scenarios where smooth-sounding synthetic room impulse responses are obtained via the introduction of attenuation filters and an optimizable scattering feedback matrix.
A Semantic Social Network Analysis Tool for Sensitivity Analysis and What-If Scenario Testing in Alcohol Consumption Studies
Authors: José Alberto Benítez-Andrades, Alejandro Rodríguez-González, Carmen Benavides, Leticia Sánchez-Valdeón, Isaías García
Date: 2024-02-14 00:00:00
Tasks: Sensitivity

Social Network Analysis (SNA) is a set of techniques developed in the field of social and behavioral sciences research, in order to characterize and study the social relationships that are established among a set of individuals. When building a social network for performing an SNA analysis, an initial process of data gathering is achieved in order to extract the characteristics of the individuals and their relationships. This is usually done by completing a questionnaire containing different types of questions that will be later used to obtain the SNA measures needed to perform the study. There are, then, a great number of different possible network generating questions and also many possibilities for mapping the responses to the corresponding characteristics and relationships. Many variations may be introduced into these questions (the way they are posed, the weights given to each of the responses, etc.) that may have an effect on the resulting networks. All these different variations are difficult to achieve manually, because the process is time-consuming and error prone. The tool described in this paper uses semantic knowledge representation techniques in order to facilitate this kind of sensitivity studies. The base of the tool is a conceptual structure, called "ontology" that is able to represent the different concepts and their definitions. The tool is compared to other similar ones, and the advantages of the approach are highlighted, giving some particular examples from an ongoing SNA study about alcohol consumption habits in adolescents.
News Source Credibility Assessment: A Reddit Case Study
Authors: Arash Amini, Yigit Ege Bayiz, Ashwin Ram, Radu Marculescu, Ufuk Topcu
Date: 2024-02-07 00:00:00
Tasks: Binary Classification, Misinformation

In the era of social media platforms, identifying the credibility of online content is crucial to combat misinformation. We present the CREDiBERT (CREDibility assessment using Bi-directional Encoder Representations from Transformers), a source credibility assessment model fine-tuned for Reddit submissions focusing on political discourse as the main contribution. We adopt a semi-supervised training approach for CREDiBERT, leveraging Reddit's community-based structure. By encoding submission content using CREDiBERT and integrating it into a Siamese neural network, we significantly improve the binary classification of submission credibility, achieving a 9% increase in F1 score compared to existing methods. Additionally, we introduce a new version of the post-to-post network in Reddit that efficiently encodes user interactions to enhance the binary classification task by nearly 8% in F1 score. Finally, we employ CREDiBERT to evaluate the susceptibility of subreddits with respect to different topics.
Robust Model Predictive Control for nonlinear discrete-time systems using iterative time-varying constraint tightening
Authors: Daniel D. Leister, Justin P. Koeln
Date: 2024-02-20 00:00:00
Tasks: Diversity, Model Predictive Control

Robust Model Predictive Control (MPC) for nonlinear systems is a problem that poses significant challenges as highlighted by the diversity of approaches proposed in the last decades. Often compromises with respect to computational load, conservatism, generality, or implementation complexity have to be made, and finding an approach that provides the right balance is still a challenge to the research community. This work provides a contribution by proposing a novel shrinking-horizon robust MPC formulation for nonlinear discrete-time systems. By explicitly accounting for how disturbances and linearization errors are propagated through the nonlinear dynamics, a constraint tightening-based formulation is obtained, with guarantees of robust constraint satisfaction. The proposed controller relies on iteratively solving a Nonlinear Program (NLP) to simultaneously optimize system operation and the required constraint tightening. Numerical experiments show the effectiveness of the proposed controller with three different choices of NLP solvers as well as significantly improved computational speed, better scalability, and generally reduced conservatism when compared to an existing technique from the literature.
Electric Field Evaluation of Reconfigurable Intelligent Surface in Wireless Networks
Authors: Zhuangzhuang Cui, Franco Minucci, Rizqi Hersyandika, Rodney Martinez Alonso, Andrea P. Guevara, Hazem Sallouha, Sofie Pollin
Date: 2024-02-20 00:00:00

