pwc-archive/papers-with-abstracts huggingface dataset:
A linear dissipativity approach to incremental input-to-state stability for a class of positive Lur'e systems
Authors: Violaine Piengeon, Chris Guiver
Date: 2024-02-06 00:00:00

Incremental stability properties are considered for certain systems of forced, nonlinear differential equations with a particular positivity structure. An incremental stability estimate is derived for pairs of input/state/output trajectories of the Lur'e systems under consideration, from which a number of consequences are obtained, including the incremental exponential input-to-state stability property and certain input-output stability concepts with linear gain. Incremental stability estimates provide a basis for an investigation into the response to convergent and (almost) periodic forcing terms, and is treated presently. Our results show that an incremental version of the real Aizerman conjecture is true for positive Lur'e systems when an incremental gain condition is imposed on the nonlinear term, as we describe. Our argumentation is underpinned by linear dissipativity theory -- a property of positive linear control systems.
Robust Data-EnablEd Predictive Leading Cruise Control via Reachability Analysis
Authors: Shuai Li, Chaoyi Chen, Haotian Zheng, Jiawei Wang, Qing Xu, Keqiang Li
Date: 2024-02-06 00:00:00
Tasks: Autonomous Vehicles, LEMMA

Data-driven predictive control promises model-free wave-dampening strategies for Connected and Autonomous Vehicles (CAVs) in mixed traffic flow. However, its performance relies on data quality, which suffers from unknown noise and disturbances. This paper introduces a Robust Data-EnablEd Predictive Leading Cruise Control (RDeeP-LCC) method based on reachability analysis, aiming to achieve safe and optimal CAV control under bounded process noise and external disturbances. Precisely, the matrix zonotope set technique and Willems' Fundamental Lemma are employed to derive the over-approximated system dynamics directly from data, and a data-driven feedback control technique is utilized to obtain an additional feedback input for stability. We decouple the mixed platoon into an error system and a nominal system, where the error system provides data-driven reachability sets for the enhanced safety constraints in the nominal system. Finally, a data-driven predictive control framework is formulated in a tube-based control manner for robustness guarantees. Nonlinear simulations with noise-corrupted data demonstrate that the proposed method outperforms baseline methods in mitigating traffic waves.
Aperiodic two-layer energy management system for community microgrids based on blockchain strategy
Authors: Miguel Gayo Abeleira, Carlos Santos Pérez, Francisco Javier Rodríguez Sánchez, Pedro Martín Sánchez, Enrique Santiso Gómez, José Antonio Jiménez Calvo
Date: 2024-02-06 00:00:00
Tasks: energy management, Management, Raspberry Pi 4

This work proposes a geographically-based split of the community microgrids into clusters of members that tend to have similar consumption and generation profiles. Assuming a community microgrid divided into clusters, a two-layer architecture is developed to facilitate the greater penetration of distributed energy resources in an efficient way. The first layer, referred as the market layer, is responsible for creating local energy markets with the aim of maximising the economic benefits for community microgrid members. The second layer is responsible for the network reconfiguration, which is based on the energy balance within each cluster. This layer complies with the IEC 61850 communication standard, in order to control commercial sectionalizing and tie switches. This allows the community microgrid network to be reconfigured to minimise energy exchanges with the main grid. To implement this two-layer energy management strategy, an aperiodic market approach based on Blockchain technology, and the additional functionality offered by Smart Contracts is adopted. This embraces the concept of energy communities since it decentralizes the control and eliminates intermediaries. The use of aperiodic control techniques helps to overcome the challenges of using Blockchain technology in terms of storage, computational requirements and member privacy. The scalability and modularity of the Smart Contract-based system allow each cluster of members to be designed by tailoring the system to their specific needs. The implementation of this strategy is based on low-cost off-the-shelf devices, such as Raspberry Pi 4 Model B boards, which operate as Blockchain nodes of community microgrid members. Finally, the strategy has been validated by emulating two use cases based on the IEEE 123-node system network model highlighting the benefits of the proposal.
Design and implementation of multiprotocol framework for residential prosumer incorporation in flexibility markets
Authors: Miguel Gayo, Francisco Javier Rodríguez, Carlos Santos, Ying Wu, Yanpeng Wu, Juan C. Vasquez, Josep M. Guerrero
Date: 2024-02-06 00:00:00

