pwc-archive/papers-with-abstracts huggingface dataset:
Suitability of Common Ingestible Antennas for Multiplexed Gastrointestinal Biosensing
Authors: Erdem Cil, Icaro V. Soares, Domitille Schanne, Ronan Sauleau, Denys Nikolayev
Date: 2024-07-05 00:00:00

Ingestible sensor devices, which are increasingly used for internal health monitoring, rely on antennas to perform sensing functions and simultaneously to communicate with external devices. Despite the development of various ingestible antennas, there has been no comprehensive comparison of their performance as biosensors. This paper addresses this gap by examining and comparing the suitability of three common types of ingestible antennas -- dipole, patch, and loop -- as biosensors for distinguishing gastrointestinal tissues (stomach, small intestine, and large intestine) based on their electromagnetic properties. The antennas studied in this work conform to the inner surface of biocompatible polylactic acid capsules with varying shell thicknesses and operate in the 433 MHz Industrial, Scientific, and Medical band. The comparison is performed in gastrointestinal tissues using several antenna parameters: 1) Sensing Capability: Changes in the phase of the reflection coefficient in the tissues are selected as the sensing parameter. 2) Robustness: The frequency interval (f_i) in which the antennas are matched (|S11| < -10 dB) in all the tissues and the maximum change in the center frequency (f_c) in different tissues are examined. 3) Radiation Performance: The gain and radiation efficiency of the antennas are examined. The effect of shell thickness on gain and radiation efficiency at 434 MHz is presented. Additionally, the radiation efficiency at various frequencies allocated for medical communications is compared with the theoretical maximum achievable efficiencies. These comprehensive data provide valuable information for making engineering decisions when designing multiplexed biosensor antennas for ingestible applications.
Balancing Operators Risk Averseness in Model Predictive Control for Real-time Reservoir Flood Control
Authors: Ja-Ho Koo, Edo Abraham, Andreja Jonoski, Dimitri P. Solomatine
Date: 2024-07-05 00:00:00
Tasks: Model Predictive Control

Model Predictive Control (MPC) is an optimal control strategy suited for flood control of water resources infrastructure. Despite many studies on reservoir flood control and their theoretical contribution, optimisation methodologies have not been widely applied in real-time operation due to disparities between research assumptions and practical requirements. To address this gap, we include practical objectives, such as minimising the magnitude and frequency of changes in the existing outflow schedule. Incorporating these objectives transforms the problem into a multi-objective nonlinear optimisation problem that is difficult to solve in real-time. Additionally, it is reasonable to assume that the weights and some parameters, considered the operators' preferences, vary depending on the system state. To overcome these limitations, we propose a framework that converts the original intractable problem into parameterized linear MPC problems with dynamic optimisation of weights and parameters. This is done by introducing a model-based learning concept. We refer to this framework as Parameterised Dynamic MPC (PD-MPC). The effectiveness of this framework is demonstrated through a numerical experiment for the Daecheong multipurpose reservoir in South Korea. We find that PD-MPC outperforms standard MPC-based designs without a dynamic optimisation process for the objective weights and model parameters. Moreover, we demonstrate that the weights and parameters vary with changing hydrological conditions.
LFT modelling and $μ$-based robust performance analysis of hybrid multi-rate control systems
Authors: Jean-Marc Biannic, Clément Roos, Christelle Cumer
Date: 2024-07-05 00:00:00

