pwc-archive/papers-with-abstracts huggingface dataset:
Source Tracing of Audio Deepfake Systems
Authors: Nicholas Klein, Tianxiang Chen, Hemlata Tak, Ricardo Casal, Elie Khoury
Date: 2024-07-10 00:00:00
Tasks: Face Swapping, text-to-speech, Text to Speech, Voice Conversion

Recent progress in generative AI technology has made audio deepfakes remarkably more realistic. While current research on anti-spoofing systems primarily focuses on assessing whether a given audio sample is fake or genuine, there has been limited attention on discerning the specific techniques to create the audio deepfakes. Algorithms commonly used in audio deepfake generation, like text-to-speech (TTS) and voice conversion (VC), undergo distinct stages including input processing, acoustic modeling, and waveform generation. In this work, we introduce a system designed to classify various spoofing attributes, capturing the distinctive features of individual modules throughout the entire generation pipeline. We evaluate our system on two datasets: the ASVspoof 2019 Logical Access and the Multi-Language Audio Anti-Spoofing Dataset (MLAAD). Results from both experiments demonstrate the robustness of the system to identify the different spoofing attributes of deepfake generation systems.
Commute-Time-Optimised Graphs for GNNs
Authors: Igor Sterner, Shiye Su, Petar Veličković
Date: 2024-07-09 00:00:00

We explore graph rewiring methods that optimise commute time. Recent graph rewiring approaches facilitate long-range interactions in sparse graphs, making such rewirings commute-time-optimal on average. However, when an expert prior exists on which node pairs should or should not interact, a superior rewiring would favour short commute times between these privileged node pairs. We construct two synthetic datasets with known priors reflecting realistic settings, and use these to motivate two bespoke rewiring methods that incorporate the known prior. We investigate the regimes where our rewiring improves test performance on the synthetic datasets. Finally, we perform a case study on a real-world citation graph to investigate the practical implications of our work.
A Reflective LLM-based Agent to Guide Zero-shot Cryptocurrency Trading
Authors: Yuan Li, Bingqiao Luo, Qian Wang, Nuo Chen, Xu Liu, Bingsheng He
Date: 2024-06-27 00:00:00

The utilization of Large Language Models (LLMs) in financial trading has primarily been concentrated within the stock market, aiding in economic and financial decisions. Yet, the unique opportunities presented by the cryptocurrency market, noted for its on-chain data's transparency and the critical influence of off-chain signals like news, remain largely untapped by LLMs. This work aims to bridge the gap by developing an LLM-based trading agent, CryptoTrade, which uniquely combines the analysis of on-chain and off-chain data. This approach leverages the transparency and immutability of on-chain data, as well as the timeliness and influence of off-chain signals, providing a comprehensive overview of the cryptocurrency market. CryptoTrade incorporates a reflective mechanism specifically engineered to refine its daily trading decisions by analyzing the outcomes of prior trading decisions. This research makes two significant contributions. Firstly, it broadens the applicability of LLMs to the domain of cryptocurrency trading. Secondly, it establishes a benchmark for cryptocurrency trading strategies. Through extensive experiments, CryptoTrade has demonstrated superior performance in maximizing returns compared to traditional trading strategies and time-series baselines across various cryptocurrencies and market conditions. Our code and data are available at \url{https://anonymous.4open.science/r/CryptoTrade-Public-92FC/}.
Nonlinear Noise Mechanisms in Active Devices: Additive Amplitude Noise and Phase Noise
Authors: Meysam Bahmanian
Date: 2024-07-15 00:00:00

In this report, we lay the foundation for amplitude noise and phase noise analysis in nonlinear devices. We build a theoretical framework that helps us to analyze extremely difficult problems which include both nonlinearity and noise of semiconductors. Using our proposed framework, we analytically calculate the amplitude noise and phase noise of nonlinear RC circuits in the presence of nonlinear capacitance and conductance. As a more practical example, we analyze the amplitude noise and phase noise of a common-emitter stage bipolar transistor.
Quantifying distribution system resilience from utility data: large event risk and benefits of investments
Authors: Arslan Ahmad, Ian Dobson
Date: 2024-07-15 00:00:00

