pwc-archive/papers-with-abstracts huggingface dataset:
Goal-Oriented Communication, Estimation, and Control over Bidirectional Wireless Links
Authors: Jie Cao, Ernest Kurniawan, Amnart Boonkajay, Nikolaos Pappas, Sumei Sun, Petar Popovski
Date: 2023-12-26 00:00:00
Tasks: Scheduling

We consider a wireless networked control system (WNCS) with bidirectional imperfect links for real-time applications such as smart grids. To maintain the stability of WNCS, captured by the probability that plant state violates preset values, at minimal cost, heterogeneous physical processes are monitored by multiple sensors. This status information, such as dynamic plant state and Markov Process-based context information, is then received/estimated by the controller for remote control. However, scheduling multiple sensors and designing the controller with limited resources is challenging due to their coupling, delay, and transmission loss. We formulate a Constrained Markov Decision Problem (CMDP) to minimize violation probability with cost constraints. We reveal the relationship between the goal and different updating actions by analyzing the significance of information that incorporates goal-related usefulness and contextual importance. Subsequently, a goal-oriented deterministic scheduling policy is proposed. Two sensing-assisted control strategies and a control-aware estimation policy are proposed to improve the violation probability-cost tradeoff, integrated with the scheduling policy to form a goal-oriented co-design framework. Additionally, we explore retransmission in downlink transmission and qualitatively analyze its preference scenario. Simulation results demonstrate that the proposed goal-oriented co-design policy outperforms previous work in simultaneously reducing violation probability and cost
A New Framework for Bounding Reachability Probabilities of Continuous-time Stochastic Systems
Authors: Bai Xue
Date: 2023-12-26 00:00:00

This manuscript presents an innovative framework for constructing barrier functions to bound reachability probabilities for continuous-time stochastic systems described by stochastic differential equations (SDEs). The reachability probabilities considered in this paper encompass two aspects: the probability of reaching a set of specified states within a predefined finite time horizon, and the probability of reaching a set of specified states at a particular time instant. The barrier functions presented in this manuscript are developed either by relaxing a parabolic partial differential equation that characterizes the exact reachability probability or by applying the Gr\"onwall's inequality. In comparison to the prevailing construction method, which relies on Doob's non-negative supermartingale inequality (or Ville's inequality), the proposed barrier functions provide stronger alternatives, complement existing methods, or fill gaps.
Outlier-immune Data-driven Linear Power Flow Model Construction via Mixed-Integer Programming
Authors: Guoan Yan, Zhengshuo Li
Date: 2023-12-26 00:00:00

The common approaches to construct a data-driven linear power flow (DD-LPF) model cannot completely eliminate the adverse impacts of outliers in a training dataset. In this letter, a novel outlier-immune DD-LPF model construction method via mixed-integer programming is presented for automatically and optimally identifying outliers to form a more accurate LPF model. Two acceleration solution strategies are further suggested to reduce the computational time. Case studies demonstrate the superior accuracy and comparable computational time of the proposed method when compared to three common approaches.
Hairpin Motors for Electromobility: Twists and Bends of a Technological Breakthrough that Initially Arrived A Century Too Soon
Authors: Stefan M. Goetz, Ricardo Lizana F., Sebastian Rivera
Date: 2023-12-26 00:00:00

There is currently a major trend to hairpin-winding motors for small and medium drives with increased power, specifically more torque density in the automotive industry. Practically all large players in the field either already use this winding technology or have announced doing so soon. However, hairpins, bar windings, and other segmented winding techniques are not purely a material and production issue. Instead their application to small drives influences all aspects of the design of machines, which are currently explored and studied by the industry. These range from not obvious gaps in the theory, parameter studies for maxima of efficiency, possible as well as advantageous winding schemes, thermal design, and ways to control ac losses to specific materials and process difficulties. Despite the apparent novelty of the trend, however, designers could revisit a widely forgotten knowledge base of more than 100 years for many of those questions. This old knowledge base and the understanding that many recently presented concepts have been developed earlier may speed up the technological development and appear to be a key to further innovation. Instead, many problems need to be solved again and technologies re-invented. Furthermore, as this technology has recently become merely industry-driven, a substantial portion of the information and technological developments are not available to the public -- a state that to our eyes may harm the innovation capacity of the drives community.
Frequency-Domain Identification of Discrete-Time Systems using Sum-of-Rational Optimization
Authors: Mohamed Abdalmoaty, Jared Miller, Mingzhou Yin, Roy S. Smith
Date: 2023-12-25 00:00:00