Reconfigurable intelligent surface (RIS) used as infrastructure in wireless networks has been a trend, thanks to its low cost and high flexibility. Working in many ways including reflective mirrors and phase-shifted surfaces, RIS is able to enhance the coverage in communications and provide more degrees of freedom for sensing. However, the key issue lies in how to place RIS in accordance with the regulations for electromagnetic field (EMF) exposure, which requires refined evaluations. In this paper, we first investigate the regulations in terms of E-field. Then, relevant deployment characteristics are evaluated jointly: the minimum distance from the base station (BS) to the RIS, and the minimum height of the RIS are given for a given BS power limit and as function of the number of RIS elements. The ray-tracing simulations verify the correctness of our analysis. Besides, different frequency ranges (FRs) and radiation patterns of RIS elements are investigated. The results show that the EMF exposure risk is negligible when RIS works in the reflective-only (RO) mode. However, when it works in the beamforming (BO) mode, its placement should be well specified based on our analytical framework to comply with the regulations of E-field limit in general public scenarios. Finally, we provide an E-field measurement methodology and low-cost solutions in terms of general wireless networks and 5G standalone networks, which pave the way for real-world evaluation in future work.
Gimbal Actuator Modeling for a Spin-Stabilized Spacecraft Equipped with a 1DoF Gimbaled-Thruster and two Reaction Wheels
Authors: Hamed Kouhi, Mansour Kabganian, Farhad Fani Saberi, Fatemeh Ghorbani
Date: 2024-02-20 00:00:00

Attitude control of spacecraft during an impulsive orbital maneuver is a vital task. Many spacecraft and launchers use the gimbaled thrust vector control (TVC) in their attitude control system during an orbital maneuver. Mathematical modeling of the gimbal actuator is an important task because we should show the applicability of the gimbaled-TVC in a spacecraft. In this paper, a spin-stabilized spacecraft equipped with one degree of freedom (DoF) gimbaled-thruster and two reaction wheels (RWs) is considered. The control goals are disturbance rejection and thrust vector (spin-axis) stabilization based on one DoF gimbal actuator and two RWs. The gimbal is assumed to be equipped with a gearbox and a DC electric motor. This actuator must supply the gimbal torque to rotate the spacecraft nozzle. The mathematical model of the mentioned spacecraft is extended with respect to the DC motor equations. In order to investigate the applicability of the proposed method, an industrial DC electric motor is considered for the gimbal actuator. The simulation results prove that an industrial DC electric motor is able to be used for attitude control of the mentioned spacecraft. The simulation results indicate the applicability of the proposed control method in an impulsive orbital maneuver.
Clifford Theory: A Geometrical Interpretation of Multivectorial Apparent Power
Authors: M. Castilla, Juan Carlos Bravo, M. Ordoñez, Juan Carlos Montaño
Date: 2024-02-20 00:00:00

In this paper, a generalization of the concept of electrical power for periodic current and voltage waveforms based on a new generalized complex geometric algebra (GCGA) is proposed. This powerful tool permits, in n-sinusoidal/nonlinear situations, representing and calculating the voltage, current, and apparent power in a single-port electrical network in terms of multivectors. The new expressions result in a novel representation of the apparent power, similar to the Steinmetz's phasor model, based on complex numbers, but limited to the purely sinusoidal case. The multivectorial approach presented is based on the frequency-domain decomposition of the apparent power into three components: the real part and the imaginary part of the complex-scalar associated to active and reactive power respectively, and distortion power, associated to the complex-bivector. A geometrical interpretation of the multivectorial components of apparent power is discussed. Numerical examples illustrate the clear advantages of the suggested approach.
ASCEND: Accurate yet Efficient End-to-End Stochastic Computing Acceleration of Vision Transformer
Authors: Tong Xie, Yixuan Hu, Renjie Wei, Meng Li, YuAn Wang, Runsheng Wang, Ru Huang
Date: 2024-02-20 00:00:00