We focus on large blackouts in electric distribution systems caused by extreme winds. Such events have a large cost and impact on customers. To quantify resilience to these events, we formulate large event risk and show how to calculate it from the historical outage data routinely collected by utilities' outage management systems. Risk is defined using an event cost exceedance curve. The tail of this curve and the large event risk is described by the probability of a large cost event and the slope magnitude of the tail on a log-log plot. Resilience can be improved by planned investments to upgrade system components or speed up restoration. The benefits that these investments would have had if they had been made in the past can be quantified by "rerunning history" with the effects of the investment included, and then recalculating the large event risk to find the improvement in resilience. An example using utility data shows a 12% and 22% reduction in the probability of a large cost event due to 10% wind hardening and 10% faster restoration respectively. This new data-driven approach to quantify resilience and resilience investments is realistic and much easier to apply than complicated approaches based on modeling all the phases of resilience. Moreover, an appeal to improvements to past lived experience may well be persuasive to customers and regulators in making the case for resilience investments.
Assessment of Continuous-Time Transmission-Distribution-Interface Active and Reactive Flexibility for Flexible Distribution Networks
Authors: Shuo Yang, Zhengshuo Li, Ye Tian
Date: 2024-07-15 00:00:00

With the widespread use of power electronic devices, modern distribution networks are turning into flexible distribution networks (FDNs), which have enhanced active and reactive power flexibility at the transmission-distribution-interface (TDI). However, owing to the stochastics and volatility of distributed generation, the flexibility can change in real time and can hardly be accurately captured using conventional discrete-time (DT) assessment methods. This paper first proposes the notion of continuous-time (CT) TDI active and reactive flexibility and establishes its mathematical model. This model comprehensively considers the flexible devices in the FDN and the impact of uncertainty of photovoltaic power generation and load. In particular, a novel direction-factor-based metric is proposed to model CT-TDI PQ flexibility. Moreover, an efficient solution method is designed to address the difficulties in handling the infinite dimension of CT model and the complexity of bi-objectivity from assessing both active and reactive flexibility to be assessed. The solution successfully transforms the infinite dimensional optimization into a finite dimensional problem and effectively explores the PQ plane in a parallel pattern. Case studies show that the method can more effectively assess the real-time TDI flexibility of an FDN relative to conventional DT counterparts, and also reveals the impact of the relevant factors, such as penetrations of flexible devices and levels of uncertainty.
Distributed Charging Coordination for Electric Trucks under Limited Facilities and Travel Uncertainties
Authors: Ting Bai, Yuchao Li, Andreas A. Malikopoulos, Karl Henrik Johansson, Jonas Mårtensson
Date: 2024-07-14 00:00:00

In this work, we address the problem of charging coordination between electric trucks and charging stations. The problem arises from the tension between the trucks' nontrivial charging times and the stations' limited charging facilities. Our goal is to reduce the trucks' waiting times at the stations while minimizing individual trucks' operational costs. We propose a distributed coordination framework that relies on computation and communication between the stations and the trucks, and handles uncertainties in travel times and energy consumption. Within the framework, the stations assign a limited number of charging ports to trucks according to the first-come, first-served rule. In addition, each station constructs a waiting time forecast model based on its historical data and provides its estimated waiting times to trucks upon request. When approaching a station, a truck sends its arrival time and estimated arrival-time windows to the nearby station and the distant stations, respectively. The truck then receives the estimated waiting times from these stations in response, and updates its charging plan accordingly while accounting for travel uncertainties. We performed simulation studies for $1,000$ trucks traversing the Swedish road network for $40$ days, using realistic traffic data with travel uncertainties. The results show that our method reduces the average waiting time of the trucks by $46.1\%$ compared to offline charging plans computed by the trucks without coordination and update, and by $33.8\%$ compared to the coordination scheme assuming zero waiting times at distant stations.
Strategies for Resilience and Battery Life Extension in the Face of Communication Losses for Isolated Microgrids
Authors: Mohammad Hossein Nejati Amiri, Fawaz Annaz, Mario De Oliveira, Florimond Gueniat
Date: 2024-07-14 00:00:00
Tasks: energy management, Management, Model Predictive Control