This paper focuses on robust stability and $H_\infty$ performance analyses of hybrid continuous/discrete time linear multi-rate control systems in the presence of parametric uncertainties. These affect the continuous-time plant in a rational way which is then modeled as a Linear Fractional Transformation (LFT). Based on a zero-order-hold (ZOH) LFT discretization process at the cost of bounded quantifiable approximations, and then using LFT-preserving down-sampling operations, a single-rate discrete-time closed-loop LFT model is derived. Interestingly, for any step inputs, and any admissible values of the uncertain parameters, the outputs of this model cover those of the initial hybrid multi-rate closed-loop system at every sampling time of the slowest control loop. Such an LFT model, which also captures the discretization errors, can then be used to evaluate both robust stability and guaranteed $H_\infty$ performance with a $\mu$-based approach. The proposed methodology is illustrated on a realistic and easily reproducible example inspired by the validation of multi-rate attitude control systems.
Nash epidemics
Authors: Simon K. Schnyder, John J. Molina, Ryoichi Yamamoto, Matthew S. Turner
Date: 2024-07-05 00:00:00
Tasks: Decision Making

Faced with a dangerous epidemic humans will spontaneously social distance to reduce their risk of infection at a socio-economic cost. Compartmentalised epidemic models have been extended to include this endogenous decision making: Individuals choose their behaviour to optimise a utility function, self-consistently giving rise to population behaviour. Here we study the properties of the resulting Nash equilibria, in which no member of the population can gain an advantage by unilaterally adopting different behaviour. We leverage a new analytic solution to obtain, (1) a simple relationship between rational social distancing behaviour and the current number of infections; (2) new scaling results for how the infection peak and number of total cases depend on the cost of contracting the disease; (3) characteristic infection costs that divide regimes of strong and weak behavioural response and depend only on the basic reproduction number of the disease; (4) a closed form expression for the value of the utility. We discuss how these analytic results provide a deep and intuitive understanding into the disease dynamics, useful for both individuals and policymakers. In particular the relationship between social distancing and infections represents a heuristic that could be communicated to the population to encourage, or "bootstrap", rational behaviour.
A Closed-Form Control for Safety Under Input Constraints Using a Composition of Control Barrier Functions
Authors: Pedram Rabiee, Jesse B. Hoagg
Date: 2024-03-30 00:00:00
Tasks: Form

We present a closed-form optimal control that satisfies both safety constraints (i.e., state constraints) and input constraints (e.g., actuator limits) using a composition of multiple control barrier functions (CBFs). This main contribution is obtained through the combination of several ideas. First, we present a method for constructing a single relaxed control barrier function (R-CBF) from multiple CBFs, which can have different relative degrees. The construction relies on a log-sum-exponential soft-minimum function and yields an R-CBF whose zero-superlevel set is a subset of the intersection of the zero-superlevel sets of all CBFs used in the composition. Next, we use the soft-minimum R-CBF to construct a closed-form control that is optimal with respect to a quadratic cost subject to the safety constraints. Finally, we use the soft-minimum R-CBF to develop a closed-form optimal control that not only guarantees safety but also respects input constraints. The key elements in developing this novel control include: the introduction of the control dynamics, which allow the input constraints to be transformed into controller-state constraints; the use of the soft-minimum R-CBF to compose multiple safety and input CBFs, which have different relative degrees; and the development of a desired surrogate control (i.e., a desired input to the control dynamics). We demonstrate these new control approaches in simulation on a nonholonomic ground robot.
Enabling Multicast Transmission for Spatio-Temporally Asynchronous User Requests in Wireless Environments
Authors: Hojung Lee, Jun-Pyo Hong, Wan Choi
Date: 2024-07-05 00:00:00