The growth of distributed renewable energy in the electrical grid presents challenges to its stability and quality. To address this at the local level, flexibility energy strategies emerge as an innovative technique. However, managing these strategies in residential areas becomes complex due to the unique characteristics of each prosumer. A major challenge lies in managing communication among diverse devices with different protocols. To address these issues, a comprehensive framework is designed and implemented to facilitate prosumers' integration in flexibility strategies, addressing communication at various levels. The effectiveness of the proposed framework is demonstrated through its implementation in a real smart home environment with diverse devices. The framework enables seamless integration and communication between IoT devices and IEC 61,850-compliant power devices. This research presents a novel approach to address the challenges of managing flexibility strategies in residential areas, providing a practical solution for prosumers to actively participate in optimizing energy consumption and enhancing the stability and quality of the electricity system amidst the growing integration of distributed renewable energy.
PMSM transient response optimization by end-to-end optimal control
Authors: Yuta Kawachi, Mitsuru Ambai, Yuichi Yoshida, Gaku Takano
Date: 2024-02-06 00:00:00

Speed responses of motors, especially Permanent Magnet Synchronous Motors (PMSMs), are increasing in importance for recent applications, such as electric vehicles or quadrotors. These applications require quick acceleration performance. However, commercial controllers are based mainly on Proportional-Integral (PI) controllers, which are suitable for eliminating steady-state errors but unsuitable for transient response optimization. In this paper, we replaced whole conventional controllers with an end-to-end Recurrent Neural Network (RNN) that has a regularized transition matrix. Our end-to-end controller directly minimizes the transient response time on the basis of optimal control theory. Computer-simulated results show that speed response indices improved using the RNN rather than a PI controller, while both were under comparable power losses. The current vector trajectories of the RNN showed that the RNN could automatically determine arbitrary trajectories in the flux-weakening region in accordance with an arbitrarily designed loss function. In contrast, the traditional flux-weakening methods using PI controllers have pre-determined current vector trajectories.
Improvement of Frequency Source Phase Noise Reduction Design under Vibration Condition
Authors: Liwei Yin, Yongjiang Shu, Heng Zhang, Yuefei Dai, Xiaopeng Lu, Yunlong Lian, Zhonghua Wang, Yong Ding
Date: 2024-02-06 00:00:00

Reasonable vibration reduction design is an important way to achieve low phase noise index of airborne frequency source output signal. Aiming at the problem of phase noise deterioration of an airborne frequency source under random condition, this paper proposes to improve the vibration reduction mode crystal oscillator and reduce the distance between the barycenter of frequency source and crystal oscillator vibration based on the analysis of the relationship between the frequency source and the phase noise of output signal. Experimental results show that the active noise control system achieves 62dB phase noise compensation under the random vibration of 0.04-0.1g*g/Hz amplitude range and 5-2000 Hz frequency range.
PSO-Based Adaptive NMPC for Uranium Extraction-Scrubbing Operation in Spent Nuclear Fuel Treatment Process
Authors: Duc-Tri Vo, Ionela Prodan, Laurent Lefèvre, Vincent Vanel, Sylvain Costenoble, Binh Dinh
Date: 2024-02-06 00:00:00
Tasks: Model Predictive Control

This paper addresses the particularities of adaptive optimal control of the uranium extraction-scrubbing operation in the PUREX process. The process dynamics are nonlinear, high dimensional, and have limited online measurements. In addition, analysis and developments are based on a qualified simulation program called PAREX, which was validated with laboratory and industrial data. The control objective is to stabilize the process at a desired solvent saturation level, guaranteeing constraints and handling disturbances. The developed control strategy relies on optimization-based methods for computing control inputs and estimates, i.e., Nonlinear Model Predictive Control (NMPC) and Nonlinear Moving Horizon Estimation (NMHE). The designs of these two associated algorithms are tailored for this process's particular dynamics and are implemented through an enhanced Particle Swarm Optimization (PSO) to guarantee constraint satisfaction. Software-in-the-loop simulations using PAREX show that the designed control scheme effectively satisfies control objectives and guarantees constraints during operation.
Autopilot System for Depth and Pitch Control in Underwater Vehicles: Navigating Near-Surface Waves and Disturbances
Authors: Vladimir Petrov, Gage MacLin, Venanzio Cichella
Date: 2024-02-05 00:00:00
Tasks: Pitch control

This paper introduces a framework for depth and pitch control of underwater vehicles in near-surface wave conditions. By effectively managing tail, sail plane angles and hover tank operations utilizing a Linear Quadratic Regulator controller and L1 Adaptive Autopilot augmentation, the system ensures balanced control input distribution and significantly attenuates wave disturbances. This development in underwater vehicle control systems offers potential for improved functionality across a range of marine applications. The proposed framework is demonstrated to be robust in a variety of wave conditions, enabling more precise navigation and improved safety in operational scenarios. The effectiveness of this control strategy is validated through extensive simulations using the Joubert BB2 model.
Ultrathin, flexible and MRI-compatible microelectrode array for chronic single units recording within subcortical layers
Authors: Scott Greenhorn, Véronique Coizet, Victor Dupuit, Bruno Fernandez, Guillaume Bres, Arnaud Claudel, Pierre Gasner, Jan M. Warnking, Emmanuel L. Barbier, Cécile Delacour
Date: 2024-02-06 00:00:00

