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Towards the use of data-driven methods for indoor airflow field reconstruction: A systematic review

JOURNAL OF BUILDING ENGINEERING 2025年 106卷

作者： Olivas, Ana Claudinne Yee, Jurng-Jae Dong A Univ Dept ICT Integrated Safe Ocean Smart Cities Engn Busan South Korea

Indoor airflow field reconstruction is vital for ensuring a safe and healthy environment for indoor occupants as people spend more than 90 % of their time indoors. This requires understanding indoor airflow dynamics, which is crucial for efficient and effective heating, ventilation, and air conditioning. This review highlights the emergence, applications, advantages, and limitations of data-driven methods within the context of indoor airflow field reconstruction. Findings show that traditional methods such as computational fluid dynamics, zonal, and multizone modelling remain trusted for indoor airflow field reconstruction. However, in 2019, the pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) led to the urgent exploration of data-driven methods (DDMs) to address the concerns of high computational cost and time. This gave rise to new challenges, such as the need for large training datasets and addressing the blackbox nature of data-driven methods, which can affect user trust and the adaptability of these methods to other domains. Furthermore, while adoption of DDMs grows, there is still a lack of standardized evaluation frameworks, given their largely empirical nature. Future research can investigate DDMs towards enhancing the robustness, scalability, and interpretability of datadriven methods for indoor airflow field reconstruction.

关键词： data-driven methods Indoor airflow Review

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A Comprehensive Review on data-driven methods of Lithium-Ion Batteries State-of-Health Forecasting

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WILEY INTERDISCIPLINARY REVIEWS-data MINING AND KNOWLEDGE DISCOVERY 2025年第2期15卷

作者： Pham, Thien Bui, Hung Nguyen, Mao Pham, Quang Vu, Vinh Le, Triet Quan, Tho Ho Chi Minh City Univ Technol HCMUT Fac Comp Sci & Engn Ho Chi Minh City Vietnam Vietnam Natl Univ Ho Chi Minh City Ho Chi Minh City Vietnam

Lithium-ion batteries are widely used in moving devices due to their many advantages compared to other battery types. The prevalence of Lithium-ion batteries is evident, playing its clear role in the operation of small devices as well as large systems such as electric vehicles, flying devices, mobile devices, and more. Monitoring lithium-ion battery health is crucial for assessing, minimizing degradation, preventing explosions, and enabling timely replacements. Assessing health often involves predicting state-of-health (SoH) or remaining useful life (RUL), with numerous studies dedicated to this field. Hence, many research studies have been conducted on predicting SoH, with a primary focus on data-driven methods based on machine learning, owing to the recent advancements in artificial intelligence (AI) techniques. To provide a systematic overview of the trends in this emerging problem, we present a comprehensive survey of classified SoH forecasting methods, with a primary focus on data-driven approaches. The paper also offers an in-depth focus on recent advancements in deep learning (DL) models, an area that has not been thoroughly discussed previously. Furthermore, we highlight the importance of input features and emphasize the critical role of temporal attributes incorporated into the models. The insights provided in this paper offer readers a comprehensive understanding of the field, equipping them to effectively advance related future work.

关键词： data-driven methods lithium-ion battery state-of-health time-series forecasting

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State of health estimation of lithium-ion batteries based on data-driven methods with a selected charging voltage interval

AIMS ENERGY

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AIMS ENERGY 2025年第2期13卷 290-308页

作者： Sun, Junguang Zhang, Xiaodong Cao, Wenrui Bo, Lili Liu, Changhai Wang, Bin China Natl Elect Apparat Res Inst Co Ltd State Key Lab Environm Adaptabil Ind Prod Guangzhou 510663 Peoples R China Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian 710049 Peoples R China China Aviat Lithium Battery Co Ltd Luoyang 471003 Peoples R China

Accurate state of health (SOH) estimation of lithium-ion batteries is of great importance for achieving efficient energy management in the overall battery energy storage system. Traditional data-driven methods for the SOH estimation of lithium-ion batteries usually require an enormous amount of data from the whole charging phase, which leads to poor performance in both computational efficiency and computational cost. To address this issue, this paper proposes the SOH estimation methods for lithium-ion batteries based on the limited data from a selected charging voltage interval. First, this study uses incremental capacity curves and Pearson correlation analysis to select an optimal and limited charging voltage interval that is the most relevant to lithium-ion battery degradation. Then, the SOH estimation based on two typical data-driven methods, including random forest regression (RFR) and support vector regression (SVR), would be implemented with the selected charging voltage interval. Results show that both the RFR and the SVR methods can achieve excellent accuracy, while each has its own irreplaceable advantages. However, compared with other voltage intervals using the two data-driven methods, the corresponding SOH estimation with the selected charging voltage interval shows the best performance. Hence, the data-driven methods based on the selected charging voltage interval have significant potential and advantages in the field of lithium-ion battery SOH estimation.

