pwc-archive/papers-with-abstracts huggingface dataset:
Analyzing Character and Consciousness in AI-Generated Social Content: A Case Study of Chirper, the AI Social Network
Authors: Jianwei Luo
Date: 2023-08-30 00:00:00

This paper delves into an intricate analysis of the character and consciousness of AI entities, with a particular focus on Chirpers within the AI social network. At the forefront of this research is the introduction of novel testing methodologies, including the Influence index and Struggle Index Test, which offers a fresh lens for evaluating specific facets of AI behavior. The study embarks on a comprehensive exploration of AI behavior, analyzing the effects of diverse settings on Chirper's responses, thereby shedding light on the intricate mechanisms steering AI reactions in different contexts. Leveraging the state-of-the-art BERT model, the research assesses AI's ability to discern its own output, presenting a pioneering approach to understanding self-recognition in AI systems. Through a series of cognitive tests, the study gauges the self-awareness and pattern recognition prowess of Chirpers. Preliminary results indicate that Chirpers exhibit a commendable degree of self-recognition and self-awareness. However, the question of consciousness in these AI entities remains a topic of debate. An intriguing aspect of the research is the exploration of the potential influence of a Chirper's handle or personality type on its performance. While initial findings suggest a possible impact, it isn't pronounced enough to form concrete conclusions. This study stands as a significant contribution to the discourse on AI consciousness, underscoring the imperative for continued research to unravel the full spectrum of AI capabilities and the ramifications they hold for future human-AI interactions.
PINN-based viscosity solution of HJB equation
Authors: Tianyu Liu, Steven Ding, Jiarui Zhang, Liutao Zhou
Date: 2023-09-18 00:00:00

This paper proposed a novel PINN-based viscosity solution for HJB equations. Although there exists work using PINN to solve HJB, but none of them gives the solution in viscosity sense. This paper reveals the fact that using the convex neural network, one can guarantee the viscosity solution and thus the neural network can easily converge to the true solution of HJB despite of the starting point.
Energy Management of Hydrogen Hybrid Electric Vehicles -- A Potential Study
Authors: David Theodor Machacek, Nazim Ozan Yazar, Thomas Huber, Christopher Harald Onder
Date: 2023-09-18 00:00:00
Tasks: energy management, Management

The hydrogen combustion engine (H$_2$ICE) is known to be able to burn H$_2$ under ultra-lean conditions, while producing no CO$_2$ emissions and extremely low engine-out NO$_x^{\mathrm{eo}}$ emissions. Immediate goals, as for instance the upcoming EURO 7 NO$_x$ limitations, can be reached more easily as extremely low engine-out NO$_x^{\mathrm{eo}}$ emissions facilitate the reduction of the overall tailpipe NO$_x^{\mathrm{tp}}$ emissions. In this work, the feasibility of achieving consistent reductions in NO$_x^{\mathrm{eo}}$ emissions through the implementation of electric hybridization of an H$_2$ICE-equipped passenger car (H$_2$-HEV), combined with a dedicated energy management strategy (EMS) is discussed. In particular, the mixed H$_2$-HEV architecture is investigated and compared to a series H$_2$-HEV, a parallel H$_2$-HEV, and a base H$_2$-vehicle, which is only equipped with an H$_2$ICE. For hybrid vehicles, a low H$_2$ consumption and low NO$_x^{\mathrm{eo}}$ emissions are conflicting objectives, the trade-off of which depends on the EMS and can be represented as a Pareto front. Overall, through the utilization of a dedicated energy management calibration, the mixed H$_2$-HEV demonstrates the capability to consistently achieve extremely low engine-out NO$_x^{\mathrm{eo}}$ emissions. For a broad range of driving missions, the mixed H$_2$-HEV is able to decrease the engine-out NO$_\mathrm{x}^\mathrm{eo}$ emissions by more than 90%, while, at the same time, the H$_2$ consumption is decreased by over 16%, compared to a comparable non-hybridized H$_2$-vehicle. These significant emission reductions are possible without having to modify the exhaust-gas aftertreatment system, or the optimization of any of the individual drivetrain components, but solely by setting the EMS calibration accordingly.
Electric Aircraft Assignment, Routing, and Charge Scheduling Considering the Availability of Renewable Energy
Authors: Finn Vehlhaber, Mauro Salazar
Date: 2023-09-18 00:00:00
Tasks: Scheduling

