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2nd International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI)

作者： Vidhya, K. Thiyagu, T. M. Taurshia, Antony Nagarajan, B. Jenefa, A. Jebas, Joshua Flower, I Karunya Inst Tech & Sci Sch Cst Coimbatore Tamil Nadu India Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & Dept CSE Morai India KPR Inst Engn & Technol Dept Artificial Intelligence & Data Sci Coimbatore Tamil Nadu India

ISBN: (纸本)9798331540661;9798331540678

In today's fast-paced world, many individuals struggle to maintain a balanced diet, leading to various nutritional deficiencies. Effective management and education on these deficiencies are crucial for improving public health. Traditional methods of addressing nutritional deficiencies often lack personalization and user engagement. The NutriGuide application, developed using the Flutter framework, aims to revolutionize nutritional management by providing a user-centric, cross-platform solution. Utilizing a comprehensive database of nutrients and symptoms, the application offers personalized dietary recommendations based on user inputs. The dataset includes detailed information on common deficiencies, symptoms, and nutrient-rich foods. Experimental results indicate high user satisfaction, with an average satisfaction score of 4.5 out of 5, and significant improvements in nutritional intake, such as a 28.6% increase in Vitamin A intake and a 50% increase in Vitamin D intake. In conclusion, NutriGuide presents an innovative approach to nutritional health, combining modern technology with evidence-based recommendations to empower users in managing their dietary needs.

关键词： Nutritional Deficiencies Personalized Dietary Recommendations Flutter Framework Cross-Platform Application Nutritional Health Management User-Centric Design data-driven algorithms Mobile Health Applications Nutritional Education

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Moment Matching for Linear Systems in Discrete-Time: Towards Enhanced Performance 63

Moment Matching for Linear Systems in Discrete-Time: Towards...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Bhattacharjee, Debraj Moreschini, Alessio Astolfi, Alessandro Imperial College London Department of Electrical and Electronic Engineering LondonSW7 2AZ United Kingdom University of Rome 'Tor Vergata' Dipartimento di Ingegneria Civile e Ingegneria Informatica Rome Italy

ISBN: (纸本)9798350316339

We study the moment matching problem for linear systems in a discrete-time setting and introduce a family of reduced-order models that can replicate the steady-state response of the underlying system. We show that reduced-order models can be directly computed from input-output data without needing to compute the moment of the underlying system. We then use a benchmark example to highlight performance improvements of the discrete-time algorithm over its continuous-time counterpart. © 2024 IEEE.

关键词： data-driven algorithms Discrete-time modeling Model reduction Moment matching

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Model-based machine learning in acoustics applications from a Bayesian perspective 24

Model-based machine learning in acoustics applications from ...

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24th International Congress on Acoustics, ICA 2022

作者： Xiang, Ning Chen, Ziqi Zhang, Jiahua Nolan, Mélanie Graduate Program in Architectural Acoustics Rensselaer Polytechnic Institute TroyNY United States

Recent acoustics investigations increasingly employ machine learning methods to analyze experimental data. Using well-understood models or hypotheses, acoustics analyses often embody two levels of inference: model selection and parameter estimation based on the chosen model. Model-based Bayesian analysis provides solutions of both inference levels centered around Bayes' theorem within a unified framework. The data-driven machine learning method represents a learning process from data in order to update our knowledge. This is exactly consistent with the core of Bayesian probability theory, which represents how our prior knowledge is updated in presence of the data. This paper discusses the model-based machine learning methods in a way of thinking like a Bayesian. In particular, it emphasizes that the well-understood models are considered as a part of the prior knowledge which are readily incorporated in the learning process. This paper highlights data-driven machine learning methods from a Bayesian perspective based on recent acoustics investigations, such as acoustic array signal processing, normal incident impedance tube measurements, chamber-based absorption measurements and wavenumber spectral analysis in enclosed spaces. Numerical challenges and promising solutions for two-levels of Bayesian learning are also discussed. © ICA *** rights reserved

关键词： Bayesian inference data-driven algorithms machine learning model-based analysis

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VARIABLE DYNAMIC MODE DECOMPOSITION FOR ESTIMATING TIME EIGENVALUES IN NUCLEAR SYSTEMS

VARIABLE DYNAMIC MODE DECOMPOSITION FOR ESTIMATING TIME EIGE...