关键词： lithium-ion battery state of health data-driven methods random forest regression support vector regression

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data-driven methods for Battery SOH Estimation: Survey and a Critical Analysis

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IEEE ACCESS 2021年 9卷 126903-126916页

作者： Oji, Tsuyoshi Zhou, Yanglin Ci, Song Kang, Feiyu Chen, Xi Liu, Xiulan Tsinghua Univ Tsinghua Berkeley Shenzhen Inst Shenzhen 519071 Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Tsinghua Univ Tsinghua Shenzhen Int Grad Sch Shenzhen Key Lab Graphene Based Mat Engn Lab Functionalized Carbon Mat Shenzhen 518055 Peoples R China Beijing Elect Power Res Inst Beijing 100075 Peoples R China

State-of-health (SOH) estimation is a critical factor in ensuring the efficiency, reliability, and safety of lithium-ion batteries (LIBs) in electric vehicles (EVs). However, due to the complexity of electrochemical processes in batteries and the dynamics of working conditions, it is challenging to estimate SOH accurately, especially in real-world EV application scenarios. Thus, various data-driven methods with robust and adaptive features for SOH estimation have been widely proposed in the current literature. However, there is a lack of a comprehensive investigation and performance comparison of those methods, which makes them hard to be adopted in practice. Hence, in this paper, we have studied current major data-driven methods with real-world EV battery data to evaluate the performance. Besides, we summarize each method's advantages and limitations with the consideration of the critical features required to achieve accurate SOH estimation in real-world applications. Hopefully, this paper provides a practical insight into the related fields.

关键词： Mathematical model Estimation Integrated circuit modeling data models State of charge Kalman filters Degradation Lithium-ion battery state of health data-driven methods state estimation

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data-driven methods for Stealthy Attacks on TCP/IP-Based Networked Control Systems Equipped With Attack Detectors

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IEEE TRANSACTIONS ON CYBERNETICS 2019年第8期49卷 3020-3031页

作者： Wang, Jun-Sheng Yang, Guang-Hong Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Liaoning Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Liaoning Peoples R China

Most of the existing stealthy attack schemes for cyber-physical systems (CPSs) are presented under the assumption that the model parameters of CPS arc known to attackers. Presently, there are only a few model-independent stealthy attack approaches, which, however, need the assumption that attackers know sensor measurements and can modify them. This paper aims to remove the aforementioned conservative assumptions and give a stealthy attack methodology for closed-loop CPS with reference signals, that is, transmission control protocol/Internet protocol (TCP/IP)-based networked control systems. To this end, under the condition that the model parameters of the CPS are unknown, a benchmark platform (consisting of an attack detector and a TCP/IP-based networked de servo system) used for testing the stealthy attack technology is constructed via data-driven methods. A plan is made, which is utilized for eavesdropping the information of the TCP/IP-based CPS. On this basis, an approach to blocking network communications and injecting the false sensor data into the CPS is explored. A closed-loop recursive identification strategy for the dynamic characteristic matrix of the CPS is designed. By employing all of the above-obtained results, a data-driven stealthy attack scheme for the CPS is proposed and, subsequently, its effectiveness and practicability are validated by experiment.

