pwc-archive/papers-with-abstracts huggingface dataset:
Learning deep Koopman operators with convex stability constraints
Authors: Marc Mitjans, Liangting Wu, Roberto Tron
Date: 2024-04-24 00:00:00

In this paper, we present a novel sufficient condition for the stability of discrete-time linear systems that can be represented as a set of piecewise linear constraints, which make them suitable for quadratic programming optimization problems. More specifically, we tackle the problem of imposing asymptotic stability to a Koopman matrix learned from data during iterative gradient descent optimization processes. We show that this sufficient condition can be decoupled by rows of the system matrix, and propose a control barrier function-based projected gradient descent to enforce gradual evolution towards the stability set by running an optimization-in-the-loop during the iterative learning process. We compare the performance of our algorithm with other two recent approaches in the literature, and show that we get close to state-of-the-art performance while providing the added flexibility of allowing the optimization problem to be further customized for specific applications.
Showcasing Automated Vehicle Prototypes: A Collaborative Release Process to Manage and Communicate Risk
Authors: Marvin Loba, Robert Graubohm, Markus Maurer
Date: 2024-04-24 00:00:00
Tasks: valid

The development and deployment of automated vehicles pose major challenges for manufacturers to this day. Whilst central questions, like the issue of ensuring a sufficient level of safety, remain unanswered, prototypes are increasingly finding their way into public traffic in urban areas. Although safety concepts for prototypes are addressed in literature, published work hardly contains any dedicated considerations on a systematic release for their operation. In this paper, we propose an incremental release process for public demonstrations of prototypes' automated driving functionality. We explicate release process requirements, derive process design decisions, and define stakeholder tasks. Furthermore, we reflect on practical insights gained through implementing the release process as part of the UNICAR$agil$ research project, in which four prototypes based on novel vehicle concepts were built and demonstrated to the public. One observation is the improved quality of internal risk communication, achieved by dismantling information asymmetries between stakeholders. Design conflicts are disclosed - providing a contribution to nurture transparency and, thereby, supporting a valid basis for release decisions. We argue that our release process meets two important requirements, as the results suggest its applicability to the domain of automated driving and its scalability to different vehicle concepts and organizational structures.
Platooning of Heterogeneous Vehicles with Actuation Delays: Theoretical and Experimental Results
Authors: Redmer de Haan, Lorenzo Redi, Tom van der Sande, Erjen Lefeber
Date: 2024-04-24 00:00:00

In this paper we present a prediction-based Cooperative Adaptive Cruise Controller for vehicles with actuation delay, applicable within heterogeneous platoons. We provide a stability analysis for the discrete-time implementation of this controller, which shows the effect of the used sampling times and can be used for selecting appropriate controller gains. The theoretical results are validated by means of experiments using full scale vehicles. This is an extended version of a paper with the same title (submitted to IFAC TDS 2024). Additional mathematical details are provided in this extended version.
Input-Output Specifications of Grid-Forming Functions and Data-Driven Verification Methods
Authors: Jennifer T. Bui, Dominic Groß
Date: 2024-04-24 00:00:00

This work investigates interoperability and performance specifications for converter interfaced generation (CIG) that can be verified using only input-output data. First, we develop decentralized conditions on frequency stability that account for network circuit dynamics and can be verified using CIG terminal dynamics and a few key network parameters. Next, we formalize performance specifications that impose requirements on the CIG disturbance response. A simple data-driven validation method is presented that enables verification of the interoperability and performance specifications for CIG using input-output data from a two-node system. Data obtained from electromagnetic transient (EMT) simulations are used to illustrate the proposed approach and the impact of key parameters such as inner control loop gains, network coupling strength, and controller bandwidth limitations.
Recent Activities of a European Union Joint Research Project on Metrology for Emerging Wireless Standards
Authors: Tian Hong Loh, Tas Emrah, Frederic Pythoud, Wei Fan, Djamel Allal, Akram Alomainy
Date: 2024-04-24 00:00:00