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2022 International Conference on Physics of Reactors, PHYSOR 2022

作者： Smith, Ethan Variansyah, Ilham McClarren, Ryan G. Department of Aerospace and Mechanical Engineering University of Notre Dame Notre DameIN United States

ISBN: (纸本)9780894487873

An extension to the Dynamic Mode Decomposition method for calculating time eigenvalues (also known as α eigenvalues) that removes the requirement of a constant time step is derived and demonstrated. The new method, called variable-dynamic mode decomposition (V-DMD), is shown to be accurate when computing eigenvalues for systems that were infeasible with DMD due to a large separation in time scales. The alpha eigenvalues of an infinite medium neutron transport problem with delayed neutrons and consequently having multiple, very different relevant time scales are computed. Furthermore, V-DMD is shown to be of similar accuracy to the original DMD approach when computing eigenvalues in other systems where DMD can be used. © 2022 Proceedings of the International Conference on Physics of Reactors, PHYSOR 2022. All Rights Reserved.

关键词： data-driven algorithms time eigenvalues time-dependent neutronics

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Two-level hierarchical model predictive control with an optimised cost function for energy management in building microgrids

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APPLIED ENERGY 2021年 285卷 116420-116420页

作者： Yamashita, Daniela Yassuda Vechiu, Ionel Gaubert, Jean-Paul ESTIA Inst Technol 97 Allee Theodore MonodESTIA 2 F-64210 Bidart France Univ Poitiers Lab Informat & Automat Syst LIAS 4 Rue Pierre Brousse Poitiers France

Hybrid energy storage systems have been increasingly envisaged for building microgrids to soften the drawbacks arising from the unpredictability of renewable energy resources and dwelling occupancy. The combination of longand shortterm energy storage systems can enhance the building microgrid capacity of shifting the demand peak toward periods of power generation, increasing the marks of self-consumption rate. However, the design of energy management systems for hybrid energy storage microgrids is more complex than single ones due to a greater number possible solutions. Faced with this issue, this paper proposes a two-level Hierarchical Model Predictive Controller (HMPC) enhanced by two data driven modules to improve the performance of building microgrids equipped with hybrid energy storage continuously and automatically. With minimum pre-design steps, the two data-driven algorithms improve the accuracy of Li-ion batteries and hydrogen storage models and determine adequate parameters for the HMPC cost function. Relying predominantly on data measurements, the proposed hierarchical controller determines which energy storage device must be run on a daily basis based on the estimation of the annual self-consumption rate and the annual microgrid operation costs. This real-time analysis decreases microgrid expenditure because it avoids grid penalisation regarding the energy autonomy index and reduces the degradation and maintenance of energy storage devices. Compared to a standard rule based strategy, the proposed controller reduces annual costs up to 5% in residential buildings and 9% in non-residential ones. In contrast, compared to a conventional HMPC the annual expenditure is reduced from 1% to 7% in both types of buildings.

