pwc-archive/papers-with-abstracts huggingface dataset:
Space-Time Decoupled Metasurface Integrating Reflective Mixer and Phase Shifter Functions
Authors: Xuehui Dong, Miyu Feng, Bokai Lai, Chen Shao, Jianan Zhang, Rujing Xiong, Tiebin Mi, Robert Caiming Qiu
Date: 2024-11-04 00:00:00
Tasks: TAG

Electromagnetic (EM) information metasurface represents a class of 2D metamaterials that can swiftly manipulate their EM characteristics to convey specific information. Past researches present various strategies for embedding information within ambient EM waves through the superposition of time-varying quantized properties for each unit. Despite their capabilities, these approaches alone are insufficient for concurrently but independently realizing the creation of information and the allocation of spatial energy. In this work, we introduce a comprehensive approach named the space-time decoupled metasurface (STD-Metasurface), facilitating the simultaneous and independent control of information modulation and passive beamforming by incorporating reflective mixer and phase shifter functionalities into each unit. As a proof of concept, we present a minimalist design of single-diode unit where the ingenious utilization of analog switch bonds the digital signal and analog signal together. By inputting baseband analogue signals in a specific level range, STD-Metasurface prototype is capable of modulating them onto the envelopes of reflected EM waves without distortion meanwhile controlling the reflected radiation patterns by digital signals, even waveform superposition and spatial multiplexing. To demonstrate the superior capabilities of the STD-Metasurface, we have developed an experimental platform to highlight its practicality and potential applications as a reconfigurable backscatter transmitter and a dynamic Doppler-spoofing reflector array.
Degradation-Infused Energy Portfolio Allocation Framework: Risk-Averse Fair Storage Participation
Authors: Parikshit Pareek, L. P. Mohasha Isuru Sampath, Anshuman Singh, Lalit Goel, Hoay Beng Gooi, Hung Dinh Nguyen
Date: 2024-11-03 00:00:00
Tasks: Fairness, Form

This work proposes a novel degradation-infused energy portfolio allocation (DI-EPA) framework for enabling the participation of battery energy storage systems in multi-service electricity markets. The proposed framework attempts to address the challenge of including the rainflow algorithm for cycle counting by directly developing a closed-form of marginal degradation as a function of dispatch decisions. Further, this closed-form degradation profile is embedded into an energy portfolio allocation (EPA) problem designed for making the optimal dispatch decisions for all the batteries together, in a shared economy manner. We term the entity taking these decisions as `facilitator' which works as a link between storage units and market operators. The proposed EPA formulation is quipped with a conditional-value-at-risk (CVaR)-based mechanism to bring risk-averseness against uncertainty in market prices. The proposed DI-EPA problem introduces fairness by dividing the profits into various units using the idea of marginal contribution. Simulation results regarding the accuracy of the closed-form of degradation, effectiveness of CVaR in handling uncertainty within the EPA problem, and fairness in the context of degradation awareness are discussed. Numerical results indicate that the DI-EPA framework improves the net profit of the storage units by considering the effect of degradation in optimal market participation.
Advancing Cyber-Attack Detection in Power Systems: A Comparative Study of Machine Learning and Graph Neural Network Approaches
Authors: Tianzhixi Yin, Syed Ahsan Raza Naqvi, Sai Pushpak Nandanoori, Soumya Kundu
Date: 2024-11-04 00:00:00
Tasks: Anomaly Detection, Cyber Attack Detection, Graph Neural Network, Time Series, Time Series Anomaly Detection