Emerging wireless technologies with Gbps connectivity, such as the 5th generation (5G) and 6th generation (6G) of mobile networks, require improved and substantiating documentation for the wireless standards concerning the radio signals, systems, transmission environments used, and the radio frequency exposures created. Current challenges faced by the telecommunications sector include the lack of accurate, fast, low-cost, and traceable methods for manufacturers to demonstrate 5G and 6G product verifications matching customer specifications. This paper gives an update on the recent research and development activities from an EU Joint Research Project entitled metrology for emerging wireless standards (MEWS) in support of the above.
Achievements, Experiences, and Lessons Learned from the European Research Infrastructure ERIGrid related to the Validation of Power and Energy Systems
Authors: T. I. Strasser, E. C. W. de Jong, M. Sosnina, J. E. Rodriguez-Seco, P. Kotsampopoulos, D. Babazadeh, K. Mäki, R. Bhandia, R. Brandl, C. Sandroni, K. Heussen, F. Coffele
Date: 2024-04-24 00:00:00

Power system operation is of vital importance and must be developed far beyond today's practice to meet future needs. Almost all European countries are facing an abrupt and very important increase of renewables with intrinsically varying yields which are difficult to predict. In addition, an increase of new types of electric loads and a reduction of traditional production from bulk generation can be observed as well. Hence, the level of complexity of system operation steadily increases. Because of these developments, the traditional power system is being transformed into a smart grid. Previous and ongoing research has tended to focus on how specific aspects of smart grids can be developed and validated, but until now there exists no integrated approach for analysing and evaluating complex smart grid configurations. To tackle these research and development needs, a pan-European research infrastructure is realized in the ERIGrid project that supports the technology development as well as the roll out of smart grid technologies and solutions. This paper provides an overview of the main results of ERIGrid which have been achieved during the last four years. Also, experiences and lessons learned are discussed and an outlook to future research needs is provided.
Dynamic fault detection and diagnosis for alkaline water electrolyzer with variational Bayesian Sparse principal component analysis
Authors: Qi Zhang, Weihua Xu, Lei Xie, Hongye Su
Date: 2024-04-24 00:00:00
Tasks: Fault Detection

Electrolytic hydrogen production serves as not only a vital source of green hydrogen but also a key strategy for addressing renewable energy consumption challenges. For the safe production of hydrogen through alkaline water electrolyzer (AWE), dependable process monitoring technology is essential. However, random noise can easily contaminate the AWE process data collected in industrial settings, presenting new challenges for monitoring methods. In this study, we develop the variational Bayesian sparse principal component analysis (VBSPCA) method for process monitoring. VBSPCA methods based on Gaussian prior and Laplace prior are derived to obtain the sparsity of the projection matrix, which corresponds to $\ell_2$ regularization and $\ell_1$ regularization, respectively. The correlation of dynamic latent variables is then analyzed by sparse autoregression and fault variables are diagnosed by fault reconstruction. The effectiveness of the method is verified by an industrial hydrogen production process, and the test results demonstrated that both Gaussian prior and Laplace prior based VBSPCA can effectively detect and diagnose critical faults in AWEs.
Research on OPF control of three-phase four-wire low-voltage distribution network considering uncertainty
Authors: Rui Wang, Xiaoqing Bai, Shengquan Huang, Shoupu Wei
Date: 2024-04-24 00:00:00
Tasks: Stochastic Optimization