We propose a computationally tractable method for the identification of stable canonical discrete-time rational transfer function models, using frequency domain data. The problem is formulated as a global non-convex optimization problem whose objective function is the sum of weighted squared residuals at each observed frequency datapoint. Stability is enforced using a polynomial matrix inequality constraint. The problem is solved globally by a moment-sum-of-squares hierarchy of semidefinite programs through a framework for sum-of-rational-functions optimization. Convergence of the moment-sum-of-squares program is guaranteed as the bound on the degree of the sum-of-squares polynomials approaches infinity. The performance of the proposed method is demonstrated using numerical simulation examples.
UAV Trajectory Tracking via RNN-enhanced IMM-KF with ADS-B Data
Authors: Yian Zhu, Ziye Jia, Qihui Wu, Chao Dong, Zirui Zhuang, Huiling Hu, Qi Cai
Date: 2023-12-25 00:00:00

With the increasing use of autonomous unmanned aerial vehicles (UAVs), it is critical to ensure that they are continuously tracked and controlled, especially when UAVs operate beyond the communication range of ground stations (GSs). Conventional surveillance methods for UAVs, such as satellite communications, ground mobile networks and radars are subject to high costs and latency. The automatic dependent surveillance-broadcast (ADS-B) emerges as a promising method to monitor UAVs, due to the advantages of real-time capabilities, easy deployment and affordable cost. Therefore, we employ the ADS-B for UAV trajectory tracking in this work. However, the inherent noise in the transmitted data poses an obstacle for precisely tracking UAVs. Hence, we propose the algorithm of recurrent neural network-enhanced interacting multiple model-Kalman filter (RNN-enhanced IMM-KF) for UAV trajectory filtering. Specifically, the algorithm utilizes the RNN to capture the maneuvering behavior of UAVs and the noise level in the ADS-B data. Moreover, accurate UAV tracking is achieved by adaptively adjusting the process noise matrix and observation noise matrix of IMM-KF with the assistance of the RNN. The proposed algorithm can facilitate GSs to make timely decisions during trajectory deviations of UAVs and improve the airspace safety. Finally, via comprehensive simulations, the total root mean square error of the proposed algorithm decreases by 28.56%, compared to the traditional IMM-KF.
Coordinated Planning of Offshore Charging Stations and Electrified Ships: A Case Study on Shanghai-Busan Maritime Route
Authors: Hao Li, Hanqi Tao, Wentao Huang, Hongcai Zhang, Ran Li
Date: 2023-12-25 00:00:00

Despite the success of electric vehicles on land, electrification of maritime ships is challenged by the dilemma of range anxiety and cargo-carrying capacity. The longer range requires larger batteries, which inevitably eat up the precious cargo space and weight. This paper breaks new ground by proposing a coordinated planning model for offshore charging stations (OCSs) and electric ships (ESs), marking a first in this field. Strategically situated OCS can partition a long maritime route into several shorter segments, which in turn lead to smaller batteries and thus larger cargo capacities. The research analyzed the impact of maritime geographical conditions on the placement and sizing process and provided insights into the trade-offs between battery size, cargo-carrying capacity, and the cruising range of different types of electrified ships. Using real Automatic Identification System (AIS) data, we estimated the economic feasibility of the Shanghai-Busan high-traffic maritime route and conducted a sensitivity analysis on factors affecting its economic viability. The results show that installing OCS can significantly reduce the propulsion cost compared with ESs without OCS and traditional internal combustion engine (ICE) ships.
Instrumental Variables based DREM for Online Asymptotic Identification of Perturbed Linear Systems
Authors: Anton Glushchenko, Konstantin Lastochkin
Date: 2023-12-25 00:00:00
Tasks: parameter estimation