This study addresses the challenges of energy deficiencies and high impact low probability (HILP) events in modern electrical grids by developing resilient microgrid energy management strategies. It introduces a sliding Model Predictive Control (MPC) methodology integrated with Battery Energy Storage Systems (BESS), emphasizing extending battery life and prioritizing critical loads during HILP events. This approach focuses on extending the sustainability of battery operation by linearizing the battery lifecycle within the optimization framework. Furthermore, this research proposed a straightforward method to mitigate communication disruptions during HILP events, thereby ensuring operational integrity. This focused approach enhances isolated microgrid resilience and sustainability, offering a strategic response to contemporary environmental challenges.
Nonlinear Two-Track Model of a Semitrailer with Experimental Validation of Lateral and Vertical Tire Forces
Authors: Simon F. G. Ehlers, Zygimantas Ziaukas, Jan-Philipp Kobler, Alexander Busch, Tobias Ortmaier, Mark Wielitzka
Date: 2024-07-14 00:00:00

As part of the automation of commercial vehicles, the number of assistance systems in this field is continuously increasing. The semitrailer plays an important role for the vehicles driving dynamics due to its highly varying loads and the large proportion to the total mass of the truck-semitrailer, especially when it is fully loaded. To create a basis for further development of assistance systems for the semitrailer, this paper presents a two-track model which includes the lateral and roll dynamics of the semitrailer. This enables the future development of observer and filter-based estimation of vehicle states and parameters, which are impossible or very difficult to measure. For offline identification of the unknown model parameters, a Particle-Swarm-Optimization (PSO) algorithm will be used. The validation of the model is based on measurements from a test vehicle. The focus is on the lateral and vertical tire forces of the semitrailer, which are measured at the test vehicle using strain gauges.
Goal-Oriented State Information Compression for Linear Dynamical System Control
Authors: Li Wang, Chao Zhang, Samson Lasaulce, Lina Bariah, Merouane Debbah
Date: 2024-07-14 00:00:00

In this paper, we consider controlled linear dynamical systems in which the controller has only access to a compressed version of the system state. The technical problem we investigate is that of allocating compression resources over time such that the control performance degradation induced by compression is minimized. This can be formulated as an optimization problem to find the optimal resource allocation policy. Under mild assumptions, this optimization problem can be proved to have the same well-known structure as in [1], allowing the optimal resource allocation policy to be determined in closed-form. The obtained insights behind the optimal policy provide clear guidelines on the issue of "when to communicate" and "how to communicate" in dynamical systems with restricted communication resources. The obtained simulation results confirm the efficiency of the proposed allocation policy and illustrate the gain over the widely used uniform rate allocation policy.
Experimental Benchmarking of Energy-saving Sub-Optimal Sliding Mode Control
Authors: Michael Ruderman
Date: 2024-07-14 00:00:00
Tasks: Benchmarking

The recently introduced energy-saving extension of the sub-optimal sliding mode control allows for control-off phases during the convergence to second-order equilibrium. This way, it enables for a lower energy consumption compared to the original sub-optimal sliding mode (SM) algorithm, both commutating a discontinuous control signal. In this paper, the energy-saving sub-optimal SM control is experimentally benchmarked against a standard second-order SM controller which also has a discontinuous control action. Here the so-called terminal second-order SM algorithm is used. The controlled plant is affected by the matched bounded disturbances which are unknown, and the output is additionally subject to the sensor noise. Moreover, a first-order actuator dynamics can lead to chattering, which is parasitic for SM applications. For a fair comparison, the same quadratic terminal surface is designed when benchmarking both SM controllers. Both experimentally compared SM algorithms have the same (bounded) control magnitude and states initial conditions.
Power System Architecture and Control for Green Hydrogen Production via Power Converter-less Photovoltaic-Electrolyser Integration
Authors: Aymeric Fabre, Glen Farivar, Andre Chambers
Date: 2024-07-14 00:00:00
Tasks: Point Tracking

