Browsing by Author "Nunez, Felipe"

Now showing 1 - 11 of 11
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    A Cyber-Physical System for Real-Time Physiological Data Monitoring and Analysis
    (2024) Huanca, Fernando; Torres, Mario; Rodriguez-Fernandez, Maria; Nunez, Felipe
    In the pursuit of a personalized healthcare experience, data-driven decision-making has become increasingly relevant. In this context, there is a growing need for technological systems specifically tailored to efficiently manage vast amounts of healthcare data. To address this need, in this work, we contribute by presenting a cyber-physical solution for monitoring and analyzing physiological data obtained from wearable devices. The proposed system is designed following a service-oriented architecture, which promotes modularity and enables efficient data access and analysis. The system is capable of ingesting and consolidating wearable data produced by a variety of commercial devices, scaling to accommodate a large number of data producers, and accepting queries from a multitude of consumers through various mechanisms. Performance tests conducted in various scenarios using real data demonstrate the system's effectiveness in maintaining real-time data access.
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    A meta-learning approach to personalized blood glucose prediction in type 1 diabetes
    (2023) Langarica, Saul; Rodriguez-Fernandez, Maria; Nunez, Felipe; Doyle III, Francis J.
    Accurate blood glucose prediction is a critical element in modern artificial pancreas systems. Recently, many deep learning-based models have been proposed for glucose prediction, showing encouraging results in population modeling. However, due to the large amount of data required for training deep learning -based models, few studies have successfully addressed personalized modeling, which is critical to ensure safe policies in a closed-loop scheme given the high inter-patient variability. To address this concern, we propose a meta-learning-based technique for accurate personalized modeling that requires minimal data volume to personalize from its population version, needs few training iterations, and has a low risk of over-fitting. Results using the UVA/Padova simulator show that the proposed technique generalizes better and outperforms other approaches in standard and task-specific metrics, particularly for longer prediction horizons and higher degrees of distributional shifts.
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    Automatic Synthesis of Containerized Industrial Cyber-Physical Systems: A Case Study
    (2023) Biskupovic, Angel; Torres, Mario; Nunez, Felipe
    Industrial cyber-physical systems (ICPSs) are widely regarded as the next generation industrial control systems and as one of the core technologies of the ongoing fourth industrial revolution. Despite its advantages, ICPSs are heavily dependent on the underlying physical process and their synthesis is a customized effort, demanding in terms of resources, which if not conducted carefully may impact the performance of the system. This work proposes a methodology to tackle ICPS synthesis in a systematic way, by using a set of industrial agents that take as input and standardized process description file and automatically deploy a modular ICPS from predesigned functional containers. Concrete examples on a tanks system and an industrial paste thickener are presented to illustrate the potential of the proposed methodology.
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    Deep Learning-Based Glucose Prediction Models: A Guide for Practitioners and a Curated Dataset for Improved Diabetes Management
    (2024) Langarica, Saul; De La Vega, Diego; Cariman, Nawel; Miranda, Martin; Andrade, David C.; Nunez, Felipe; Rodriguez-Fernandez, Maria
    Accurate short- and mid-term blood glucose predictions are crucial for patients with diabetes struggling to maintain healthy glucose levels, as well as for individuals at risk of developing the disease. Consequently, numerous efforts from the scientific community have focused on developing predictive models for glucose levels. This study harnesses physiological data collected from wearable sensors to construct a series of data-driven models based on deep learning approaches. We systematically compare these models to offer insights for practitioners and researchers venturing into glucose prediction using deep learning techniques. Key questions addressed in this work encompass the comparison of various deep learning architectures for this task, determining the optimal set of input variables for accurate glucose prediction, comparing population-wide, fine-tuned, and personalized models, and assessing the impact of an individual's data volume on model performance. Additionally, as part of our outcomes, we introduce a meticulously curated dataset inclusive of data from both healthy individuals and those with diabetes, recorded in free-living conditions. This dataset aims to foster research in this domain and facilitate equitable comparisons among researchers.
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    Early-Warning System for Supervision of Urban Water Services: Case Study of Coquimbo, Chile
    (2023) Aguirre, Paula; Bravo, Marilyn; Torres, Mario; Langarica, Saul; Oyarzun, Muriel; Nunez, Felipe
    The combination of rapid urbanization, population growth, and the hydric stress due to climate change effects demand innovative, optimized approaches to the operation and supervision of urban water services. In Chile, the Superintendency of Sanitary Services has underscored the need for automatized data integration and analysis tools that foster an evidence-based, preventive approach to the supervision of urban water systems. This motivates the development of a pilot supervision and early-warning system, conceived as a cybernetic entity whose objective is to enable efficient access, analysis, and predictive modeling of the data provided by water service companies, so as to identify risks and inefficiencies in water services, monitor their evolution, and anticipate possible failures. This article discusses the development and implementation of a prototype system that provides tools for visualization, statistical and temporal analysis of georeferenced data on water pressures, network disruptions, and client complaints and deploys machine learning capabilities for predicting the quality of service indicators at different locations. The initial implementation in one region of Chile has been shown to expedite the exploitation of data on urban water services, reduce time lags in the detection of service disruptions, and generate evidence for the planning and execution of supervision activities. Based on this successful pilot, a roadmap for geographical and technological expansion is formulated, including other technological, organizational, and regulatory gaps that must be addressed to establish a data-driven framework for the supervision of urban water services.