Electric airplanes are expected to take to the skies soon, finding first use cases in small networks within hardly accessible areas, such as island communities. In this context, the environmental footprint of such airplanes will be strongly determined by the energy sources employed when charging them. This paper presents a framework to optimize aircraft assignment, routing and charge schedules explicitly accounting for the energy availability at the different airports, which are assumed to be equipped with renewable energy sources and stationary batteries. Specifically, considering the daily travel demand and weather conditions forecast in advance, we first capture the aircraft operations within a time-expanded directed acyclic graph, and combine it with a dynamic energy model of the individual airports. Second, aiming at minimizing grid-dependency, we leverage our models to frame the optimal electric aircraft and airport operational problem as a mixed-integer linear program that can be solved with global optimality guarantees. Finally, we showcase our framework in a real-world case-study considering one week of operations on the Dutch Leeward Antilles. Our results show that, depending on weather conditions and compared to current schedules, optimizing flights and operations in a renewable-energy-aware manner can reduce grid dependency from 18 to 100%, whilst significantly shrinking the operational window of the airplanes.
System-level Testing of the Congestion Management Capability of a Hardware-Independent Optimal Power Flow Algorithm
Authors: Thomas Schwierz, Rajkumar Palaniappan, Oleksii Molodchyk, Christian Rehtanz
Date: 2023-09-18 00:00:00
Tasks: Management

The integration of distributed energy resources (DERs) into the electrical grid causes various challenges in the distribution grids. The complexity of smart grids as multi-domain energy systems requires innovative architectures and algorithms for system control. While these solutions are good on paper, several testing methods are required to test the applicability of components, functions and entire systems to the existing energy grids. In this paper, a full-scale low-voltage test setup in the Smart Grid Technology Lab (SGTL) at TU Dortmund University is used to evaluate the capability of an Optimal Power Flow Algorithm (OPF) to support voltage control, congestion management, and to provide redispatch to the higher grid levels. While conventional redispatch is commonly done preemptively, this paper analyses the possibility of providing redispatch to the higher voltage levels without taking the future grid state into consideration. The importance of this implementation is that the smart grid application used to execute the OPF is configured based on IEC 61850 data models, making the software independent of the hardware. Such standardised control algorithms are interoperable and can be implemented on any hardware that suits the requirements.
Integrated Model Predictive Control of High-Speed Railway Running Gears with Driven Independently Rotating Wheels
Authors: Jan-Hendrik Ewering, Christoph Schwarz, Simon F. G. Ehlers, Hans-Georg Jacob, Thomas Seel, Andreas Heckmann
Date: 2023-09-18 00:00:00
Tasks: Model Predictive Control

Railway running gears with independently rotating wheels (IRW) can significantly improve wear figures, comfort, and safety in railway transportation, but certain measures for wheelset stabilization are required. This is one reason why the application of traditional wheelsets is still common practice in industry. Apart from lateral guidance, the longitudinal control is of crucial importance for railway safety. In the current contribution, an integrated controller for joined lateral and longitudinal control of a high-speed railway running gear with driven IRW is designed. To this end, a novel adhesion-based traction control law is combined with linear time-variant and nonlinear model predictive control (MPC) schemes for lateral guidance. The MPC schemes are able to use tabulated track geometry data and preview information about set points to minimize the lateral displacement error. Co-simulation results with a detailed multi-body simulation show the effectiveness of the approach compared with state-of-the-art techniques in various scenarios, including curving, varying velocities up to 400 km/h and abruptly changing wheel-rail adhesion conditions.
Learning Optimal Robust Control of Connected Vehicles in Mixed Traffic Flow
Authors: Jie Li, Jiawei Wang, Shengbo Eben Li, Keqiang Li
Date: 2023-09-18 00:00:00