As power systems become more complex and uncertain, low-voltage distribution networks face numerous challenges, including three-phase imbalances caused by asymmetrical loads and distributed energy resources. We propose a robust stochastic optimization (RSO) based optimal power flow (OPF) control method for three-phase, four-wire low-voltage distribution networks that consider uncertainty to address these issues. Using historical data and deep learning classification methods, the proposed method simulates optimal system behaviour without requiring communication infrastructure. The simulation results verify that the proposed method effectively controls the voltage and current amplitude while minimizing the operational cost and three-phase imbalance within acceptable limits. The proposed method shows promise for managing uncertainties and optimizing performance in low-voltage distribution networks.
Designing, simulating, and performing the 100-AV field test for the CIRCLES consortium: Methodology and Implementation of the Largest mobile traffic control experiment to date
Authors: Mostafa Ameli, Sean McQuade, Jonathan W. Lee, Matthew Bunting, Matthew Nice, Han Wang, William Barbour, Ryan Weightman, Chris Denaro, Ryan Delorenzo, Sharon Hornstein, Jon F. Davis, Dan Timsit, Riley Wagner, Rita Xu, Malaika Mahmood, Mikail Mahmood, Maria Laura Delle Monache, Benjamin Seibold, Daniel B. Work, Jonathan Sprinkle, Benedetto Piccoli, Alexandre M. Bayen
Date: 2024-04-23 00:00:00

Previous controlled experiments on single-lane ring roads have shown that a single partially autonomous vehicle (AV) can effectively mitigate traffic waves. This naturally prompts the question of how these findings can be generalized to field operational, high-density traffic conditions. To address this question, the Congestion Impacts Reduction via CAV-in-the-loop Lagrangian Energy Smoothing (CIRCLES) Consortium conducted MegaVanderTest (MVT), a live traffic control experiment involving 100 vehicles near Nashville, TN, USA. This article is a tutorial for developing analytical and simulation-based tools essential for designing and executing a live traffic control experiment like the MVT. It presents an overview of the proposed roadmap and various procedures used in designing, monitoring, and conducting the MVT, which is the largest mobile traffic control experiment at the time. The design process is aimed at evaluating the impact of the CIRCLES AVs on surrounding traffic. The article discusses the agent-based traffic simulation framework created for this evaluation. A novel methodological framework is introduced to calibrate this microsimulation, aiming to accurately capture traffic dynamics and assess the impact of adding 100 vehicles to existing traffic. The calibration model's effectiveness is verified using data from a six-mile section of Nashville's I-24 highway. The results indicate that the proposed model establishes an effective feedback loop between the optimizer and the simulator, thereby calibrating flow and speed with different spatiotemporal characteristics to minimize the error between simulated and real-world data. Finally, We simulate AVs in multiple scenarios to assess their effect on traffic congestion. This evaluation validates the AV routes, thereby contributing to the execution of a safe and successful live traffic control experiment via AVs.
Deep Hankel matrices with random elements
Authors: Nathan P. Lawrence, Philip D. Loewen, Shuyuan Wang, Michael G. Forbes, R. Bhushan Gopaluni
Date: 2024-04-23 00:00:00
Tasks: LEMMA

Willems' fundamental lemma enables a trajectory-based characterization of linear systems through data-based Hankel matrices. However, in the presence of measurement noise, we ask: Is this noisy Hankel-based model expressive enough to re-identify itself? In other words, we study the output prediction accuracy from recursively applying the same persistently exciting input sequence to the model. We find an asymptotic connection to this self-consistency question in terms of the amount of data. More importantly, we also connect this question to the depth (number of rows) of the Hankel model, showing the simple act of reconfiguring a finite dataset significantly improves accuracy. We apply these insights to find a parsimonious depth for LQR problems over the trajectory space.
An Alternative Method to Identify the Susceptibility Threshold Level of Device under Test in a Reverberation Chamber
Authors: Qian Xu, Kai Chen, Xueqi Shen, Lei Xing, Yi Huang, Tian Hong Loh
Date: 2024-04-23 00:00:00

By counting the number of pass/fail occurrences of a DUT (Device under Test) in the stirring process in a reverberation chamber (RC), the threshold electric field (E-field) level can be well estimated without tuning the input power and repeating the whole testing many times. The Monte-Carlo method is used to verify the results. Estimated values and uncertainties are given for Rayleigh distributed fields and for Rice distributed fields with different K-factors.
A Realisation of Channel Emulation in a Reverberation Chamber method for Over-the-Air Compliance Testing in Support of 3GPP Standardisation
Authors: Yunsong Gui, Tian Hong Loh
Date: 2024-04-23 00:00:00