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    Fault tolerant measurement system based on Takagi-Sugeno fuzzy models for a gas turbine in a combined cycle power plant
    (ELSEVIER, 2011) Berrios, Rodrigo; Nunez, Felipe; Cipriano, Aldo
    A fault tolerant measurement system for a gas turbine in a combined cycle power plant, based on dynamic models, principal component analysis (PCA) and Q test, is presented. The proposed scheme makes use of a model-based symptom generator, which delivers fault signals obtained by using direct identification of parity relations and structured residuals. Symptoms are then analyzed in a statistical module achieving fault diagnosis and reconstruction of the faulty signals. The scheme presents as main advantage the ability of detecting faults in both input and output sensors due to its particular structure. Tests carried out on the gas turbine of the San Isidro combined cycle power plant in the V Region, Chile, show that Takagi-Sugeno fuzzy models present the best fitting performance and an acceptable computational cost in comparison with autoregressive exogenous. state space, and neural models. Real time software based on this scheme has been developed and connected to Osisoft PI System'. The software is running at Endesa Monitoring and Diagnosis Center in Santiago. Chile. (C) 2011 Elsevier B.V. All rights reserved.
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    High-Gain Adaptive Control With Switching Derivation Order and Its Application to a Class of Multiagent Systems
    (2024) Gallegos, Javier; Aguila-Camacho, Norelys; Nunez, Felipe
    This article presents the design and analysis of a switching high-gain adaptive control scheme for a class of nonlinear systems. Adaptation is included in the scheme to estimate the controller gains, using differential equations whose order can switch between 1 (integer order) and some real number (fractional order) in the interval (0,1) , depending on the error level. This switching strategy permits obtaining lower values for controller gains due to fractional orders, resulting in improved robustness, while simultaneously guaranteeing fast convergence of the state to the origin due to the integer order, leading to a better balance between system behavior and control energy efficiency. Applications to multiagent systems are presented to illustrate the potential of the proposed scheme.
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    On the Relation Between Peak Age and Stability in Control Loops Over Non-Beacon Enabled CSMA/CA
    (2021) Mena, Juan P.; Nunez, Felipe
    The proliferation of the Internet of Things has enabled implementing control loops using low-capable devices over non-deterministic networks. This letter addresses the use of non-beacon enabled CSMA/CA and motivates the need of minimizing the probability of peak age surpassing a bound, given in terms of two indicators of control over networks: the maximum allowable transmit interval and the maximum allowable delay. A concrete analytical result for calculating the probability is given, which represents an improvement of 50% with respect to a known bound at the optimal rate in a 5-loops control system.
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    Predictive Control for Current Distortion Mitigation in Mining Power Grids
    (2023) Gomez, Juan S. S.; Navas-Fonseca, Alex; Flores-Bahamonde, Freddy; Tarisciotti, Luca; Garcia, Cristian; Nunez, Felipe; Rodriguez, Jose; Cipriano, Aldo Z. Z.
    Current distortion is a critical issue of power quality because the low frequency harmonics injected by adjustable speed drives increase heating losses in transmission lines and induce torque flickering in induction motors, which are widely used in mining facilities. Although classical active filtering techniques mitigate the oscillatory components of imaginary power, they may not be sufficient to clean the sensitive nodes of undesirable power components, some of which are related to real power. However, the usage of power electronic converters for distributed generation and energy storage, allows the integration of complementary power quality control objectives in electrical systems, by using the same facilities required for active power transferring. This paper proposes a predictive control-based scheme for mitigating the current distortion in the coupling node between utility grid and the mining facility power system. Instead of the classical approach of active filtering, this task is included as a secondary level objective control referred into the microgrid control hierarchy. Hardware-in-the-Loop simulation results showed that the proposed scheme is capable of bounding the current distortion, according to IEEE standard 1547, for both individual harmonics and the total rated current distortion, through inequality constraints of the optimization problem.
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    Robust Adaptive Average Consensus Over a Time-Varying and Nonbalanced Environment
    (2024) Gallegos, Javier A.; Schlotterbeck, Constanza; Nunez, Felipe
    Average consensus is a fundamental problem in distributed control that still lacks a general solution when the agents face nonideal conditions and uncertain environments. This note contributes to the topic by addressing average consensus in networks of agents that: 1) interact over a time-varying and nonbalanced environment and 2) face parametric and nonparametric disturbances. It is shown that an adaptive strategy, which combines surplus variables and virtual agents, effectively solves the exact average consensus problem in this nonideal and uncertain setup. A numerical example is presented to illustrate the potential of the proposed approach.
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    Robust Gossiping for Distributed Average Consensus in IoT Environments
    (2019) Orostica, Boris; Nunez, Felipe