This paper explores the detection and localization of cyber-attacks on time-series measurements data in power systems, focusing on comparing conventional machine learning (ML) like k-means, deep learning method like autoencoder, and graph neural network (GNN)-based techniques. We assess the detection accuracy of these approaches and their potential to pinpoint the locations of specific sensor measurements under attack. Given the demonstrated success of GNNs in other time-series anomaly detection applications, we aim to evaluate their performance within the context of power systems cyber-attacks on sensor measurements. Utilizing the IEEE 68-bus system, we simulated four types of false data attacks, including scaling attacks, additive attacks, and their combinations, to test the selected approaches. Our results indicate that GNN-based methods outperform k-means and autoencoder in detection. Additionally, GNNs show promise in accurately localizing attacks for simple scenarios, although they still face challenges in more complex cases, especially ones that involve combinations of scaling and additive attacks.
Optimal Sensing of Momentum Kicks with a Feedback-Controlled Nanomechanical Resonator
Authors: Kaspar Schmerling, Hajrudin Bešić, Andreas Kugi, Silvan Schmid, Andreas Deutschmann-Olek
Date: 2024-11-04 00:00:00

External disturbances exciting a mechanical resonator can be exploited to gain information on the environment. Many of these interactions manifest as momentum kicks, such as the recoil of residual gas, radioactive decay, or even hypothetical interactions with dark matter. These disturbances are often rare enough that they can be resolved as singular events rather than cumulated as force noise. While high-Q resonators with low masses are particularly sensitive to such momentum kicks, they will strongly excite the resonator, leading to nonlinear effects that deteriorate the sensing performance. Hence, this paper utilizes optimal estimation methods to extract individual momentum kicks from measured stochastic trajectories of a mechanical resonator kept in the linear regime through feedback control. The developed scheme is illustrated and tested experimentally using a pre-stressed SiN trampoline resonator. Apart from enhancing a wide range of sensing scenarios mentioned above, our results indicate the feasibility of novel single-molecule mass spectrometry approaches.
Distributed Stochastic ACOPF Based on Consensus ADMM and Scenario Reduction
Authors: Shan Yang, Yongli Zhu
Date: 2024-11-04 00:00:00
Tasks: Clustering, Load Forecasting

This paper presents a Consensus ADMM-based modeling and solving approach for the stochastic ACOPF. The proposed optimization model considers the load forecasting uncertainty and its induced load-shedding cost via Monte Carlo sampling. The sampled scenarios are reduced using a clustering method combined with simultaneous backward reduction techniques to reduce the computational complexity. The proposed approach is tested on two IEEE systems, achieving about 2% cost reduction and more than 15 times lower reliability index in stochastic load settings compared to the baseline approach.
AI-Ready Energy Modelling for Next Generation RAN
Authors: Kishan Sthankiya, Keith Briggs, Mona Jaber, Richard G. Clegg
Date: 2024-11-04 00:00:00
Tasks: CPU

Recent sustainability drives place energy-consumption metrics in centre-stage for the design of future radio access networks (RAN). At the same time, optimising the trade-off between performance and system energy usage by machine-learning (ML) is an approach that requires large amounts of granular RAN data to train models, and to adapt in near realtime. In this paper, we present extensions to the system-level discrete-event AIMM (AI-enabled Massive MIMO) Simulator, generating realistic figures for throughput and energy efficiency (EE) towards digital twin network modelling. We further investigate the trade-off between maximising either EE or spectrum efficiency (SE). To this end, we have run extensive simulations of a typical macrocell network deployment under various transmit power-reduction scenarios with a range of difference of 43 dBm. Our results demonstrate that the EE and SE objectives often require different power settings in different scenarios. Importantly, low mean user CPU execution times of 2.17 $\pm$ 0.05 seconds (2~s.d.) demonstrate that the AIMM Simulator is a powerful tool for quick prototyping of scalable system models which can interface with ML frameworks, and thus support future research in energy-efficient next generation networks.
Bidding and Dispatch Strategies with Flexibility Quantification and Pricing for Electric Vehicle Aggregator in Joint Energy-Regulation Market
Authors: Manqi Xu, Ye Guo, Hongbin Sun
Date: 2024-11-04 00:00:00
Tasks: Model Predictive Control