The inherent long decay power delay profile (PDP) in the reverberation chamber (RC) is a major challenge for accurate channel emulation of 3GPP channel model, which is widely used in performance test of the physical layer. To tackle this challenge, we propose in this paper a novel two-step "closed-loop" approach consisting of (i) a channel measuring step and (ii) a channel model synthesis step. The channel measurement step is used to capture the wireless channel of the RC. In the channel model synthesis step, an additional IQ signal convolution process is introduced prior the IQ signal passes through the channel emulator (CE). This process filters the IQ signal by an equalizer filter derived from the measured channel impulse response (CIR) of the RC obtained in channel measurement step. From the measurement results, the proposed approach is proven that able to effectively emulate typical 3GPP 5G channel model.
Scandium Aluminum Nitride Overmoded Bulk Acoustic Resonators for Future Wireless Communication
Authors: Walter Gubinelli, Pietro Simeoni, Ryan Tetro, Luca Colombo, Matteo Rinaldi
Date: 2024-04-23 00:00:00

This work reports on the modeling, fabrication, and experimental characterization of a 13 GHz 30% Scandium-doped Aluminum Nitride (ScAlN) Overmoded Bulk Acoustic Resonator (OBAR) for high-frequency Radio Frequency (RF) applications, notably in 5G technology and beyond. The Finite Element Analysis (FEA) optimization process targets the top and bottom metal electrode thicknesses, balancing the electromechanical coupling coefficient and acoustic energy distribution to enhance device Figure of Merit (FOM). Experimental results on fabricated devices employing platinum and aluminum as bottom and top electrode, respectively, demonstrate a quality factor at resonance (Qs) of 210 and a coupling coefficient (kt2) of 5.2% at 13.3 GHz for the second bulk thickness overtone, effectively validating the simulation framework and hinting at the possible implementation of OBARs for advanced RF filters in 5G networks.
A novel mathematical model for predicting the benefits of physical activity on type 2 diabetes progression
Authors: Pierluigi Francesco De Paola, Alessandro Borri, Fabrizio Dabbene, Karim Keshavjee, Pasquale Palumbo, Alessia Paglialonga
Date: 2024-04-23 00:00:00

Despite the well-acknowledged benefits of physical activity for type 2 diabetes (T2D) prevention, the literature surprisingly lacks validated models able to predict the long-term benefits of exercise on T2D progression and support personalized risk prediction and prevention. To bridge this gap, we developed a novel mathematical model that formalizes the link between exercise and short- and long-term glucose-insulin dynamics to predict the benefits of regular exercise on T2D progression. The model quantitatively captured the dose-response relationship (larger benefits with increasing intensity and/or duration of exercise), it consistently reproduced the benefits of clinical guidelines for diabetes prevention, and it accurately predicted persistent benefits following interruption of physical activity, in line with real-world evidence from the literature. These results are encouraging and can be the basis for future development of decision support tools able to assist patients and clinicians in tailoring preventive lifestyle interventions.
PRoTECT: Parallelized Construction of Safety Barrier Certificates for Nonlinear Polynomial Systems
Authors: Ben Wooding, Viacheslav Horbanov, Abolfazl Lavaei
Date: 2024-04-23 00:00:00

We develop an open-source software tool, called PRoTECT, for the parallelized construction of safety barrier certificates (BCs) for nonlinear polynomial systems. This tool employs sum-of-squares (SOS) optimization programs to systematically search for polynomial-type BCs, while aiming to verify safety properties over four classes of dynamical systems: (i) discrete-time stochastic systems, (ii) discrete-time deterministic systems, (iii) continuous-time stochastic systems, and (iv) continuous-time deterministic systems. PRoTECT is implemented in Python as an application programming interface (API), offering users the flexibility to interact either through its user-friendly graphic user interface (GUI) or via function calls from other Python programs. PRoTECT leverages parallelism across different barrier degrees to efficiently search for a feasible BC.
Antifragile control systems in neuronal processing: A sensorimotor perspective
Authors: Cristian Axenie
Date: 2024-04-23 00:00:00