Existing online continuous-time parameter estimation laws provide exact (asymptotic/exponential or finite/fixed time) identification of dynamical linear/nonlinear systems parameters only if the external perturbations are equaled to zero or independent with the regressor of the system. However, in real systems the disturbances are almost always non-vanishing and dependent with the regressor. In the presence of perturbations with such properties the above-mentioned identification approaches ensure only boundedness of a parameter estimation error. The main goal of this study is to close this gap and develop a novel online continuous-time parameter estimator, which guarantees exact asymptotic identification of unknown parameters of linear systems in the presence of unknown but bounded perturbations and has relaxed convergence conditions. To achieve the aforementioned goal, it is proposed to augment the deeply investigated Dynamic Regressor Extension and Mixing (DREM) procedure with the novel Instrumental Variables (IV) based extension scheme with averaging. Such an approach allows one to obtain a set of scalar regression equations with asymptotically vanishing perturbation if the initial disturbance that affects the plant is bounded and independent not with the system regressor, but with the instrumental variable. It is rigorously proved that a gradient estimation law designed on the basis of such scalar regressions ensures online unbiased asymptotic identification of the parameters of the perturbed linear systems if some weak independence and excitation assumptions are met. Theoretical results are illustrated and supported with adequate numerical simulations.
Integrating Renewable Energy Sources as Reserve Providers: Modeling, Pricing, and Properties
Authors: Wenli Wu, Ye Guo, Jiantao Shi
Date: 2023-12-24 00:00:00

In pursuit of carbon neutrality, many countries have adopted renewable portfolio standards to facilitate the integration of renewable energy. However, increasing penetration of renewable energy resources will also pose higher requirements on system flexibility. Allowing renewable themselves to participate in the reserve market could be a viable solution. To this end, this paper proposes an optimal dispatch model for joint energy-reserve procurement that incorporates renewable portfolio standards and RES serve as reserve providers. Potential generator outages and deviations in renewable and load power are modelled through a given number of probability-weighted scenarios. In particular, reserve resources are initially booked in the base case and then activated in non-base scenarios through the re-dispatch process. Marginal pricing is used to derive energy, reserve, and power deviation prices. Next, we develop the associated settlement process and establish several market properties. The proposed pricing scheme establishes equivalence between thermal generators and renewable units by accounting for their uncertainties, including thermal generator outages and renewable power deviations, and their flexibility, namely reserve and re-dispatch. We have shown that for renewable resources, supplying reserve according to the dispatch results compared to generating as much as possible leads to better profits. Simulations validate the effectiveness of the proposed method and properties established.
On Completeness of SDP-Based Barrier Certificate Synthesis over Unbounded Domains
Authors: Hao Wu, Shenghua Feng, Ting Gan, Jie Wang, Bican Xia, Naijun Zhan
Date: 2023-12-24 00:00:00

Barrier certificates, serving as differential invariants that witness system safety, play a crucial role in the verification of cyber-physical systems (CPS). Prevailing computational methods for synthesizing barrier certificates are based on semidefinite programming (SDP) by exploiting Putinar Positivstellensatz. Consequently, these approaches are limited by the Archimedean condition, which requires all variables to be bounded, i.e., systems are defined over bounded domains. For systems over unbounded domains, unfortunately, existing methods become incomplete and may fail to identify potential barrier certificates. In this paper, we address this limitation for the unbounded cases. We first give a complete characterization of polynomial barrier certificates by using homogenization, a recent technique in the optimization community to reduce an unbounded optimization problem to a bounded one. Furthermore, motivated by this formulation, we introduce the definition of homogenized systems and propose a complete characterization of a family of non-polynomial barrier certificates with more expressive power. Experimental results demonstrate that our two approaches are more effective while maintaining a comparable level of efficiency.
A Multi-day Needs-based Modeling Approach for Activity and Travel Demand Analysis
Authors: Kexin Chen, Jinping Guan, Ravi Seshadri, Varun Pattabhiraman, Youssef Medhat Aboutaleb, Ali Shamshiripour, Chen Liang, Xiaochun Zhang, Moshe Ben-Akiva
Date: 2023-12-24 00:00:00
Tasks: parameter estimation