Managing and unlocking the flexibility hidden in electric vehicles (EVs) has emerged as a critical yet challenging task towards low-carbon power and energy systems. This paper focuses on the online bidding and dispatch strategies for an EV aggregator (EVA) in a joint energy-regulation market, considering EVs' flexibility contributions and compensations. A method for quantifying EV flexibility as a tradable commodity is proposed, allowing the EVA to set flexibility prices based on bid-in supply curves. An EVA bidding model in the joint market incorporate flexibility procurement is formulated. The stochastic model predictive control technique is employed to solve the bidding problem online and address the uncertainties from the electricity markets and the EVs. A power dispatch protocol that ensures a profitable and feasible allocation based on EV flexibility contribution is proposed. An affine mapping control strategy can be derived based on parametric linear programming, enables online indexing of optimal solutions given the regulation signals to avoid repeatedly solving the problem. Numerical experiments show the effectiveness of the proposed scheme, and the solution methodology can be applied in real-time.
KPCA for Thrust Vectoring Systems Exhibiting Singular Points
Authors: Tam W. Nguyen, Kyoungseok Han, Kenji Hirata
Date: 2024-11-04 00:00:00
Tasks: continuous-control, Continuous Control

This paper considers a class of thrust vectoring systems, which are nonlinear, overactuated, and time-invariant. We assume that the system is composed of two subsystems and there exist singular points around which the linearized system is uncontrollable. Furthermore, we assume that the system is stabilizable through a two-level control allocation. In this particular setting, we cannot do much with the linearized system, and a direct nonlinear control approach must be used to analyze the system stability. Under adequate assumptions and a suitable nonlinear continuous control-allocation law, we can prove uniform asymptotic convergence of the points of equilibrium using Lyapunov input-to-state stability and the small gain theorem. This control allocation, however, requires the design of an allocated mapping and introduces two exogenous inputs. In particular, the closed-loop system is cascaded, and the output of one subsystem is the disturbance of the other, and vice versa. In general, it is difficult to find a closed-form solution for the allocated mapping; it needs to satisfy restrictive conditions, among which Lipschitz continuity to ensure that the disturbances eventually vanish. Additionally, this mapping is in general nontrivial and non-unique. In this paper, we propose a new kernel-based predictive control allocation to substitute the need for designing an analytic mapping, and assess if it can produce a meaningful mapping ``on-the-fly" by solving online an optimization problem. The simulations include three examples, which are the manipulation of an object through an unmanned aerial vehicle in two and three dimensions, and the control of a surface vessel actuated by two azimuthal thrusters.
Preemptive Holistic Collaborative System and Its Application in Road Transportation
Authors: Yuan Li, Tao Li, Xiaoxue Xu, Xiang Dong, Yincai Cai, Ting Peng
Date: 2024-11-04 00:00:00

Numerous real-world systems, including manufacturing processes, supply chains, and robotic systems, involve multiple independent entities with diverse objectives. The potential for conflicts arises from the inability of these entities to accurately predict and anticipate each other's actions. To address this challenge, we propose the Preemptive Holistic Collaborative System (PHCS) framework. By enabling information sharing and collaborative planning among independent entities, the PHCS facilitates the preemptive resolution of potential conflicts. We apply the PHCS framework to the specific context of road transportation, resulting in the Preemptive Holistic Collaborative Road Transportation System (PHCRTS). This system leverages shared driving intentions and pre-planned trajectories to optimize traffic flow and enhance safety. Simulation experiments in a two-lane merging scenario demonstrate the effectiveness of PHCRTS, reducing vehicle time delays by 90%, increasing traffic capacity by 300%, and eliminating accidents. The PHCS framework offers a promising approach to optimize the performance and safety of complex systems with multiple independent entities.
Frequency-based Design Method for Model-Free Controllers
Authors: Marcos Moreno-Gonzalez, Antonio Artuñedo, Jorge Villagra
Date: 2024-11-04 00:00:00
Tasks: model