The surge in wireless devices and data traffic volume necessitates more efficient transmission methods. Multicasting has garnered consistent attention as a means to fulfill the increasing demand for more efficient data transmission methods. Nevertheless, leveraging multicast wireless networks for spatio-temporally asynchronous data requests poses challenges. In this context, this paper introduces a new multicast mechanism called \emph{set-up based merged multicast (SMMC)} to minimize the delivery time of the requested file in wireless networks by considering the uncertainties inherent in wireless channels. The proposed mechanism comprises two phases. The first phase involves gathering asynchronous requests for a file from users experiencing diverse channel conditions. During this phase, packets of the requested file are transmitted individually in unicast mode within a specified set-up time. Following this, the second phase initiates multicast transmission, which sequentially handles the remaining packets of the file in multicast mode. In the proposed mechanism, we optimize the set-up time and transmission rates of both unicast and multicast modes to minimize the expected file delivery time by jointly taking into account the statistical characteristics of wireless channels, users' locations, and file popularity. Additionally, we also delve into a \emph{fine-tuned SMMC} by utilizing posterior information on the multicast group size and further improve the performance. Our performance evaluations reveal that the proposed SMMC outperforms conventional unicast methods, especially with high-demand data.
Digital twin with automatic disturbance detection for real-time optimization of a semi-autogenous grinding (SAG) mill
Authors: Paulina Quintanilla, Francisco Fernández, Cristobal Mancilla, Matías Rojas, Mauricio Estrada, Daniel Navia
Date: 2024-07-04 00:00:00

This work describes the development and validation of a digital twin for a semi-autogenous grinding (SAG) mill controlled by an expert system. The digital twin consists of three modules emulating a closed-loop system: fuzzy logic for the expert control, a state-space model for regulatory control, and a recurrent neural network for the SAG mill process. The model was trained with 68 hours of data and validated with 8 hours of test data. It predicts the mill's behavior within a 2.5-minute horizon with a 30-second sampling time. The disturbance detection evaluates the need for retraining, and the digital twin shows promise for supervising the SAG mill with the expert control system. Future work will focus on integrating this digital twin into real-time optimization strategies with industrial validation.
Forward Reachability for Discrete-Time Nonlinear Stochastic Systems via Mixed-Monotonicity and Stochastic Order
Authors: Vignesh Sivaramakrishnan, Rosalyn A. Devonport, Murat Arcak, Meeko M. K. Oishi
Date: 2024-07-04 00:00:00

We present a method to overapproximate forward stochastic reach sets of discrete-time, stochastic nonlinear systems with interval geometry. This is made possible by extending the theory of mixed-monotone systems to incorporate stochastic orders, and a concentration inequality result that lower-bounds the probability the state resides within an interval through a monotone mapping. Then, we present an algorithm to compute the overapproximations of forward reachable set and the probability the state resides within it. We present our approach on two aerospace examples to show its efficacy.
Configuring Transmission Thresholds in IIoT Alarm Scenarios for Energy-Efficient Event Reporting
Authors: David E. Ruíz-Guirola, Onel L. A. López, Samuel Montejo-Sánchez
Date: 2024-07-04 00:00:00
Tasks: Q-Learning, Scheduling

Industrial Internet of Things (IIoT) applications involve real-time monitoring, detection, and data analysis. This is challenged by the intermittent activity of IIoT devices (IIoTDs) and their limited battery capacity. Indeed, the former issue makes resource scheduling and random access difficult, while the latter constrains IIoTDs' lifetime and efficient operation. In this paper, we address interconnected aspects of these issues. Specifically, we focus on extending the battery life of IIoTDs sensing events/alarms by minimizing the number of unnecessary transmissions. Note that when multiple devices access the channel simultaneously, there are collisions, potentially leading to retransmissions, thus reducing energy efficiency. We propose a threshold-based transmission-decision policy based on the sensing quality and the network spatial deployment. We optimize the transmission thresholds using several approaches such as successive convex approximation, block coordinate descent methods, Voronoi diagrams, explainable machine learning, and algorithms based on natural selection and social behavior. Besides, we propose a new approach that reformulates the optimization problem as a $Q$-learning solution to promote adaptability to system dynamics. Through numerical evaluation, we demonstrate significant performance enhancements in complex IIoT environments, thus validating the practicality and effectiveness of the proposed solutions. We show that Q-learning performs the best, while the block coordinate descending method incurs the worst performance. Additionally, we compare the proposed methods with a benchmark assigning the same threshold to all the devices for transmission decision. Compared to the benchmark, up to 94\% and 60\% reduction in power consumption are achieved in low-density and high-density scenarios, respectively.
Robust Affine Formation Control of Multiagent Systems
Authors: Zhonggang Li, Raj Thilak Rajan
Date: 2024-07-04 00:00:00