Stochastic computing (SC) has emerged as a promising computing paradigm for neural acceleration. However, how to accelerate the state-of-the-art Vision Transformer (ViT) with SC remains unclear. Unlike convolutional neural networks, ViTs introduce notable compatibility and efficiency challenges because of their nonlinear functions, e.g., softmax and Gaussian Error Linear Units (GELU). In this paper, for the first time, a ViT accelerator based on end-to-end SC, dubbed ASCEND, is proposed. ASCEND co-designs the SC circuits and ViT networks to enable accurate yet efficient acceleration. To overcome the compatibility challenges, ASCEND proposes a novel deterministic SC block for GELU and leverages an SC-friendly iterative approximate algorithm to design an accurate and efficient softmax circuit. To improve inference efficiency, ASCEND develops a two-stage training pipeline to produce accurate low-precision ViTs. With extensive experiments, we show the proposed GELU and softmax blocks achieve 56.3% and 22.6% error reduction compared to existing SC designs, respectively and reduce the area-delay product (ADP) by 5.29x and 12.6x, respectively. Moreover, compared to the baseline low-precision ViTs, ASCEND also achieves significant accuracy improvements on CIFAR10 and CIFAR100.
Smart Mobility Digital Twin Based Automated Vehicle Navigation System: A Proof of Concept
Authors: Kui Wang, Zongdian Li, Kazuma Nonomura, Tao Yu, Kei Sakaguchi, Omar Hashash, Walid Saad
Date: 2024-02-20 00:00:00
Tasks: Autonomous Driving, Blocking

Digital twins (DTs) have driven major advancements across various industrial domains over the past two decades. With the rapid advancements in autonomous driving and vehicle-to-everything (V2X) technologies, integrating DTs into vehicular platforms is anticipated to further revolutionize smart mobility systems. In this paper, a new smart mobility DT (SMDT) platform is proposed for the control of connected and automated vehicles (CAVs) over next-generation wireless networks. In particular, the proposed platform enables cloud services to leverage the abilities of DTs to promote the autonomous driving experience. To enhance traffic efficiency and road safety measures, a novel navigation system that exploits available DT information is designed. The SMDT platform and navigation system are implemented with state-of-the-art products, e.g., CAVs and roadside units (RSUs), and emerging technologies, e.g., cloud and cellular V2X (C-V2X). In addition, proof-of-concept (PoC) experiments are conducted to validate system performance. The performance of SMDT is evaluated from two standpoints: (i) the rewards of the proposed navigation system on traffic efficiency and safety and, (ii) the latency and reliability of the SMDT platform. Our experimental results using SUMO-based large-scale traffic simulations show that the proposed SMDT can reduce the average travel time and the blocking probability due to unexpected traffic incidents. Furthermore, the results record a peak overall latency for DT modeling and route planning services to be 155.15 ms and 810.59 ms, respectively, which validates that our proposed design aligns with the 3GPP requirements for emerging V2X use cases and fulfills the targets of the proposed design. Our demonstration video can be found at https://youtu.be/3waQwlaHQkk.
Antifragile Perimeter Control: Anticipating and Gaining from Disruptions with Reinforcement Learning
Authors: Linghang Sun, Michail A. Makridis, Alexander Genser, Cristian Axenie, Margherita Grossi, Anastasios Kouvelas
Date: 2024-02-20 00:00:00
Tasks: Deep Reinforcement Learning, Model Predictive Control, Reinforcement Learning (RL)