This paper proposes a power system architecture and control for efficient and low-cost green hydrogen production. The proposed system integrates photovoltaic (PV) sources directly with an electrolyser stack, thereby eliminating the need for traditional power converters. With the removal of traditional power converters, maximum power point tracking is achieved through dynamic switching of electrolyser cells in the stack, enabling load variation to maintain optimal voltage for maximum power output. The demonstration methodology involves comprehensive MATLAB Simulink analysis of the integrated system performance through controlled PV-electrolyser interactions.
Impedance Measurement of Rolling Bearings Using an unbalanced AC Wheatstone Bridge
Authors: Steffen Puchtler, Julius van der Kuip, Florian Michael Becker-Dombrowsky, Eckhard Kirchner
Date: 2024-07-13 00:00:00

Industry 4.0 drives the demand for cost-efficient and reliable process data and condition monitoring. Therefore, visualizing the state of tribological contacts becomes important, as they are regularly found in the center of many applications. Utilizing rolling element bearings as sensors and monitoring their health by the electrical impedance method are promising approaches as it allows e.g. load sensing and detection of bearing failures. The impedance cannot be measured directly, but there are various methods available. This work discusses advantages and disadvantages and suggests the AC Wheatstone bridge as a reliable way of measuring impedances with low phase angles at sampling rates in the kHz range. The corresponding equations are introduced, a simulation built, an uncertainty mode and effects analysis carried out and sample measurement results of real rolling elements shown. It can be demonstrated that the AC Wheatstone bridge meets the proposed requirements for sensory utilization and condition monitoring when the bearing is operated in the hydrodynamic regime.
Gravity Balanced Arm Exoskeleton for Basketball Shooting Training
Authors: Yunfei Liu, Zhanghao Yang
Date: 2024-07-13 00:00:00

This paper proposes a gravity balanced arm exoskeleton design for basketball shooting training. The potential energy equation of the mechanism is derived. A simulation of the arm going through the basketball shooting motion is done on the mechanism. Throughout the motion the total potential energy is constant. Thus, the proposed arm exoskeleton is indeed gravity balanced with the use of two springs.
Human Leg Training Machine Based on The Multi-linkage System
Authors: Yunfei Liu, Zhanghao Yang
Date: 2024-07-13 00:00:00

In real life, many people have leg defects. the goal of our work is to design a mechanism which could help them walk based on a specific trajectory and realize flexible walking finally. In this paper, we use a motor to drive a multi-link leg mechanism. The major issues addressed in this paper are as follows: (i) design human leg training mechanism based on the multi-link mechanism (ii) Simulate leg movement trajectory of multi-link mechanism based on walking process (iii) make use of one motor torque control to control the trajectory and velocity of this mechanism.
Temperature Secret in Bathtub: A Model of Temperature Distribution of Bathtub Based on Heat Conduction Equation
Authors: Yunfei Liu
Date: 2024-07-13 00:00:00

We use the multidimensional heat conduction and heat transfer equations to model the temperature distribution of water in a bathtub by solving partial differential equations. We address optimal water addition and bathtub design. First, we establish a water surface cooling model using Newton's law of cooling to simulate heat exchange between air and water. Without new heat sources, the water temperature reaches a minimum in 40 minutes. We then simulate adding hot water with a one-dimensional heat conduction model, including air cooling effects. We determine that the optimal heat input is 80 Joules and the optimal water velocity is 0.042 m/s to maintain temperature and save water. The ideal bathtub dimensions are 1.5m length, 0.6m width, 0.42m depth, with rounded corners. Using finite difference methods and MATLAB's Pdetool, we solve the heat conduction equation and verify numerical stability, discussing the model's pros and cons and suggesting improvements.
Multi-Provider Resource Scheduling in Massive MIMO Radio Access Networks
Authors: Qing An, Divyanshu Pandey, Rahman Doost-Mohammady, Ashutosh Sabharwal, Srinivas Shakkottai
Date: 2024-07-12 00:00:00
Tasks: Scheduling