Model-Free Control (MFC) has been applied to a wide variety of systems in which it has shown its performance. MFC offers "model-free operation", but the controller design requires some information from the nominal plant. This paper introduces a new design method for model-free controllers that uses minimal data about the system and retrieves a set of stable controller configurations. This method is specifically developed for first-order model-free controllers, but can be extended to second-order controllers, and it relies in a frequency analysis of the controller and the plant. The main feature of the design method is decoupling the design of the main control parameter alpha from the rest, providing specific values for it. The efficacy of the proposed method will be showcased with some relevant application examples.
Backstepping Design for Incremental Input-to-State Stabilization of Unknown Systems
Authors: David Smith Sundarsingh, Bhabani Shankar Dey, Pushpak Jagtap
Date: 2024-11-04 00:00:00

Incremental stability of dynamical systems ensures the convergence of trajectories from different initial conditions towards each other rather than a fixed trajectory or equilibrium point. Here, we introduce and characterize a novel class of incremental Lyapunov functions, an incremental stability notion known as Incremental Input-to-State practical Stability ({\delta}-ISpS). Using Gaussian Process, we learn the unknown dynamics of a class of control systems. We then present a backstepping control design scheme that provides state-feedback controllers that render the partially unknown control system {\delta}-ISpS. To show the effectiveness of the proposed controller, we implement it in two case studies.
Deep Reinforcement Learning for Optimizing Inverter Control: Fixed and Adaptive Gain Tuning Strategies for Power System Stability
Authors: Shuvangkar Chandra Das, Tuyen Vu, Deepak Ramasubramanian, Evangelos Farantatos, Jianhua Zhang, Thomas Ortmeyer
Date: 2024-11-03 00:00:00
Tasks: Deep Reinforcement Learning

This paper presents novel methods for tuning inverter controller gains using deep reinforcement learning (DRL). A Simulink-developed inverter model is converted into a dynamic link library (DLL) and integrated with a Python-based RL environment, leveraging the multi-core deployment and accelerated computing to significantly reduce RL training time. A neural network-based mechanism is developed to transform the cascaded PI controller into an actor network, allowing optimized gain tuning by an RL agent to mitigate scenarios such as subsynchronous oscillations (SSO) and initial transients. Two distinct tuning approaches are demonstrated: a fixed gain strategy, where controller gains are represented as RL policy (actor network) weights, and an adaptive gain strategy, where gains are dynamically generated as RL policy (actor network) outputs. A comparative analysis of these methods is provided, showcasing their effectiveness in stabilizing the transient performance of grid-forming and grid-following converters and deployment challenges in hardware. Experimental results are presented, demonstrating the enhanced robustness and practical applicability of the RL-tuned controller gains in real-world systems.
Near-Optimal Emission-Aware Online Ride Assignment Algorithm for Peak Demand Hours
Authors: Ali Zeynali, Mahsa Sahebdel, Noman Bashir, Ramesh K. Sitaraman, Mohammad Hajiesmaili
Date: 2024-11-03 00:00:00

Ridesharing has experienced significant global growth over the past decade and is becoming integral to future transportation networks. These services offer alternative mobility options in many urban areas, promoting car-light or car-free lifestyles, with their market share rapidly expanding due to the convenience they offer. However, alongside these benefits, concerns have arisen about the environmental impact of ridesharing, particularly its contribution to carbon emissions. A major source of these emissions is deadhead miles that are driven without passengers between trips. This issue is especially pronounced during high-demand periods when the number of ride requests exceeds platform capacity, leading to longer deadhead miles and higher emissions. While reducing these unproductive miles can lower emissions, it may also result in longer wait times for passengers as they wait for a nearby driver, potentially diminishing the overall user experience. In this paper, we propose LARA, an online algorithm for rider-to-driver assignment that dynamically adjusts the maximum allowed deadhead miles for drivers and assigns ride requests accordingly. While LARA can be applied under any conditions, it is particularly more effective during high-demand hours, aiming to reduce both carbon emissions and rider wait times. We prove that LARA achieves near-optimal performance in online settings compared to the optimal offline algorithm. Furthermore, we evaluate LARA using both synthetic and real-world datasets, demonstrating up to 34.2% reduction in emissions and up to 42.9% reduction in rider wait times compared to state-of-the-art algorithms. While recent studies have introduced the problem of emission-aware ride assignment, LARA is the first algorithm to provide both theoretical and empirical guarantees on performance.
Control Node Placement and Structural Controllability of Water Quality Dynamics in Drinking Networks
Authors: Salma M. Elsherif, Ahmad F. Taha
Date: 2024-11-02 00:00:00