The optimal operation of transportation systems is often susceptible to unexpected disruptions, such as traffic accidents and social events. Many established control strategies relying on mathematical models can struggle with real-world disruptions, leading to a significant deviation from their anticipated efficiency. This work applies the cutting-edge concept of antifragility to design a traffic control strategy for urban road networks against disruptions. Antifragility sets itself apart from robustness, resilience, and reliability as it represents a system's ability to not only withstand stressors, shocks, and volatility but also to thrive and enhance performance in the presence of such adversarial events. Incorporating antifragile terms composed of traffic state derivatives and redundancy, a model-free deep reinforcement learning algorithm is developed and subsequently evaluated in a two-region cordon-shaped urban traffic perimeter network. Promising results highlight (a) the superior performance of the proposed algorithm compared to the state-of-the-art methods under incremental magnitude of disruptions, (b) distribution skewness as the antifragility indicator demonstrating its relative antifragility, and (c) its effectiveness under limited observability due to real-world data availability constraints. The proposed antifragile methodology is generalizable and holds potential for application beyond perimeter control, offering integration into systems exposed to disruptions across various disciplines.
Model Predictive Control Design for Unlocking the Energy Flexibility of Heat Pump and Thermal Energy Storage Systems
Authors: Weihong Tang, Yun Li, Shalika Walker, Tamas Keviczky
Date: 2024-02-19 00:00:00
Tasks: Management, Model Predictive Control

Heat pump and thermal energy storage (HPTES) systems, which are widely utilized in modern buildings for providing domestic hot water, contribute to a large share of household electricity consumption. With the increasing integration of renewable energy sources (RES) into modern power grids, demand-side management (DSM) becomes crucial for balancing power generation and consumption by adjusting end users' power consumption. This paper explores an energy flexible Model Predictive Control (MPC) design for a class of HPTES systems to facilitate demand-side management. The proposed DSM strategy comprises two key components: i) flexibility assessment, and ii) flexibility exploitation. Firstly, for flexibility assessment, a tailored MPC formulation, supplemented by a set of auxiliary linear constraints, is developed to quantitatively assess the flexibility potential inherent in HPTES systems. Subsequently, in flexibility exploitation, the energy flexibility is effectively harnessed in response to feasible demand response (DR) requests, which can be formulated as a standard mixed-integer MPC problem. Numerical experiments, based on a real-world HPTES installation, are conducted to demonstrate the efficacy of the proposed design.
Single and Multi-Objective Real-Time Optimisation of an Industrial Injection Moulding Process via a Bayesian Adaptive Design of Experiment Approach
Authors: Mandana Kariminejad, David Tormey, Caitríona Ryan, Christopher O'Hara, Albert Weinert, Marion McAfee
Date: 2024-02-19 00:00:00
Tasks: Bayesian Optimisation

Minimising cycle time without inducing quality defects is a major challenge in the injection moulding (IM). Design of Experiment methods (DoE) have been widely studied for optimisation of the IM, however existing methods have limitations, including the need for a large number of experiments and a pre-determined search space. Bayesian adaptive design of experiment (ADoE) is an iterative process where the results of the previous experiments are used to make an informed selection for the next design. In this study, for the first time, an experimental ADoE approach, based on Bayesian optimisation, was developed in injection moulding using process and sensor data to optimise the quality and cycle time in real-time. A novel approach for the real-time characterisation of post-production shrinkage was introduced, utilising in-mould sensor data on temperature differential during part cooling. This characterisation approach was verified by post-production metrology results. A single and multi-objective optimisation of the cycle time and temperature differential in an injection moulded component is proposed. The multi-objective optimisation techniques, composite desirability function and Nondominated Sorting Genetic Algorithm (NSGA-II) using Response Surface Methodology (RSM) model, are compared with the real-time novel ADoE approach. ADoE achieved almost a 50% reduction in the number of experiments required for the single optimisation of temperature differential, and an almost 30% decrease for the optimisation of temperature differential and cycle time together compared to composite desirability function and NSGA-II. Also, the optimal settings identified by ADoE for multiobjective optimisation were similar to the selected Pareto optimal solution found by the NSGA-II.
Enhancing Power Prediction of Photovoltaic Systems: Leveraging Dynamic Physical Model for Irradiance-to-Power Conversion
Authors: Baojie Li, Xin Chen, Anubhav Jain
Date: 2024-02-19 00:00:00
Tasks: parameter estimation