The stability--robustness--resilience--adaptiveness continuum in neuronal processing follows a hierarchical structure that explains interactions and information processing among the different time scales. Interestingly, using "canonical" neuronal computational circuits, such as Homeostatic Activity Regulation, Winner-Take-All, and Hebbian Temporal Correlation Learning, one can extend the behaviour spectrum towards antifragility. Cast already in both probability theory and dynamical systems, antifragility can explain and define the interesting interplay among neural circuits, found, for instance, in sensorimotor control in the face of uncertainty and volatility. This perspective proposes a new framework to analyse and describe closed-loop neuronal processing using principles of antifragility, targeting sensorimotor control. Our objective is two-fold. First, we introduce antifragile control as a conceptual framework to quantify closed-loop neuronal network behaviours that gain from uncertainty and volatility. Second, we introduce neuronal network design principles, opening the path to neuromorphic implementations and transfer to technical systems.
Remaining Discharge Energy Prediction for Lithium-Ion Batteries Over Broad Current Ranges: A Machine Learning Approach
Authors: Hao Tu, Manashita Borah, Scott Moura, Yebin Wang, Huazhen Fang
Date: 2024-04-23 00:00:00

Lithium-ion batteries have found their way into myriad sectors of industry to drive electrification, decarbonization, and sustainability. A crucial aspect in ensuring their safe and optimal performance is monitoring their energy levels. In this paper, we present the first study on predicting the remaining energy of a battery cell undergoing discharge over wide current ranges from low to high C-rates. The complexity of the challenge arises from the cell's C-rate-dependent energy availability as well as its intricate electro-thermal dynamics especially at high C-rates. To address this, we introduce a new definition of remaining discharge energy and then undertake a systematic effort in harnessing the power of machine learning to enable its prediction. Our effort includes two parts in cascade. First, we develop an accurate dynamic model based on integration of physics with machine learning to capture a battery's voltage and temperature behaviors. Second, based on the model, we propose a machine learning approach to predict the remaining discharge energy under arbitrary C-rates and pre-specified cut-off limits in voltage and temperature. The experimental validation shows that the proposed approach can predict the remaining discharge energy with a relative error of less than 3% when the current varies between 0~8 C for an NCA cell and 0~15 C for an LFP cell. The approach, by design, is amenable to training and computation.
Emergent Cooperation for Energy-efficient Connectivity via Wireless Power Transfer
Authors: Winston Hurst, Anurag Pallaprolu, Yasamin Mostofi
Date: 2024-04-23 00:00:00

This paper addresses the challenge of incentivizing energy-constrained, non-cooperative user equipment (UE) to serve as cooperative relays. We consider a source UE with a non-line-of-sight channel to an access point (AP), where direct communication may be infeasible or may necessitate a substantial transmit power. Other UEs in the vicinity are viewed as relay candidates, and our aim is to enable energy-efficient connectivity for the source, while accounting for the self-interested behavior and private channel state information of these candidates, by allowing the source to ``pay" the candidates via wireless power transfer (WPT). We propose a cooperation-inducing protocol, inspired by Myerson auction theory, which ensures that candidates truthfully report power requirements while minimizing the expected power used by the source. Through rigorous analysis, we establish the regularity of valuations for lognormal fading channels, which allows for the efficient determination of the optimal source transmit power. Extensive simulation experiments, employing real-world communication and WPT parameters, validate our theoretical framework. Our results demonstrate over 71% reduction in outage probability with as few as 4 relay candidates, compared to the non-cooperative scenario, and as much as 70% source power savings compared to a baseline approach, highlighting the efficacy of our proposed methodology.