Connected and automated vehicles (CAVs) technologies promise to attenuate undesired traffic disturbances. However, in mixed traffic where human-driven vehicles (HDVs) also exist, the nonlinear human-driving behavior has brought critical challenges for effective CAV control. This paper employs the policy iteration method to learn the optimal robust controller for nonlinear mixed traffic systems. Precisely, we consider the H_infty control framework and formulate it as a zero-sum game, the equivalent condition for whose solution is converted into a Hamilton-Jacobi inequality with a Hamiltonian constraint. Then, a policy iteration algorithm is designed to generate stabilizing controllers with desired attenuation performance. Based on the updated robust controller, the attenuation level is further optimized in sum of squares program by leveraging the gap of the Hamiltonian constraint. Simulation studies verify that the obtained controller enables the CAVs to dampen traffic perturbations and smooth traffic flow.
Integrating Vertidrome Management Tasks into U-space
Authors: Bianca I. Schuchardt, Aditya Devta, Andreas Volkert
Date: 2023-09-18 00:00:00
Tasks: Management, Scheduling

U-space as defined by the European Commission is a set of new services relying on a high level of digitalization and automation of functions and specific procedures, designed to provide safe, efficient and secure access to airspace for large numbers of unmanned aircraft, operating automatically and even beyond visual line of sight. This kind of concepts of operations (ConOps) of airspace integration for drones and air taxis are also called UTM (Unmanned aircraft system Traffic Management) systems, being U-space the UTM ConOps agreed for Europe. U-space services are under development but commercially not available yet. For demonstration purposes, in the project HorizonUAM, a central U-space cloud service is simulated through a local messaging server using the protocol MQTT (Message Queuing Telemetry Transport). A prototypical vertidrome management tool was created to demonstrate the scheduling and sequencing of air taxi flights. The vertidrome manager is fully integrated within U-space and receives real-time information on flight plans, including requests for start and landing and emergency notifications. Additional information coming from other U-space services (e.g. weather information) can be accessed on request. The integration was demonstrated in a scaled flight test environment with multicopters (<15 kg) representing passenger carrying air taxis.
A Distributed Strategy to Maximize Coverage in a Heterogeneous Sensor Network in the Presence of Obstacles
Authors: Hesam Mosalli, Amir G. Aghdam
Date: 2023-09-17 00:00:00
Tasks: Position

In this paper, an efficient deployment strategy is proposed for a network of mobile and static sensors with nonidentical sensing and communication radii. The multiplicatively weighted Voronoi (MW-Voronoi) diagram is used to partition the field and assign the underlying coverage task to each mobile sensor. A gradient-based method is applied to find the best candidate point based on the detected coverage holes and the coverage priority considering the relative distance of the mobile sensor from the static ones and the obstacles in the field. The sensors move to a new position if such a relocation increases their local coverage. The efficiency of the proposed strategy in different scenarios is demonstrated by simulations.
Data Center-Enabled High Altitude Platforms: A Green Computing Alternative
Authors: Wiem Abderrahim, Osama Amin, Basem Shihada
Date: 2023-09-17 00:00:00

Information technology organizations and companies are seeking greener alternatives to traditional terrestrial data centers to mitigate global warming and reduce carbon emissions. Currently, terrestrial data centers consume a significant amount of energy, estimated at about 1.5% of worldwide electricity use. Furthermore, the increasing demand for data-intensive applications is expected to raise energy consumption, making it crucial to consider sustainable computing paradigms. In this study, we propose a data center-enabled High Altitude Platform (HAP) system, where a flying data center supports the operation of terrestrial data centers. We conduct a detailed analytical study to assess the energy benefits and communication requirements of this approach. Our findings demonstrate that a data center-enabled HAP is more energy-efficient than a traditional terrestrial data center, owing to the naturally low temperature in the stratosphere and the ability to harvest solar energy. Adopting a data center-HAP can save up to 14% of energy requirements while overcoming the offloading outage problem and the associated delay resulting from server distribution. Our study highlights the potential of a data center-enabled HAP system as a sustainable computing solution to meet the growing energy demands and reduce carbon footprint.
Data-driven control of nonlinear systems from input-output data
Authors: Xiaoyan Dai, Claudio De Persis, Nima Monshizadeh, Pietro Tesi
Date: 2023-09-17 00:00:00