关键词： Cyber-physical systems (CPSs) data-driven methods de servo systems stealthy attack transmission control protocol/Internet protocol (TCP/IP)

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data-driven methods for operational modal parameters identification: A comparison and application

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MEASUREMENT 2019年 132卷 238-251页

作者： Guan, Wei Dong, L. L. Zhou, J. M. Han, Yi Zhou, J. Xi An Jiao Tong Univ Sch Aerosp Xian 710049 Shaanxi Peoples R China Xi An Jiao Tong Univ State Key Lab Strength & Vibrat Mech Struct Xian 710049 Shaanxi Peoples R China

The operational modal parameters identification under ambient excitation has been widely applied in structural dynamics analysis, where the input force is difficult to measure. This paper makes comprehensive and systematic comparisons of four statistical learning algorithms (PCA, ICA, SOBI, LLE) on analyzing their performance for resolving operational modal parameters identification. It mainly focuses on the comparison and evaluation of the four data-driven methods based for operational modal analysis and explores the use of Hilbert Transform and Random Decrement Technique for the identification of modal damping ratios. The further tests, the robustness of four algorithms, are conducted for investigating the influences of external noise to the performance of the algorithms. On the basis of the modal expansion theory, the vibration response signals could be decomposed into the inner product of modal shapes matrix and modal responses vector in the modal coordinate, from which the modal shapes, modal natural frequencies and modal damping ratios can be well estimated. Hence, a one-to-one mapping between the mathematical model of four statistical learning algorithms and physical model of dynamic systems is established. To validate the effectiveness of the proposed methods, performance and comparison of the proposed methods are studied using a discrete three degree-of-freedom (DOF) system and continuous cantilever steel plate. The simulation and experimental results show that, for the same experimental data, the SOBI algorithm has better performance than other algorithms, and it is more suitable for operational modal identification. Finally, the characteristics of four algorithms and further directions are concluded and discussed in this paper. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Operational modal identification Structural dynamics data-driven methods Blind source separation Manifold learning

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data-driven methods distort optimal cutoffs and accuracy estimates of depression screening tools: a simulation study using individual participant data

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JOURNAL OF CLINICAL EPIDEMIOLOGY 2021年 137卷 137-147页

作者： Bhandari, Parash Mani Levis, Brooke Neupane, Dipika Patten, Scott B. Shrier, Ian Thombs, Brett D. Benedetti, Andrea Lady Davis Inst Med Res Jewish Gen Hosp Montreal PQ Canada McGill Univ Dept Epidemiol Biostat & Occupat Hlth Montreal PQ Canada Keele Univ Sch Med Ctr Prognosis Res Keele Staffs England Univ Calgary Dept Community Hlth Sci Calgary AB Canada McGill Univ Dept Family Med Montreal PQ Canada McGill Univ Dept Med Montreal PQ Canada McGill Univ Dept Psychiat Montreal PQ Canada McGill Univ Dept Psychol Montreal PQ Canada McGill Univ Dept Educ & Counselling Psychol Montreal PQ Canada McGill Univ Biomed Ethics Unit Montreal PQ Canada

Objective: To evaluate, across multiple sample sizes, the degree that data-driven methods result in (1) optimal cutoffs different from population optimal cutoff and (2) bias in accuracy estimates. Study design and setting: A total of 1,000 samples of sample size 100, 200, 500 and 1,000 each were randomly drawn to simulate studies of different sample sizes from a database (n = 13,255) synthesized to assess Edinburgh Postnatal Depression Scale (EPDS) screening accuracy. Optimal cutoffs were selected by maximizing Youden's J (sensitivity+specificity-1). Optimal cutoffs and accuracy estimates in simulated samples were compared to population values. Results: Optimal cutoffs in simulated samples ranged from >= 5 to >= 17 for n = 100, >= 6 to >= 16 for n = 200, >= 6 to >= 14 for n = 500, and >= 8 to >= 13 for n = 1,000. Percentage of simulated samples identifying the population optimal cutoff (>= 11) was 30% for n = 100, 35% for n = 200, 53% for n = 500, and 71% for n = 1,000. Mean overestimation of sensitivity and underestimation of specificity were 6.5 percentage point (pp) and -1.3 pp for n = 100, 4.2 pp and -1.1 pp for n = 200, 1.8 pp and -1.0 pp for n = 500, and 1.4 pp and -1.0 pp for n = 1,000. Conclusions: Small accuracy studies may identify inaccurate optimal cutoff and overstate accuracy estimates with data-driven methods. (C) 2021 Elsevier Inc. All rights reserved.