Affine formation control is a subset of formation control methods, which has gained increasing popularity for its flexibility and maneuverability in diverse applications. Affine formation control is inherently distributed in nature, where the local controllers onboard each agent are linearly dependent on the relative position measurements of the neighboring agents. The unavailability of these measurements in practice, due to node failure or missing links, leads to a change in the underlying graph topology, and subsequently causes instability and sub-optimal performance. In this paper, we propose an estimation framework to enhance the robustness of distributed affine formation control systems against these topology changes. Our estimation framework features an adaptive fusion of both temporal information from the dynamics of agents and spatial information which is derived from the geometry of the affine formations. We propose a suite of algorithms under this framework to tackle various practical scenarios, and numerically verify our proposed estimator on stability, convergence rate, and optimality criterion. Simulations show the performance of our proposed algorithms as compared to the state-of-the-art methods, and we summarize them with future research directions.
Specification-guided temporal logic control for stochastic systems: a multi-layered approach
Authors: Birgit C. van Huijgevoort, Ruohan Wang, Sadegh Soudjani, Sofie Haesaert
Date: 2024-07-04 00:00:00

Designing controllers to satisfy temporal requirements has proven to be challenging for dynamical systems that are affected by uncertainty. This is mainly due to the states evolving in a continuous uncountable space, the stochastic evolution of the states, and infinite-horizon temporal requirements on the system evolution, all of which makes closed-form solutions generally inaccessible. A promising approach for designing provably correct controllers on such systems is to utilize the concept of abstraction, which is based on building simplified abstract models that can be used to approximate optimal controllers with provable closeness guarantees. The available abstraction-based methods are further divided into discretization-based approaches that build a finite abstract model by discretizing the continuous space of the system, and discretization-free approaches that work directly on the continuous state space without the need for building a finite space. To reduce the conservatism in the sub-optimality of the designed controller originating from the abstraction step, this paper develops an approach that naturally has the flexibility to combine different abstraction techniques from the aforementioned classes and to combine the same abstraction technique with different parameters. First, we develop a multi-layered discretization-based approach with variable precision by combining abstraction layers with different precision parameters. Then, we exploit the advantages of both classes of abstraction-based methods by extending this multi-layered approach guided by the specification to combinations of layers with respectively discretization-based and discretization-free abstractions. We achieve an efficient implementation that is less conservative and improves the computation time and memory usage. We illustrate the benefits of the proposed multi-layered approach on several case studies.
Automated C/C++ Program Repair for High-Level Synthesis via Large Language Models
Authors: Kangwei Xu, Grace Li Zhang, Xunzhao Yin, Cheng Zhuo, Ulf Schlichtmann, Bing Li
Date: 2024-07-04 00:00:00
Tasks: C++ code, Code Generation, High-Level Synthesis, Program Repair, RAG, Retrieval-augmented Generation

In High-Level Synthesis (HLS), converting a regular C/C++ program into its HLS-compatible counterpart (HLS-C) still requires tremendous manual effort. Various program scripts have been introduced to automate this process. But the resulting codes usually contain many issues that should be manually repaired by developers. Since Large Language Models (LLMs) have the ability to automate code generation, they can also be used for automated program repair in HLS. However, due to the limited training of LLMs considering hardware and software simultaneously, hallucinations may occur during program repair using LLMs, leading to compilation failures. Besides, using LLMs for iterative repair also incurs a high cost. To address these challenges, we propose an LLM-driven program repair framework that takes regular C/C++ code as input and automatically generates its corresponding HLS-C code for synthesis while minimizing human repair effort. To mitigate the hallucinations in LLMs and enhance the prompt quality, a Retrieval-Augmented Generation (RAG) paradigm is introduced to guide the LLMs toward correct repair. In addition, we use LLMs to create a static bit width optimization program to identify the optimized bit widths for variables. Moreover, LLM-driven HLS optimization strategies are introduced to add/tune pragmas in HLS-C programs for circuit optimization. Experimental results demonstrate that the proposed LLM-driven automated framework can achieve much higher repair pass rates in 24 real-world applications compared with the traditional scripts and the direct application of LLMs for program repair.
Coupling Optimization using Design Structure Matrices and Genetic Algorithm
Authors: Sebastien Dube, Mirna Ojeda, Jean-Marie Gauthier
Date: 2024-07-04 00:00:00