关键词： Hierarchical model predictive control Hydrogen storage system Li-ion batteries data-driven algorithms Economic optimisation

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Analysis of Two Dimensionality Reduction Techniques for Fast Simulation of the Spectral Radiances in the Hartley-Huggins Band

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ATMOSPHERE 2019年第3期10卷 142-142页

作者： del Aguila, Ana Efremenko, Dmitry S. Molina Garcia, Victor Xu, Jian German Aerosp Ctr DLR Remote Sensing Technol Inst D-82234 Oberpfaffenhofen Germany

The new generation of atmospheric composition sensors such as TROPOMI is capable of providing spectra of high spatial and spectral resolution. To process this vast amount of spectral information, fast radiative transfer models (RTMs) are required. In this regard, we analyzed the efficiency of two acceleration techniques based on the principal component analysis (PCA) for simulating the Hartley-Huggins band spectra. In the first one, the PCA is used to map the data set of optical properties of the atmosphere to a lower-dimensional subspace, in which the correction function for an approximate but fast RTM is derived. The second technique is based on the dimensionality reduction of the data set of spectral radiances. Once the empirical orthogonal functions are found, the whole spectrum can be reconstructed by performing radiative transfer computations only for a specific subset of spectral points. We considered a clear-sky atmosphere where the optical properties are defined by Rayleigh scattering and trace gas absorption. Clouds can be integrated into the model as Lambertian reflectors. High computational performance is achieved by combining both techniques without losing accuracy. We found that for the Hartley-Huggins band, the combined use of these techniques yields an accuracy better than 0.05% while the speedup factor is about 20. This innovative combination of both PCA-based techniques can be applied in future works as an efficient approach for simulating the spectral radiances in other spectral regions.

关键词： PCA data-driven algorithms Hartley-Huggins band dimensionality reduction techniques two-stream multi-stream

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Multiclass Damage Identification in a Full-Scale Bridge Using Optimally Tuned One-Dimensional Convolutional Neural Network

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JOURNAL OF COMPUTING IN CIVIL ENGINEERING 2022年第2期36卷 04021035-04021035页

作者： Sony, Sandeep Gamage, Sunanda Sadhu, Ayan Samarabandu, Jagath Western Univ Dept Civil & Environm Engn London ON N6A 5B9 Canada Western Univ Dept Elect & Comp Engn London ON N6A 5B9 Canada

In this paper, a novel method is proposed based on a windowed one-dimensional convolutional neural network (1D CNN) for multiclass damage identification using vibration responses of a full-scale bridge. The measured data are first augmented by extracting samples of windows of raw acceleration time series to alleviate the problem of a limited training data set. 1D CNN is developed to classify the windowed time series into multiple damage classes. The damage is quantified using the predicted class probabilities, and the damage is localized if the predicted class is equal to the assigned damage class, exceeding a threshold associated with majority voting. The proposed network is optimally tuned with respect to various hyperparameters such as window size and random initialization of weights to achieve the best classification performance using a global 1D CNN model. The proposed method is validated using a benchmark bridge data for multiclass classification for two different damage scenarios, namely, pier settlement and rupture of tendons, under the various extents of damage. The damage identification is carried out on various bridge components to collectively identify the structural component with a damaged signature. The results show that the proposed windowed 1D CNN method achieves an accuracy of 97%, and performs well with different types of damage.

关键词： Damage identification Deep learning Structural health monitoring data-driven algorithms Bridges

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Just-in-time based soft sensors for process industries: A status report and recommendations

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JOURNAL OF PROCESS CONTROL 2023年第1期128卷

作者： Yeo, Wan Sieng Saptoro, Agus Kumar, Perumal Kano, Manabu Curtin Univ Malaysia Dept Chem & Energy Engn CDT 250 Miri 98009 Sarawak Malaysia Curtin Univ Malaysia Curtin Malaysia Res Inst CMRI CDT 250 Miri 98009 Sarawak Malaysia Kyoto Univ Dept Syst Sci Kyoto 6068501 Japan