Current techniques of neuroimaging, including electrical devices, are either of low spatiotemporal resolution or invasive, impeding multiscale monitoring of brain activity at both single cell and network levels. Overcoming this issue is of great importance to assess brain's computational ability and for neurorehabilitation projects that require real-time monitoring of neurons and concomitant networks activities. Currently, that information could be extracted from functional MRI when combined with mathematical models. Novel methods enabling quantitative and long-lasting recording at both single cell and network levels will allow to correlate the MRI data with intracortical activity at single cell level, and to refine those models. Here, we report the fabrication and validation of ultra-thin, optically transparent and flexible intracortical microelectrode arrays for combining extracellular multi-unit and fMRI recordings. The sensing devices are compatible with large-scale manufacturing, and demonstrate both fMRI transparency at 4.7 T, and high electrical performance, and thus appears as a promising candidate for simultaneous multiscale neurodynamic measurements.
ProtAgents: Protein discovery via large language model multi-agent collaborations combining physics and machine learning
Authors: A. Ghafarollahi, M. J. Buehler
Date: 2024-01-27 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, Protein Design

Designing de novo proteins beyond those found in nature holds significant promise for advancements in both scientific and engineering applications. Current methodologies for protein design often rely on AI-based models, such as surrogate models that address end-to-end problems by linking protein structure to material properties or vice versa. However, these models frequently focus on specific material objectives or structural properties, limiting their flexibility when incorporating out-of-domain knowledge into the design process or comprehensive data analysis is required. In this study, we introduce ProtAgents, a platform for de novo protein design based on Large Language Models (LLMs), where multiple AI agents with distinct capabilities collaboratively address complex tasks within a dynamic environment. The versatility in agent development allows for expertise in diverse domains, including knowledge retrieval, protein structure analysis, physics-based simulations, and results analysis. The dynamic collaboration between agents, empowered by LLMs, provides a versatile approach to tackling protein design and analysis problems, as demonstrated through diverse examples in this study. The problems of interest encompass designing new proteins, analyzing protein structures and obtaining new first-principles data -- natural vibrational frequencies -- via physics simulations. The concerted effort of the system allows for powerful automated and synergistic design of de novo proteins with targeted mechanical properties. The flexibility in designing the agents, on one hand, and their capacity in autonomous collaboration through the dynamic LLM-based multi-agent environment on the other hand, unleashes great potentials of LLMs in addressing multi-objective materials problems and opens up new avenues for autonomous materials discovery and design.
Reliable ligand discrimination in stochastic multistep kinetic proofreading: First passage time vs. product counting strategies
Authors: Xiangting Li, Tom Chou
Date: 2024-02-07 00:00:00
Tasks: Specificity

Cellular signaling, crucial for biological processes like immune response and homeostasis, relies on specificity and fidelity in signal transduction to accurately respond to stimuli amidst biological noise. Kinetic proofreading (KPR) is a key mechanism enhancing signaling specificity through time-delayed steps, although its effectiveness is debated due to intrinsic noise potentially reducing signal fidelity. In this study, we reformulate the theory of kinetic proofreading (KPR) by convolving multiple intermediate states into a single state and then define an overall "processing" time required to traverse these states. This simplification allows us to succinctly describe kinetic proofreading in terms of a single waiting time parameter, facilitating a more direct evaluation and comparison of KPR performance across different biological contexts such as DNA replication and T cell receptor (TCR) signaling. We find that loss of fidelity for longer proofreading steps relies on the specific strategy of information extraction and show that in the first-passage time (FPT) discrimination strategy, longer proofreading steps can exponentially improve the accuracy of KPR at the cost of speed. Thus, KPR can still be an effective discrimination mechanism in the high noise regime. However, in a product concentration-based discrimination strategy, longer proofreading steps do not necessarily lead to an increase in performance. However, by introducing activation thresholds on product concentrations, can we decompose the product-based strategy into a series of FPT based strategies to better resolve the subtleties of KPR-mediated product discrimination. Our findings underscore the importance of understanding KPR in the context of how information is extracted and processed in the cell.
When Geoscience Meets Generative AI and Large Language Models: Foundations, Trends, and Future Challenges
Authors: Abdenour Hadid, Tanujit Chakraborty, Daniel Busby
Date: 2024-01-25 00:00:00
Tasks: Decision Making, Super-Resolution, Uncertainty Quantification

