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data-driven Detection of Hot Spots in Photovoltaic Modules Using Information Fusion

INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2025年第3期23卷 777-787页

作者： Yi, Hui Liu, Jianfei Zeng, Deshan Yang, Chen Chen, Hongtian Nanjing Tech Univ Coll Elect Engn & Control Sci Nanjing 211800 Peoples R China Nari Technol Co Ltd Nanjing 211106 Peoples R China Univ Calif San Diego Dept Comp Sci & Engn San Diego CA 92093 USA Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China

Hot spots are common defects in photovoltaic (PV) modules that can lead to performance degradation and even pose a fire hazard. This study proposes an online detection methodology for hot spots within a data-driven framework. The modeling of PV modules under normal conditions relies on the integration of two-dimensional information. The first dimension pertains to the correlation among measured variables across multiple tests, while the second dimension captures the local nonlinearity among past, present, and future measurements from PV modules. By combining these two dimensions of information, accurate modeling of PV modules can be achieved in a data-driven manner. Based on this model, the performance-oriented detection of hot spots in PV modules is formulated in detail. The proposed approach is then applied to both darkroom and outdoor PV systems, with a total of 23 tests and comparisons conducted. The results of these tests and comparisons convincingly demonstrate the validity and superiority of the proposed method.

关键词： data-driven methods hot spots information infusion modeling and detection photovoltaic modules

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Individualized models of social judgments and context-dependent representations

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SCIENTIFIC REPORTS 2025年第1期15卷 1-15页

作者： Albohn, Daniel N. Uddenberg, Stefan Todorov, Alexander Univ Chicago Booth Sch Business Chicago IL 60615 USA

How individuals view the world is critical to understanding human behavior. Yet, almost all research within perception and judgment has drawn inferences from group-level behavior, with little work focused on understanding how the individual perceives their world. However, for complex judgments (e.g., trustworthiness), most of the meaningful variance is due to factors specific to the individual. Here we showcase a data-driven reverse correlation method for visualizing any perceptually-derived stereotype at the individual level. We show that our method (1) produces photorealistic and reliable results related to a broad range of judgments, (2) produces valid, psychologically-aligned representations of what individuals are imagining "in their mind's eye", and (3) is capable of capturing visual representations sensitive enough to examine context-dependent categories (e.g., a trustworthy individual to babysit your children vs. to fix your car). Across all studies, we highlight the theoretical implications and utility of developing idiosyncratic models of visual perception.

关键词： Idiosyncratic models Reverse correlation data-driven methods Generative modeling

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A multimodal approach to depression diagnosis: insights from machine learning algorithm development in primary care

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EUROPEAN ARCHIVES OF PSYCHIATRY AND CLINICAL NEUROSCIENCE 2025年 1-14页

作者： Eder, Julia Dong, Mark Sen Woehler, Melanie Simon, Maria S. Glocker, Catherine Pfeiffer, Lisa Gaus, Richard Wolf, Johannes Mestan, Kadir Krcmar, Helmut Koutsouleris, Nikolaos Schneider, Antonius Gensichen, Jochen Musil, Richard Falkai, Peter POKAL Grp Ludwig Maximilians Univ Munchen LMU Univ Hosp Dept Psychiat & Psychotherapy Nussbaumstr 7 D-80336 Munich Germany Grad Program POKAL Predictors & Outcomes Primary C Munich Germany German Ctr Mental Hlth DZPG Partner Site Munich Augsburg Munich Germany Tech Univ Munich TUM Chair Informat Syst D-85748 Garching Germany Kings Coll London Inst Psychiat Psychol & Neurosci London England Tech Univ Munich Inst Gen Practice & Hlth Serv Res Sch Med Munich Germany Ludwig Maximilians Univ Munchen Inst Gen Practice & Family Med Munich Germany Oberberg Fachklin Bad Tolz Bad Tolz Germany Max Planck Inst Psychiat Munich Germany

General practitioners play an essential role in identifying depression and are often the first point of contact for patients. Current diagnostic tools, such as the Patient Health Questionnaire-9, provide initial screening but might lead to false positives. To address this, we developed a two-step machine learning model called Clinical 15, trained on a cohort of 581 participants using a nested cross-validation framework. The model integrates self-reported data from validated questionnaires within a study sample of patients presenting to general practitioners. Clinical 15 demonstrated a balanced accuracy of 88.2% and incorporates a traffic light system: green for healthy, red for depression, and yellow for uncertain cases. Gaussian mixture model clustering identified four depression subtypes, including an Immuno-Metabolic cluster characterized by obesity, low-grade inflammation, autonomic nervous system dysregulation, and reduced physical activity. The Clinical 15 algorithm identified all patients within the immuno-metabolic cluster as depressed, although 22.2% (30.8% across the whole dataset) were categorized as uncertain, leading to a yellow traffic light. The biological characterization of patients and monitoring of their clinical course may be used for differential risk stratification in the future. In conclusion, the Clinical 15 model provides a highly sensitive and specific tool to support GPs in diagnosing depression. Future algorithm improvements may integrate further biological markers and longitudinal data. The tool's clinical utility needs further evaluation through a randomized controlled trial, which is currently being planned. Additionally, assessing whether GPs actively integrate the algorithm's predictions into their diagnostic and treatment decisions will be critical for its practical adoption.