The design of controllers from data for nonlinear systems is a challenging problem. In a recent paper, De Persis, Rotulo and Tesi, "Learning controllers from data via approximate nonlinearity cancellation," IEEE Transactions on Automatic Control, 2023, a method to learn controllers that make the closed-loop system stable and dominantly linear was proposed. The approach leads to a simple solution based on data-dependent semidefinite programs. The method uses input-state measurements as data, while in a realistic setup it is more likely that only input-output measurements are available. In this note we report how the design principle of the above mentioned paper can be adjusted to deal with input-output data and obtain dynamic output feedback controllers in a favourable setting.
Consensus-Based Leader-Follower Formation Tracking for Control-Affine Nonlinear Multiagent Systems
Authors: Clinton Enwerem, John S. Baras
Date: 2023-09-17 00:00:00

In the typical multiagent formation tracking problem centered on consensus, the prevailing assumption in the literature is that the agents' nonlinear models can be approximated by integrator systems, by their feedback-linearized equivalents, or by dynamics composed of deterministic linear and nonlinear terms. The resulting approaches associated with such assumptions, however, are hardly applicable to general nonlinear systems. To this end, we present consensus-based control laws for multiagent formation tracking in finite-dimensional state space, with the agents represented by a more general class of dynamics: control-affine nonlinear systems. The agents also exchange information via a leader-follower communication topology modeled as an undirected and connected graph with a single leader node. By leveraging standard tools from algebraic graph theory and Lyapunov analysis, we first derive a locally asymptotically stabilizing formation tracking law. Next, to demonstrate the effectiveness of our approach, we present results from numerical simulations of an example in robotics. These results -- together with a comparison of the formation errors obtained with our approach and those realized via an optimization-based method -- further validate our theoretical propositions.
A Contracting Dynamical System Perspective toward Interval Markov Decision Processes
Authors: Saber Jafarpour, Samuel Coogan
Date: 2023-09-17 00:00:00

Interval Markov decision processes are a class of Markov models where the transition probabilities between the states belong to intervals. In this paper, we study the problem of efficient estimation of the optimal policies in Interval Markov Decision Processes (IMDPs) with continuous action-space. Given an IMDP, we show that the pessimistic (resp. the optimistic) value iterations, i.e., the value iterations under the assumption of a competitive adversary (resp. cooperative agent), are monotone dynamical systems and are contracting with respect to the $\ell_{\infty}$-norm. Inspired by this dynamical system viewpoint, we introduce another IMDP, called the action-space relaxation IMDP. We show that the action-space relaxation IMDP has two key features: (i) its optimal value is an upper bound for the optimal value of the original IMDP, and (ii) its value iterations can be efficiently solved using tools and techniques from convex optimization. We then consider the policy optimization problems at each step of the value iterations as a feedback controller of the value function. Using this system-theoretic perspective, we propose an iteration-distributed implementation of the value iterations for approximating the optimal value of the action-space relaxation IMDP.
Intermittent Control for Safe Long-Acting Insulin Intensification for Type 2 Diabetes: In-Silico Experiment
Authors: Anas El Fathi, Mohammadreza Ganji, Dimitri Boiroux, Henrik Bengtsson, Marc D. Breton
Date: 2023-09-17 00:00:00

Around a third of type 2 diabetes patients (T2D) are escalated to basal insulin injections. Basal insulin dose is titrated to achieve a tight glycemic target without undue hypoglycemic risk. In the standard of care (SoC), titration is based on intermittent fasting blood glucose (FBG) measurements. Lack of adherence and the day-to-day variabilities in FBG measurements are limiting factors to the existing insulin titration procedure. We propose an adaptive receding horizon control strategy where a glucose-insulin fasting model is identified and used to predict the optimal basal insulin dose. This algorithm is evaluated in \textit{in-silico} experiments using the new UVA virtual lab (UVlab) and a set of T2D avatars matched to clinical data (NCT01336023). Compared to SoC, we show that this control strategy can achieve the same glucose targets faster (as soon as week 8) and safer (increased hypoglycemia protection and robustness to missing FBG measurements). Specifically, when insulin is titrated daily, a time-in-range (TIR, 70--180 mg/dL) of 71.4$\pm$20.0\% can be achieved at week eight and maintained at week 52 (72.6$\pm$19.6%) without an increased hypoglycemia risk as measured by time under 70 mg/dL (TBR, week 8: 1.3$\pm$1.9% and week 52: 1.2$\pm$1.9%), when compared to the SoC (TIR at week 8: 59.3$\pm$28.0% and week:52 72.1$\pm$22.3%, TBR at week 8: 0.5$\pm$1.3% and week 52: 2.8$\pm$3.4%). Such an approach can potentially reduce treatment inertia and prescription complexity, resulting in improved glycemic outcomes for T2D using basal insulin injections.
Data-driven input-to-state stabilization with respect to measurement errors
Authors: Hailong Chen, Andrea Bisoffi, Claudio De Persis
Date: 2023-09-16 00:00:00