Power prediction is crucial to the efficiency and reliability of Photovoltaic (PV) systems. For the model-chain-based (also named indirect or physical) power prediction, the conversion of ground environmental data (plane-of-array irradiance and module temperature) to the output power is a fundamental step, commonly accomplished through physical modeling. The core of the physical model lies in the parameters. However, traditional parameter estimation either relies on datasheet information that cannot reflect the system's current health status or necessitates additional I-V characterization of the entire array, which is not commonly available. To address this, our paper introduces PVPro, a dynamic physical modeling method for irradiance-to-power conversion. It extracts model parameters from the recent production data without requiring I-V curve measurements. This dynamic model, periodically-updated (as short as daily), can closely capture the actual health status, enabling precise power estimation. To evaluate the performance, PVPro is compared with the smart persistence, nominal physical, and various machine learning models for day-ahead power prediction. The results indicate that PVPro achieves an outstanding power estimation performance with the average nMAE =1.4% across four field PV systems, reducing the error by 17.6% compared to the best of other techniques. Furthermore, PVPro demonstrates robustness across different seasons and weather conditions. More importantly, PVPro can also perform well with a limited amount of historical production data (3 days), rendering it applicable for new PV systems. The tool is available as a Python package at: https://github.com/DuraMAT/pvpro.
Terahertz User-Centric Clustering in the Presence of Beam Misalignment
Authors: Khaled Humadi, Imene Trigui, Wei-Ping Zhu, Wessam Ajib
Date: 2024-02-19 00:00:00
Tasks: Clustering

Beam misalignment is one of the main challenges for the design of reliable wireless systems in terahertz (THz) bands. This paper investigates how to apply user-centric base station (BS) clustering as a valuable add-on in THz networks. In particular, to reduce the impact of beam misalignment, a user-centric BS clustering design that provides multi-connectivity via BS cooperation is investigated. The coverage probability is derived by leveraging an accurate approximation of the aggregate interference distribution that captures the effect of beam misalignment and THz fading. The numerical results reveal the impact of beam misalignment with respect to crucial link parameters, such as the transmitter's beam width and the serving cluster size, demonstrating that user-centric BS clustering is a promising enabler of THz networks.
Autonomous Hyperspectral Characterisation Station: Robotically Assisted Characterisation of Polymer Degradation
Authors: Shayan Azizi, Ehsan Asadi, Shaun Howard, Benjamin W. Muir, Riley O'Shea, Alireza Bab-Hadiashar
Date: 2024-02-19 00:00:00

This paper addresses the gap between the capabilities and utilisation of robotics and automation in laboratory settings and builds upon the concept of Self Driving Labs (SDL). %to significantly impact laboratory operations. We introduce an innovative approach to the temporal characterisation of materials. The article discusses the challenges posed by manual methods involving established laboratory equipment and presents an automated hyperspectral characterisation station. This station integrates robot-aided hyperspectral imaging, complex material characterisation modeling, and automated data analysis, offering a non-destructive and comprehensive approach. This work explains how the proposed assembly can automatically measure the half-life of biodegradable polymers with higher throughput and accuracy than manual methods. The investigation explores the effect of pH, number of average molecular weight (Mn), end groups, and blends on the degradation rate of polylactic acid (PLA). The contributions of the paper lie in introducing an adaptable classification station for novel characterisation methods and presenting an innovative methodology for polymer degradation rate measurement. The proposed system has the potential to accelerate the development of high-throughput screening and characterisation methods in material and chemistry laboratories.