关键词： Depression diagnosis Primary care Depression subtypes data-driven methods Predictive algorithm

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A new data-driven paradigm for the study of avian migratory navigation

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MOVEMENT ECOLOGY 2025年第1期13卷 1-12页

作者： Demsar, Urska Zein, Beate Long, Jed A. Norwegian Inst Nat Res Trondheim Norway Western Univ Ctr Anim Move Dept Geog & Environm London ON Canada Univ St Andrews Sch Geog & Sustainable Dev Irvine BldgNorth St St Andrews KT16 9AL Scotland

Avian navigation has fascinated researchers for many years. Yet, despite a vast amount of literature on the topic it remains a mystery how birds are able to find their way across long distances while relying only on cues available locally and reacting to those cues on the fly. Navigation is multi-modal, in that birds may use different cues at different times as a response to environmental conditions they find themselves in. It also operates at different spatial and temporal scales, where different strategies may be used at different parts of the journey. This multi-modal and multi-scale nature of navigation has however been challenging to study, since it would require long-term tracking data along with contemporaneous and co-located information on environmental cues. In this paper we propose a new alternative data-driven paradigm to the study of avian navigation. That is, instead of taking a traditional theory-based approach based on posing a research question and then collecting data to study navigation, we propose a data-driven approach, where large amounts of data, not purposedly collected for a specific question, are analysed to identify as-yet-unknown patterns in behaviour. Current technological developments have led to large data collections of both animal tracking data and environmental data, which are openly available to scientists. These open data, combined with a data-driven exploratory approach using data mining, machine learning and artificial intelligence methods, can support identification of unexpected patterns during migration, and lead to a better understanding of multi-modal navigational decision-making across different spatial and temporal scales.

关键词： Avian navigation Multi-modal navigation Multi-scale navigation data-driven methods Tracking data Environmental data data mining Machine learning Artificial intelligence

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Enhancing accurate prediction of soil permeability coefficients using data-driven approaches for soil and water conservation

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2025年第1期8卷 1-13页

作者： Luo, Libo Guan, Di Wang, Zhen Changjiang Water Resources & Hydropower Dev Grp Hu Wuhan 123456 Hubei Peoples R China

Soil permeability coefficient (k) is crucial for water conservation, drainage projects, and watershed management. In the present study, for modeling k, two powerful white box data-driven techniques, including Multivariate Adaptive Regression Splines (MARS) and Gene Expression Programming (GEP), and soil physical parameters including water content (omega\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\omega$$\end{document}), void ratio (e\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$e$$\end{document}), liquid limit (LL\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$LL$$\end{document}), plastic limit (PL\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$PL$$\end{document}), clay content (CC\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$CC$$\end{document}) and specific density (gamma\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\gamma$$\end{document}), were used. MARS provides simple linear funct

关键词： data-driven methods Soil permeability coefficient MARS GEP SHAP analysis

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Conventional and advanced AI-based models in soil moisture prediction

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PHYSICS AND CHEMISTRY OF THE EARTH 2025年 139卷

作者： Kheimi, Marwan Zounemat-Kermani, Mohammad King Abdulaziz Univ Fac Engn Dept Civil & Environm Engn Rabigh Branch Jeddah 21589 Saudi Arabia Shahid Bahonar Univ Kerman Dept Civil Engn Kerman Iran

This study evaluates the predictive accuracy of various computational models for Soil Moisture (SM) content, including (i) hard computing models such as mathematical (MLR) and stochastic techniques (AR, ARMA, ARIMA), and (ii) soft computing AI-based models such as shallow learning (NAR, NARX, MLPNN), advanced deep learning (LSTM, DRNN, CNN), and ensemble learning (Bagging, Boosting, and Adaboost). Using data from a sandy clay loam soil area, the models are developed and then compared for accuracy performance, tendency, and computational expense. Results indicate that hard computing models, particularly the stochastic AR model, do not act properly in predicting daily SM values, so that they could not improve the general predictive accuracy in comparison to the Na & iuml;ve model based on several evaluation metrics. Shallow machine learning models like NAR and MLPNN perform better than the hard computing models, especially when they get the advantage of exogenous input vector (here, precipitation data). Deep learning models (Pearson Correlation Coefficient: PCC >0.9 and RMSE <1.39), especially the LSTM, exhibited higher accuracy than shallow learning models, however, they were the least favorite category in terms of computational cost. On the other hand, ensemble models show the best performance (PCC >0.92, RMSE >1.28) by combining multiple learners' strengths. In summary, the use of ensemble modeling improved the modeling accuracy of RMSE and PCC up to 6 % and 23 % in comparison to stochastic models, respectively.