We consider noisy input/state data collected from an experiment on a polynomial input-affine nonlinear system. Motivated by event-triggered control, we provide data-based conditions for input-to-state stability with respect to measurement errors. Such conditions, which take into account all dynamics consistent with data, lead to the design of a feedback controller, an ISS Lyapunov function, and comparison functions ensuring ISS with respect to measurement errors. When solved alternately for two subsets of the decision variables, these conditions become a convex sum-of-squares program. Feasibility of the program is illustrated with a numerical example.
Comprehensive Investigation and Evaluation of an Indoor 3D System Performance Based on Visible Light Communication
Authors: Vailet Hikmat Faraj Al Khattat, Siti Barirah Ahmad Anas, Abdu Saif
Date: 2023-09-16 00:00:00

The abstract discusses the significance of Visible Light Communication (VLC) as an efficient and cost-effective solution in the era of green technology. VLC not only provides illumination but also high-speed data transmission through existing infrastructure, making it ideal for indoor positioning systems (IPS) with minimal interference with the Radio Frequency (RF) spectrum and enhanced security. While previous research has mainly focused on positioning accuracy, this paper delves into the performance evaluation of a VLC-based indoor system. The study examines key performance parameters, namely Signal-to-Noise Ratio (SNR) and path loss, in a Line of Sight (LOS) scenario. It employs a single LED and ten different photodiode (PD) locations in a 3D room. MATLAB simulations demonstrate the system's effectiveness, achieving a good SNR with low path loss. Additionally, the research highlights the importance of optimizing the PD's position to maximize signal strength while minimizing noise and losses.
Computational Enhancement for Day-Ahead Energy Scheduling with Sparse Neural Network-based Battery Degradation Model
Authors: Cunzhi Zhao, Xingpeng Li
Date: 2023-09-16 00:00:00
Tasks: Scheduling, SENTS

Battery energy storage systems (BESS) play a pivotal role in future power systems as they contribute to achiev-ing the net-zero carbon emission objectives. The BESS systems, predominantly employing lithium-ion batteries, have been exten-sively deployed. The degradation of these batteries significantly affects system efficiency. Deep neural networks can accurately quantify the battery degradation, however, the model complexity hinders their applications in energy scheduling for various power systems at different scales. To address this issue, this paper pre-sents a novel approach, introducing a linearized sparse neural network-based battery degradation model (SNNBD), specifically tailored to quantify battery degradation based on the scheduled BESS daily operational profiles. By leveraging sparse neural networks, this approach achieves accurate degradation predic-tion while substantially reducing the complexity associated with a dense neural network model. The computational burden of inte-grating battery degradation into day-ahead energy scheduling is thus substantially alleviated. Case studies, conducted on both microgrids and bulk power grids, demonstrated the efficiency and suitability of the proposed SNNBD-integrated scheduling model that can effectively address battery degradation concerns while optimizing day-ahead energy scheduling operations.
RNN Controller for Lane-Keeping Systems with Robustness and Safety Verification
Authors: Ying Shuai Quan, Jin Sung Kim, Chung Choo Chung
Date: 2023-09-16 00:00:00

This paper proposes a Recurrent Neural Network (RNN) controller for lane-keeping systems, effectively handling model uncertainties and disturbances. First, quadratic constraints cover the nonlinearities brought by the RNN controller, and the linear fractional transformation method models the dynamics of system uncertainties. Second, we prove the robust stability of the lane-keeping system in the presence of uncertain vehicle speed using a linear matrix inequality. Then, we define a reachable set for the lane-keeping system. Finally, to confirm the safety of the lane-keeping system with tracking error bound, we formulate semidefinite programming to approximate the outer set of the reachable set. Numerical experiments demonstrate that this approach confirms the stabilizing RNN controller and validates the safety with an untrained dataset with untrained varying road curvatures.