Soft sensors are mathematical models employed to estimate hard-to-measure variables from available easy-to-measure variables. These sensors are typically developed using either model-driven algorithms or data-driven counterparts. data-driven algorithms are preferable since developing soft sensors via model-driven algorithms is in most cases unprofitable, technically difficult, or impossible. Soft sensors are desirable to be adaptive so that they can cope with the state and characteristics changes in industrial processes. Among the existing adaptive methods, just-in-time (JIT) models are gaining popularity due to their advantages of dealing with nonlinear, and time-varying processes with abrupt changes processes. In this paper, the existing JIT-based algorithms adopted to formulate adaptive soft sensors are critically reported and discussed. Furthermore, the limitations of these JIT-based algorithms are also highlighted. These JIT-based models are generally categorized into Gaussian and non-Gaussian distributed algorithms. A short discussion on algorithms considering nonlinear and missing data is also presented. Lastly, recommendations and future directions on JIT-based algorithms are outlined. It is envisaged that this manuscript will serve as a more comprehensive and up-to-date review and provide general guidelines for developing JIT models as soft sensors.& COPY;2023 Elsevier Ltd. All rights reserved.

关键词： Adaptive soft sensors data-driven algorithms Process industry Just-in-time models

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Combined Model-Free Adaptive Control with Fuzzy Component by Virtual Reference Feedback Tuning for Tower Crane Systems

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Procedia Computer Science 2019年 162卷 267-274页

作者： Raul-Cristian Roman Radu-Emil Precup Claudia-Adina Bojan-Dragos Alexandra-Iulia Szedlak-Stinean Politehnica University of Timişoara Dept. Automation and Applied Informatics Bd. V. Parvan 2 300223 Timişoara Romania

A novel mix of two data-driven algorithms is proposed in this paper. The mix of the algorithms aims to exploit the main advantage of data-driven Virtual Reference Feedback Tuning (VRFT) algorithm, that is represented by the automatic computation of the optimal parameters using a metaheuristic Grey Wolf Optimizer (GWO) for the Compact Form Dynamic Linearization (CFDL) version of the authors’ Model-Free Adaptive Control Takagi-Sugeno Fuzzy Algorithm (CFDL-PDTSFA), so the parameters of the CFDL-PDTSFA are optimally tuned in a model-free manner via VRFT. Three specific optimization problems are defined and solved by Model-Free Adaptive Control, VRFT and GWO algorithms. The new resulted algorithm is validated using experimental results to the arm angular position of the nonlinear tower crane system laboratory equipment.

关键词： data-driven algorithms model-free adaptive fuzzy control tower crane systems virtual reference feedback tuning

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Disaggregation of total energy use into space heating and domestic hot water: A city-scale suited approach

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ENERGY 2024年 291卷

作者： Schaffer, Markus Widen, Joakim Vera-Valdes, J. Eduardo Marszal-Pomianowska, Anna Larsen, Tine Steen Aalborg Univ Dept Built Environm DK-9220 Aalborg Denmark Uppsala Univ Dept Civil & Ind Engn Built Environm Energy Syst Grp S-75237 Uppsala Sweden Aalborg Univ Dept Math Sci DK-9220 Aalborg Denmark

This paper develops a computationally fast algorithm to disaggregate the total energy use recorded by smart heat meters into space heating (SH) and domestic hot water (DHW). The algorithm trains a regression model on SH-only hours and predicts SH for all other hours with potential DHW use. data from smart water meters were used to identify hours with only SH. Assessing 13 regression models, an untuned random forest was identified as the best -performing regression model with an acceptable computational cost (median: 7.4 s per building). Furthermore, using one year of data from over 2400 single-family homes, it was shown that the best result (median CVRMSE: 0.13) can be obtained using only smart heat meters based regressors, increasing the applicability of the developed method. Furthermore, two tests were implemented, and it was successfully demonstrated that they identify situations where the regressors are distributed differently for hours with SH only and hours with SH and DHW, to identify possible training bias;thus, buildings where the disaggregation is potentially unreliable. Validation against data from three single-family houses with known SH and DHW use confirmed the good performance of the method and that the performance can be estimated without ground truth data via nested cross -validation.

关键词： Smart heat meter Smart water meter District heating Space heating Domestic hot water data-driven algorithms

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