Chlorine, the most widely used disinfectant, needs to be adequately distributed in water distribution networks (WDNs) to maintain consistent residual levels and ensure water safety. This is performed through control node injections at the treatment plant via booster stations scattered in WDNs. While previous studies have applied various optimization metrics for booster station placement, many have failed to consider the coverage of the station injections and the dynamic nature of WDNs. In particular, variations in hydraulics and demand significantly impact the reachability and efficacy of chlorine injections which then impact optimal placement of booster stations. This study introduces a novel formulation that combines control- and graph-theoretic approaches to solve the booster station placement problem. Unlike traditional methods, our approach emphasizes maximizing the system's ability to control disinfectant levels with minimal energy, taking into account the time-varying hydraulic profiles that lead to different optimal station placements. We propose a simple weighting technique to determine the placements by assessing the structural controllability of each configuration, based on the network's topology and independent of specific parameters like decay rates or pipe roughness. This method ensures effective chlorine coverage across the network. Our approach is validated on different networks, demonstrating its operational effectiveness, scalability, and practicality.
Stability analysis of distributed Kalman filtering algorithm for stochastic regression model
Authors: Siyu Xie, Die Gan, Zhixin Liu
Date: 2024-11-02 00:00:00
Tasks: regression

In this paper, a distributed Kalman filtering (DKF) algorithm is proposed based on a diffusion strategy, which is used to track an unknown signal process in sensor networks cooperatively. Unlike the centralized algorithms, no fusion center is need here, which implies that the DKF algorithm is more robust and scalable. Moreover, the stability of the DKF algorithm is established under non-independent and non-stationary signal conditions. The cooperative information condition used in the paper shows that even if any sensor cannot track the unknown signal individually, the DKF algorithm can be utilized to fulfill the estimation task in a cooperative way. Finally, we illustrate the cooperative property of the DKF algorithm by using a simulation example.
Relay Satellite Assisted LEO Constellation NOMA Communication System
Authors: Xuyang Zhang, Xinwei Yue, Zhihao Han, Tian Li, Xia Shen, Yafei Wang, Rongke Liu
Date: 2024-11-02 00:00:00

This paper proposes a relay satellite assisted low earth orbit (LEO) constellation non-orthogonal multiple access combined beamforming (R-NOMA-BF) communication system, where multiple antenna LEO satellites deliver information to ground non-orthogonal users. To measure the service quality, we formulate a resource allocation problem to minimize the second-order difference between the achievable capacity and user request traffic. Based on the above problem, joint optimization for LEO satellite-cell assignment factor, NOMA power and BF vector is taken into account. The optimization variables are analyzed with respect to feasibility and non-convexity. Additionally, we provide a pair of effective algorithms, i.e., doppler shift LEO satellite-cell assisted monotonic programming of NOMA with BF vector (D-mNOMA-BF) and ant colony pathfinding based NOMA exponential cone programming with BF vector (A-eNOMA-BF). Two compromise algorithms regarding the above are also presented. Numerical results show that: 1) D-mNOMA-BF and A-eNOMA-BF algorithms are superior to that of orthogonal multiple access based BF (OMA-BF) and polarization multiplexing schemes; 2) With the increasing number of antennas and single satellite power, R-NOMA-BF system is able to expand users satisfaction; and 3) By comparing various imperfect successive interference cancellation, the performance of A-mNOMA-BF algorithm exceeds D-mNOMA-BF.