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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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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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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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Machine learning empowered computer networks

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COMPUTER NETWORKS 2023年第1期230卷

作者： Cerquitelli, Tania Meo, Michela Curado, Marilia Skorin-Kapov, Lea Tsiropoulou, Eirini Eleni Politecn Torino Dept Control & Comp Engn C Duca Abruzzi 24 I-10129 Turin Italy Politecn Torino Dept Elect & Telecommun C Duca Abruzzi 24 I-10129 Turin Italy Univ Coimbra Ctr Informat & Syst Dept Informat Engn P-3030688 Coimbra Portugal Univ Zagreb Fac Elect Engn & Comp Unska 3 Zagreb 10000 Croatia Univ New Mexico Dept Elect & Comp Engn 1 Univ New Mexico MSC01 1100 Albuquerque NM 87131 USA

This special issue explores how emerging machine learning (ML) and artificial intelligence (AI) algorithms can help computer networks become smarter. The final goal is to disseminate cutting-edge research findings and computer network advances on innovative data-driven methodologies and technologies to grow innovation in ML-empowered communication networks. This particular issue aims to present advances on cross-cutting edge machine learning solutions tailored to the computer communication networking area, focusing on algorithmic aspects. The objective is to present which ML methodologies are the most effective and promising ones in the networking context so that they can inspire other researchers and practitioners in the research area of computer networks.

关键词： data-driven algorithms Computer communication networking Research findings Resource-constrained devices Multimodal deep learning Explainable AI Quality of service Cyber-physical systems

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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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A lithium-ion battery state of charge estimation method based on the fusion of data-driven and Kalman filter-based method

Future Batteries

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Future Batteries 2025年 5卷

作者： Chuanxin Fan Kailong Liu Chunfei Gu Jingyang Fang Naxin Cui Depeng Kong Qiao Peng School of Automation Nanjing Institute of Technology Nanjing 211167 China School of Control Science and Engineering Shandong University Jinan 250100 China School of Electrical Power Engineering Nanjing Institute of Technology Nanjing 211167 China State Key Laboratory of Chemical Safety College of Mechanical and Electronic Engineering China University of Petroleum (East China) Qingdao 266580 China Information Technology Analytics & Operations group Queen’s University Belfast Belfast BT9 5EE UK

State of charge (SOC) estimation is crucial for battery management systems (BMS), which relies on accurate battery mathematical models. The conventional equivalent circuit model, however, does not describe the actual electrochemical nonlinear dynamic response of a lithium-ion battery. In this work, a novel a nonlinear equivalent circuit model (NLECM) is established. It is based on an odd random phase multisine signal for parameter estimation. The signal allows parametrization over a bandwidth broader than that of a conventional HPPC signal. Based on the established NLECM model, a window-varying adaptive extended Kalman filter (WVAEKF) with data-driven algorithm is first applied for SOC estimation. The designed WVAEKF can identify variations in the error innovation sequence distribution and modify the window’s length. Learning from a large number of battery operating data, the data-driven algorithm extracts useful features for the estimation of SOC error and improves the accuracy of the SOC estimation. The experimental results show that the error of SOC estimation by WVAEKF with data-driven algorithm is limited to 1% under dynamic stress test (DST) conditions and 0.5C constant current discharge. Compared with artificial neural network and traditional AEKF, the RMSE of the proposed algorithm is reduced by 93 % and 96 % respectively, which shows that the algorithm has higher accuracy under DST conditions .

关键词： Lithium-ion battery State of charge Window-varying adaptive extended Kalman filter data-driven algorithms

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Emerging evolutionary algorithm integrated with kernel principal component analysis for modeling the performance of a water treatment plant

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JOURNAL OF WATER PROCESS ENGINEERING 2020年 33卷

作者： Abba, S., I Quoc Bao Pham Usman, A. G. Nguyen Thi Thuy Linh Aliyu, D. S. Quyen Nguyen Quang-Vu Bach Yusuf Maitama Sule Univ Kano Dept Phys Planning Dev Kano Nigeria Natl Cheng Kung Univ Dept Hydraul & Ocean Engn Tainan 701 Taiwan Near East Univ Fac Pharm Dept Analyt Chem Near East BlvdVia Mersin 10 TR-99138 Nicosia North Cyprus Turkey Thuyloi Univ 175 Tay Son Hanoi Vietnam Newcastle Univ Dept Civil Engn Newcastle Upon Tyne Tyne & Wear England Duy Tan Univ Inst Res & Dev Danang 550000 Vietnam