This paper proposes a multi-day needs-based model for activity and travel demand analysis. The model captures the multi-day dynamics in activity generation, which enables the modeling of activities with increased flexibility in time and space (e.g., e-commerce and remote working). As an enhancement to activity-based models, the proposed model captures the underlying decision-making process of activity generation by accounting for psychological needs as the drivers of activities. The level of need satisfaction is modeled as a psychological inventory, whose utility is optimized via decisions on activity participation, location, and duration. The utility includes both the benefit in the inventory gained and the cost in time, monetary expense as well as maintenance of safety stock. The model includes two sub-models, a Deterministic Model that optimizes the utility of the inventory, and an Empirical Model that accounts for heterogeneity and stochasticity. Numerical experiments are conducted to demonstrate model scalability. A maximum likelihood estimator is proposed, the properties of the log-likelihood function are examined and the recovery of true parameters is tested. This research contributes to the literature on transportation demand models in the following three aspects. First, it is arguably better grounded in psychological theory than traditional models and allows the generation of activity patterns to be policy-sensitive (while avoiding the need for ad hoc utility definitions). Second, it contributes to the development of needs-based models with a non-myopic approach to model multi-day activity patterns. Third, it proposes a tractable model formulation via problem reformulation and computational enhancements, which allows for maximum likelihood parameter estimation.
Resonant Inductive Coupling Network for Human-Sized Magnetic Particle Imaging
Authors: Fabian Mohn, Fynn Förger, Florian Thieben, Martin Möddel, Ingo Schmale, Tobias Knopp, Matthias Graeser
Date: 2023-12-23 00:00:00

In Magnetic Particle Imaging, a field-free region is maneuvered throughout the field of view using a time-varying magnetic field known as the drive-field. Human-sized systems operate the drive-field in the kHz range and generate it by utilizing strong currents that can rise to the kA range within a coil called the drive field generator. Matching and tuning between a power amplifier, a band-pass filter and the drive-field generator is required. Here, for reasons of safety in future human scanners, a symmetrical topology and a transformer, called inductive coupling network is used. Our primary objectives are to achieve floating potentials to ensure patient safety, attaining high linearity and high gain for the resonant transformer. We present a novel systematic approach to the design of a loss-optimized resonant toroid with a D-shaped cross section, employing segmentation to adjust the inductance-to-resistance ratio while maintaining a constant quality factor. Simultaneously, we derive a specific matching condition of a symmetric transmit-receive circuit for magnetic particle imaging. The chosen setup filters the fundamental frequency and allows simultaneous signal transmission and reception. In addition, the decoupling of multiple drive field channels is discussed and the primary side of the transformer is evaluated for maximum coupling and minimum stray field. Two prototypes were constructed, measured, decoupled, and compared to the derived theory and to method-of-moment based simulations.
CSI Measurements and Initial Results for Massive MIMO to UAV Communication
Authors: Zhuangzhuang Cui, Achiel Colpaert, Sofie Pollin
Date: 2023-12-23 00:00:00

Non-Terrestrial Network (NTN) has been envisioned as a key component of the sixth-generation (6G) mobile communication system. Meanwhile, unmanned aerial vehicles (UAVs) play an important role in enabling and deploying NTNs. In this paper, we focus on massive multi-input multi-output (MaMIMO) supported UAV communications, where channel state information (CSI) was measured considering different heights and trajectories of a rotary-wing drone. To characterize the propagation channel for this air-to-ground link, some initial results were analyzed, such as stationary distance. To investigate the impact of channels on communication performance, we analyzed spectral efficiency (SE) by using Maximum Ratio Combining (MRC). This study shows that the presented space-time-frequency channel dataset facilitates channel correlation analysis and supports performance evaluation for MaMIMO-UAV communications.
Stochastic Data-Driven Predictive Control with Equivalence to Stochastic MPC
Authors: RuiQi Li, John W. Simpson-Porco, Stephen L. Smith
Date: 2023-12-23 00:00:00
Tasks: Model Predictive Control