Generative Artificial Intelligence (GAI) represents an emerging field that promises the creation of synthetic data and outputs in different modalities. GAI has recently shown impressive results across a large spectrum of applications ranging from biology, medicine, education, legislation, computer science, and finance. As one strives for enhanced safety, efficiency, and sustainability, generative AI indeed emerges as a key differentiator and promises a paradigm shift in the field. This paper explores the potential applications of generative AI and large language models in geoscience. The recent developments in the field of machine learning and deep learning have enabled the generative model's utility for tackling diverse prediction problems, simulation, and multi-criteria decision-making challenges related to geoscience and Earth system dynamics. This survey discusses several GAI models that have been used in geoscience comprising generative adversarial networks (GANs), physics-informed neural networks (PINNs), and generative pre-trained transformer (GPT)-based structures. These tools have helped the geoscience community in several applications, including (but not limited to) data generation/augmentation, super-resolution, panchromatic sharpening, haze removal, restoration, and land surface changing. Some challenges still remain such as ensuring physical interpretation, nefarious use cases, and trustworthiness. Beyond that, GAI models show promises to the geoscience community, especially with the support to climate change, urban science, atmospheric science, marine science, and planetary science through their extraordinary ability to data-driven modeling and uncertainty quantification.
Translating the future: Image-to-image translation for the prediction of future brain metabolism
Authors: Elena Doering, Merle C. Hönig, Tobias Deußer, Gerard N. Bischof, Thilo van Eimeren, Alexander Drzezga, Lotta M. Ellingsen
Date: 2024-02-06 00:00:00
Tasks: Image-to-Image Translation

Alzheimer's disease (AD) is a progressive neurodegenerative disorder leading to cognitive decline. [$^{18}$F]-Fluorodeoxyglucose positron emission tomography ([$^{18}$F]-FDG PET) is used to monitor brain metabolism, aiding in the diagnosis and assessment of AD over time. However, the feasibility of multi-time point [$^{18}$F]-FDG PET scans for diagnosis is limited due to radiation exposure, cost, and patient burden. To address this, we have developed a predictive image-to-image translation (I2I) model to forecast future [$^{18}$F]-FDG PET scans using baseline and year-one data. The proposed model employs a convolutional neural network architecture with long-short term memory and was trained on [$^{18}$F]-FDG PET data from 161 individuals from the Alzheimer's Disease Neuroimaging Initiative. Our I2I network showed high accuracy in predicting year-two [18F]-FDG PET scans, with a mean absolute error of 0.031 and a structural similarity index of 0.961. Furthermore, the model successfully predicted PET scans up to seven years post-baseline. Notably, the predicted [$^{18}$F]-FDG PET signal in an AD-susceptible meta-region was highly accurate for individuals with mild cognitive impairment across years. In contrast, a linear model was sufficient for predicting brain metabolism in cognitively normal and dementia subjects. In conclusion, both the I2I network and the linear model could offer valuable prognostic insights, guiding early intervention strategies to preemptively address anticipated declines in brain metabolism and potentially to monitor treatment effects.
Application analysis of ai technology combined with spiral CT scanning in early lung cancer screening
Authors: Shulin Li, Liqiang Yu, Bo Liu, Qunwei Lin, Jiaxin Huang
Date: 2024-01-26 00:00:00
Tasks: Organ Segmentation, Prognosis

At present, the incidence and fatality rate of lung cancer in China rank first among all malignant tumors. Despite the continuous development and improvement of China's medical level, the overall 5-year survival rate of lung cancer patients is still lower than 20% and is staged. A number of studies have confirmed that early diagnosis and treatment of early stage lung cancer is of great significance to improve the prognosis of patients. In recent years, artificial intelligence technology has gradually begun to be applied in oncology. ai is used in cancer screening, clinical diagnosis, radiation therapy (image acquisition, at-risk organ segmentation, image calibration and delivery) and other aspects of rapid development. However, whether medical ai can be socialized depends on the public's attitude and acceptance to a certain extent. However, at present, there are few studies on the diagnosis of early lung cancer by AI technology combined with SCT scanning. In view of this, this study applied the combined method in early lung cancer screening, aiming to find a safe and efficient screening mode and provide a reference for clinical diagnosis and treatment.
Inference for Two-Stage Extremum Estimators
Authors: Aristide Houndetoungan, Abdoul Haki Maoude
Date: 2024-02-07 00:00:00

We present a simulation-based inference approach for two-stage estimators, focusing on extremum estimators in the second stage. We accommodate a broad range of first-stage estimators, including extremum estimators, high-dimensional estimators, and other types of estimators such as Bayesian estimators. The key contribution of our approach lies in its ability to estimate the asymptotic distribution of two-stage estimators, even when the distributions of both the first- and second-stage estimators are non-normal and when the second-stage estimator's bias, scaled by the square root of the sample size, does not vanish asymptotically. This enables reliable inference in situations where standard methods fail. Additionally, we propose a debiased estimator, based on the mean of the estimated distribution function, which exhibits improved finite sample properties. Unlike resampling methods, our approach avoids the need for multiple calculations of the two-stage estimator. We illustrate the effectiveness of our method in an empirical application on peer effects in adolescent fast-food consumption, where we address the issue of biased instrumental variable estimates resulting from many weak instruments.