This article seeks to contribute to a nuanced understanding of the integration of Design Structure Matrix (DSM) and genetic algorithms in the context of Complex Systems modelling described within Model-Based System Engineering approach. By examining coupling minimization as a critical aspect of advanced systems engineering practices, we aim to provide a scholarly exploration, blending theoretical insights with practical applications. The objective is to equip systems architects with analytical tools integrated within their Model Based Systems Engineering (MBSE) environment for exploring the design space of component interactions, facilitating the identification of optimal system architectures.
Analytical Gradient and Hessian Evaluation for System Identification using State-Parameter Transition Tensors
Authors: Premjit Saha, Tarunraj Singh
Date: 2024-07-03 00:00:00

In this work, the Einstein notation is utilized to synthesize state and parameter transition matrices, by solving a set of ordinary differential equations. Additionally, for the system identification problem, it has been demonstrated that the gradient and Hessian of a cost function can be analytically constructed using the same matrix and tensor metrics. A general gradientbased optimization problem is then posed to identify unknown system parameters and unknown initial conditions. Here, the analytical gradient and Hessian of the cost function are derived using these state and parameter transition matrices. The more robust performance of the proposed method for identifying unknown system parameters and unknown initial conditions over an existing conventional quasi-Newton method-based system identification toolbox (available in MATLAB) is demonstrated by using two widely used benchmark datasets from real dynamic systems. In the existing toolbox, gradient and Hessian information, which are derived using a finite difference method, are more susceptible to numerical errors compared to the analytical approach presented. Keywords: Gradient-based Optimization, Transition matrix and tensors, Gradient and Hessian, System identification.
Reconfigurable Intelligent Surface Identification in Mobile Networks: Opportunities and Challenges
Authors: Emre Arslan, Ali Tugberk Dogukan, Fatih Kilinc, Ahmet Faruk Coskun, Ertugrul Basar
Date: 2024-06-29 00:00:00

The advent of the sixth generation (6G) wireless networks heralds a transformative era for mobile communication, where the integration of cutting-edge technologies like Reconfigurable Intelligent Surfaces (RISs) is paramount in addressing the burgeoning demands for energy efficiency, high data rates, reliable connectivity, and enhanced coverage in densely populated areas. RISs have attracted the attention of academia as well as the industry and emerged as a beacon of innovation, offering a novel paradigm to reconfigure the wireless propagation environment beneficially, thereby enhancing the overall network performance. It is envisioned that the deployment of numerous RISs in a mobile network will serve user equipments (UEs) to boost various key performance indicators (KPIs), with lower energy consumption compared to alternative solutions (e.g., relays, and network-controlled repeaters). However, the knowledge of whether or not a UE is being served through an RIS and if so, which RIS it is being served by is crucial and beneficial for various network planning and operational reasons. In this paper, we address the importance and the benefits of RIS identification in a mobile network. Additionally, we guide the readers and researchers by introducing alternative methods to enable RIS identification. Through the lens of this research, we unveil and shed light to various benefits, the challenges, and future opportunities in the identification of RISs serving mobile users in complex network environments, highlighting the necessity for advanced identification strategies to fully harness the potential of RIS technology in next-generation wireless systems.