We propose a data-driven receding-horizon control method dealing with the chance-constrained output-tracking problem of unknown stochastic linear time-invariant (LTI) systems with partial state observation. The proposed method takes into account the statistics of the process noise, the measurement noise and the uncertain initial condition, following an analogous framework to Stochastic Model Predictive Control (SMPC), but does not rely on the use of a parametric system model. As such, our receding-horizon algorithm produces a sequence of closed-loop control policies for predicted time steps, as opposed to a sequence of open-loop control actions. Under certain conditions, we establish that our proposed data-driven control method produces identical control inputs as that produced by the associated model-based SMPC. Simulation results on a grid-connected power converter are provided to illustrate the performance benefits of our methodology.
Electromagnetic Transient Model of Cryptocurrency Mining Loads for Low-Voltage Ride Through Assessment in Transmission Grids
Authors: Anindita Samanta, Subir Majumder, Hasan Ibrahim, Prasad Enjeti, Le Xie
Date: 2023-12-22 00:00:00

In this paper, we developed an Electromagnetic Transient (EMT) model tailored for large cryptocurrency mining loads to understand the cross-interaction of these loads with the electric grid. The load model has been built using Electromagnetic Transients Program (EMTP) software. We have cross-validated the performance of the EMT model of the load with commercial application-specific integrated circuit miners, typically used by large-scale mining facilities, by comparing their low-voltage ride-through (LVRT) capabilities. Subsequently, LVRT capabilities of the large-scale miners have been tested against various fault scenarios both within the miner's remote facility as well as at one of the distant buses of the interconnected grid. The significance of this model lies in its scalability to accommodate larger blocks of mining loads and its seamless integration into a larger electric grid.
Harnessing Uncertainty for a Separation Principle in Direct Data-Driven Predictive Control
Authors: Alessandro Chiuso, Marco Fabris, Valentina Breschi, Simone Formentin
Date: 2023-12-22 00:00:00
Tasks: Model Predictive Control, valid

Model Predictive Control (MPC) is a powerful method for complex system regulation, but its reliance on an accurate model poses many limitations in real-world applications. Data-driven predictive control (DDPC) aims at overcoming this limitation, by relying on historical data to provide information on the plant to be controlled. In this work, we present a unified stochastic framework for direct DDPC, where control actions are obtained by optimizing the Final Control Error (FCE), which is directly computed from available data only and automatically weighs the impact of uncertainty on the control objective. Our framework allows us to establish a separation principle for Predictive Control, elucidating the role that predictive models and their uncertainty play in DDPC. Moreover, it generalizes existing DDPC methods, like regularized Data-enabled Predictive Control (DeePC) and $\gamma$-DDPC, providing a path toward noise-tolerant data-based control with rigorous optimality guarantees. The theoretical investigation is complemented by a series of experiments (code available on GitHub: https://github.com/marcofabris92/a-separation-principle-in-d3pc), revealing that the proposed method consistently outperforms or, at worst, matches existing techniques without requiring tuning regularization parameters as other methods do.
Forecasting exports in selected OECD countries and Iran using MLP Artificial Neural Network
Authors: Soheila Khajoui, Saeid Dehyadegari, Sayyed Abdolmajid Jalaee
Date: 2023-12-24 00:00:00

The present study aimed to forecast the exports of a select group of Organization for Economic Co-operation and Development (OECD) countries and Iran using the neural networks. The data concerning the exports of the above countries from 1970 to 2019 were collected. The collected data were implemented to forecast the exports of the investigated countries for 2021 to 2025. The analysis was performed using the Multi-Layer-Perceptron (MLP) neural network in Python. Out of the total number, 75 percent were used as training data, and 25 percent were used as the test data. The findings of the study were evaluated with 99% accuracy, which indicated the reliability of the output of the network. The Results show that Covid-19 has affected exports over time. However, long-term export contracts are less affected by tensions and crises, due to the effect of exports on economic growth, per capita income and it is better for economic policies of countries to use long-term export contracts.