关键词： Optimal cutoff Accuracy estimates Bias Cherry-picking data-driven methods Depression

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data-driven methods to Diversify Knowledge of Human Psychology

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TRENDS IN COGNITIVE SCIENCES 2018年第1期22卷 1-5页

作者： Jack, Rachael E. Crivelli, Carlos Wheatley, Thalia Univ Glasgow Inst Neurosci & Psychol Glasgow Lanark Scotland Univ Glasgow Sch Psychol Glasgow Lanark Scotland De Montfort Univ Sch Appl Social Sci Leicester Leics England Dartmouth Coll Psychol & Brain Sci Hanover NH 03755 USA

Psychology aims to understand real human behavior. However, cultural biases in the scientific process can constrain knowledge. We describe here how data-driven methods can relax these constraints to reveal new insights that theories can overlook. To advance knowledge we advocate a symbiotic approach that better combines data-driven methods with theory.

关键词： cultural psychology data-driven methods diversity generalizability human universals

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Multistep-ahead flood forecasting using wavelet and data-driven methods

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KSCE JOURNAL OF CIVIL ENGINEERING 2015年第2期19卷 401-417页

作者： Seo, Youngmin Kim, Sungwon Singh, Vijay P. Kyungpook Natl Univ Dept Construct Disaster Prevent Engn Sangju 742711 South Korea Dongyang Univ Dept Railrd & Civil Engn Yeongju 750711 South Korea Texas A&M Univ Dept Biol & Agr Engn College Stn TX 77843 USA Texas A&M Univ Zachry Dept Civil Engn College Stn TX 77843 USA

Accurate forecasting of floods is vital for developing a flood warning systems, flood prevention, flood damage mitigation, soil erosion reduction and soil conservation. The objective of this study is to apply two hybrid models for flood forecasting and investigate their accuracy for different lead times. These two models are the Wavelet-based Artificial Neural Network (WANN) and the Wavelet-based Adaptive Neuro-Fuzzy Inference System (WANFIS). Wavelet decomposition is employed to decompose the flood time series into approximation and detail components. These decomposed time series are then used as inputs of Artificial Neural Network (ANN) and adaptive Neuro-Fuzzy Inference System (ANFIS) modules in the WANN and WANFIS models, respectively. The WANN and WANFIS models yielded better results than the ANN and ANFIS models for different lead times. The WANN and WANFIS models performed almost similarly. However, in terms of model efficiency, the WANFIS model was superior to other models for lead times of 1 to 6 hours, and the WANN model was superior to other models for lead time of 8 to 10 hours. The results obtained from this study indicate that the combination of wavelet decomposition and data-driven models, including ANN and ANFIS, can improve the efficiency of data-driven models. Results also indicate that the combination of wavelet decomposition and data-driven models can be a potential tool for forecasting flood stage more accurately.

关键词： flood stage forecasting discrete wavelet decomposition data-driven methods artificial neural network adaptive neuro-fuzzy inference system Wavelet-based ANN Wavelet-based ANFIS

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An Investigation of Contemporary data-driven methods Applied to Complex Systems

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IEEE ACCESS 2022年 10卷 134744-134757页

作者： Dwivedi, Saumitra Torres, Ricardo Da Silva Hameed, Ibrahim A. Karlsen, Anniken Susanne T. Norwegian Univ Sci & Technol Dept ICT & Nat Sci N-6025 Alesund Norway Norwegian Univ Sci & Technol Dept Comp Sci N-7491 Trondheim Norway Wageningen Univ & Res Wageningen Data Competence Ctr Farm Technol Grp NL-6708 PB Wageningen Netherlands

Understanding complex systems by the help of modeling, simulation, and control is a well-known challenge across several application domains. As such, these type of systems are not amenable to empirical models, typically due to their dynamic and non-linear nature. data-driven methods, have in recent years gained traction regarding their application to such complex systems. This paper presents findings from a systematic literature review of data-driven methods applied to complex systems in recent years. The review is a result of a search strategy resulting in 1106 scientific publications, out of which forty one were identified as primary studies. It is our goal that the review findings will guide researchers with an evidence-base regarding data-driven methods, thereby equipping them with an arsenal of applied methods across various application domains. Also, the objective is to construct a knowledge-base based on these methods' contributions while applied to different types of problems. Additionally, the review findings may also guide researchers to realize potential gaps for future research.

关键词： Complex systems data-driven methods systematic map systematic review

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