Providing a robust and reliable model is essential for hydro-environmental and public health engineering perspectives, including water treatment plants (WTPs). The current research develops an emerging evolutionary data-intelligence model: extreme learning machine (ELM) integrated with kernel principal component analysis (KPCA) to predict the performance of the Tamburawa WTP in Kano, Nigeria. A traditional feed-forward neural network (FFNN) and a classical linear autoregressive (AR) models were also employed to compare the predictive performance. For this purpose, different input data with the corresponding treated pH, turbidity, total dissolve solids, and hardness as the target variables obtained from the WTP were used. The predictive models are evaluated based on the three numerical indices, namely Nash-Sutcliffe (NC), root mean squared error (RMSE) and mean absolute percentage error (MAPE). To examine the similarities and differences between the observed and predicted values, a two-dimension graphical diagram (i.e., Taylor diagram) was also utilized. The predictive results revealed the potential of KPCA-ELM, which exhibited a high level of accuracy in comparison to the single models for all the considered variables with a slight exception in terms of pH prediction. Two different model combination were built for each single (FFNN, ELM, and AR) model and KPCA algorithms (KPCA-FFNN, KPCA-ELM, and KPCA-AR). The results also depicted that both ELM and FFNN models demonstrated prediction skill and therefore, can serve as reliable models. The outcomes may contribute to the aforementioned modeling of the treated parameters and provides a reference benchmark for wastewater management and control in the Tamburawa WTP.

关键词： Principal component analysis Water treatment plant data-driven algorithms Cross-validation Kano-Tamburawa Extreme learning marchine

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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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Damage Identification in Structural Health Monitoring: A Brief Review from its Implementation to the Use of data-driven Applications

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SENSORS 2020年第3期20卷 733-733页

作者： Tibaduiza Burgos, Diego A. Gomez Vargas, Ricardo C. Pedraza, Cesar Agis, David Pozo, Francesc Univ Nacl Colombia Dept Ingn Elect & Elect Cra 45 26-85 Bogota 111321 Colombia Politecn Grancolombiano Inst Univ Escuela Tecnol Informac & Comunicac Bogota 111321 Colombia Univ Nacl Colombia Dept Ingn Sistemas & Ind Cra 45 26-85 Bogota 111321 Colombia UPC EEBE Dept Matemat Control Modeling Identificat & Applicat CoDAlab CDBEduard Maristany 16 Barcelona 08019 Spain

The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors. data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making;however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification.

关键词： data-driven algorithms damage identification structural health monitoring sensors

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Multivariate learning framework for long-term adaptation in the artificial pancreas

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BIOENGINEERING & TRANSLATIONAL MEDICINE 2019年第1期4卷 61-74页

作者： Shi, Dawei Dassau, Eyal Doyle, Francis J., III Harvard Univ Harvard John A Paulson Sch Engn & Appl Sci Cambridge MA 02138 USA

The long-term use of the artificial pancreas (AP) requires an automated insulin delivery algorithm that can learn and adapt with the growth, development, and lifestyle changes of patients. In this work, we introduce a data-driven AP adaptation method for improved glucose management in a home environment. A two-phase Bayesian optimization assisted parameter learning algorithm is proposed to adapt basal and carbohydrate-ratio profile, and key feedback control parameters. The method is evaluated on the basis of the 111-adult cohort of the FDA-accepted UVA/Padova type 1 diabetes mellitus simulator through three scenarios with lifestyle disturbances and incorrect initial parameters. For all the scenarios, the proposed method is able to robustly adapt AP parameters for improved glycemic regulation performance in terms of percent time in the euglycemic range [70, 180] mg/dl without causing risk of hypoglycemia in terms of percent time below 70 mg/dl.

关键词： artificial pancreas data-driven algorithms parameter adaptation safety-critical control

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