An important aspect of 5G networks is the development of Radio Access Network (RAN) slicing, a concept wherein the virtualized infrastructure of wireless networks is subdivided into slices (or enterprises), tailored to fulfill specific use-cases. A key focus in this context is the efficient radio resource allocation to meet various enterprises' service-level agreements (SLAs). In this work, we introduce a channel-aware and SLA-aware RAN slicing framework for massive multiple input multiple output (MIMO) networks where resource allocation extends to incorporate the spatial dimension available through beamforming. Essentially, the same time-frequency resource block (RB) can be shared across multiple users through multiple antennas. Notably, certain enterprises, particularly those operating critical infrastructure, necessitate dedicated RB allocation, denoted as private networks, to ensure security. Conversely, some enterprises would allow resource sharing with others in the public network to maintain network performance while minimizing capital expenditure. Building upon this understanding, the proposed scheduler comprises scheduling schemes under both scenarios: where different slices share the same set of RBs, and where they require exclusivity of allocated RBs. We validate the efficacy of our proposed schedulers through simulation by utilizing a channel data set collected from a real-world massive MIMO testbed. Our assessments demonstrate that resource sharing across slices using our approach can lead up to 60.9% reduction in RB usage compared to other approaches. Moreover, our proposed schedulers exhibit significantly enhanced operational efficiency, with significantly faster running time compared to exhaustive greedy approaches while meeting the stringent 5G sub-millisecond-level latency requirement.
The Transmission Value of Energy Storage and Fundamental Limitations
Authors: Qian Zhang, P. R. Kumar, Le Xie
Date: 2024-07-12 00:00:00

This study addresses the transmission value of energy storage in electric grids. The inherent connection between storage and transmission infrastructure is captured from a "cumulative energy" perspective, which enables the reformulating of the conventional optimization problem by employing line power flow as the decision variable. The study also establishes the theoretical limitations of both storage and transmission lines that can be replaced by each other, providing explicit closed-form expressions for the minimum capacity needed. As a key departure from conventional practice in which transmission lines are designed according to the peak power delivery needs, with sufficient storage capacity, the transmission line capacity can be designed based on the average power delivery needs. The models of this paper only rely on a few basic assumptions, paving the way for understanding future storage as a transmission asset market design. Numerical experiments based on 2-bus, modified RTS 24-bus, RTS-GMLC, and Texas synthetic power systems illustrate the results.
Cost-optimized probabilistic maintenance for condition monitoring of wind turbines with rare failures
Authors: Viktor Begun, Ulrich Schlickewei
Date: 2024-07-12 00:00:00

We propose a method, a model, and a form of presenting model results for condition monitoring of a small set of wind turbines with rare failures. The main new ingredient of the method is to sample failure thresholds according to the profit they give to an operating company. The model is a multiple linear regression with seasonal components and external regressors, representing all sensor components except for the selected one. To overcome the scarcity of the training data, we use the median sensor values from all available turbines in their healthy state. The cumulated deviation from the normal behavior model obtained for this median turbine is calibrated for each turbine at the beginning of the test period and after known failures. The proposed form of presenting results is to set a scale for possible costs, control for random maintenance, and show a whole distribution of costs depending on the free model parameters. We make a case study on an open dataset with SCADA data from multiple sensors and show that considering the influence of turbine components is more critical than seasonality. The distribution, the average, and the standard deviation of maintenance costs can be very different for similar minimal costs. Random maintenance can be more profitable than reactive maintenance and other approaches. Our predictive maintenance model outperforms random maintenance and competitors for the whole set of considered turbines, giving substantial savings.