关键词： Hydrological modeling Committee learning Soil science data-driven methods Machine learning

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Boundary Integral Neural Networks for Acoustic Radiation Prediction from Noisy Boundary data

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JOURNAL OF THEORETICAL AND COMPUTATIONAL ACOUSTICS 2025年第0期

作者： Schmid, Johannes D. Preuss, Simone Maicher, Lukas Marburg, Steffen Tech Univ Munich Chair Vibroacoust Vehicles & Machines TUM Sch Engn & Design Dept Engn Phys & Computat Boltzmannstr 15 D-85748 Garching Germany

Accurate predictions of sound radiation are crucial for assessing sound emissions in the far field. A widely used approach is the boundary element method, which traditionally solves the Kirchhoff-Helmholtz integral equation by discretization. While the boundary integral formulation inherently satisfies the Sommerfeld radiation condition, a significant drawback of the boundary element method is its difficulty in incorporating noisy measurement data. In recent years, physics-informed machine learning approaches have demonstrated robust predictions of physical problems, even in the presence of noisy or imperfect data. To utilize these benefits while addressing acoustic predictions in unbounded domains, this study employs boundary integral neural networks for predicting acoustic radiation. These networks incorporate the residual of the boundary integral equation into the neural network's loss function, enabling data-driven predictions of acoustic radiation from noisy boundary data. The results demonstrate that boundary integral neural networks are able to accurately predict the sound pressure field for both interior and exterior problems of a two-dimensional acoustic domain. The study also highlights that the data-driven approach outperforms the conventional boundary element method, particularly at high noise levels. Consequently, the presented method offers a promising approach for predicting sound radiation based on noisy surface vibration measurements.

关键词： Physics-informed neural networks boundary integrals machine learning data-driven methods acoustics

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Energy Theft in Smart Grids: A Survey on data-driven Attack Strategies and Detection methods

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IEEE ACCESS 2021年 9卷 159291-159312页

作者： Althobaiti, Ahlam Jindal, Anish Marnerides, Angelos K. Roedig, Utz Univ Lancaster Sch Comp & Commun Lancaster LA1 4WA England Univ Essex Sch Comp Sci & Elect Engn Colchester CO4 3SQ Essex England Univ Glasgow Sch Comp Sci Glasgow G12 8QQ Lanark Scotland Univ Coll Cork Sch Comp Sci & Informat Technol CSIT Cork T12 K8AF Ireland

The convergence of legacy power system components with advanced networking and communication facilities have led towards the development of smart grids. Smart grids are envisioned to be the next generation innovative power systems, guaranteeing resilience, reliability and sustainability and to facilitate energy production, distribution and management. Nonetheless, the development of such systems entails challenges covering a broad spectrum ranging from operational management up to data-driven power accounting and network security. Given the highly distributed properties of the modern grid, energy theft can now be observed at various transmission and distribution levels. Apart from the financial gain for a malicious actor, energy theft can also affect critical grid processes with a direct impact on its overall resilience and safety. This survey reviews recent energy theft strategies as well as detection methods from a data-driven perspective. By considering various operational and functional layers within modern smart grids we critically assess how energy theft can be formulated. Moreover, we provide an overview of the grid demand, supply and control chain with a focus on energy theft and associated security flaws that currently exist in the smart grid ecosystem. Different attack detection models for theft detection in the smart grid are categorized. Lastly, we discuss various open issues in the scope of data-driven energy theft detection methods and provide future directions to carry out research in this field.

关键词： Smart grids Resilience Renewable energy sources Reliability Power transmission lines Licenses Companies Energy theft data-driven methods smart grid cybersecurity

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data-driven landslide forecasting: methods, data completeness, and real-time warning

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ENGINEERING GEOLOGY 2023年第1期317卷

作者： Xiao, Te Zhang, Li -Min Hong Kong Univ Sci & Technol Dept Civil & Environm Engn Hong Kong Peoples R China HKUST Shenzhen Hong Kong Collaborat Innovat Res In Shenzhen Futian Peoples R China

Various data-driven methods, including empirical, statistical, and machine learning methods, have been devel-oped to promptly forecast rain-induced landslides. Their abilities differ considerably in spatio-temporal landslide prediction and in handling datasets of varying qualities. A challenging issue that significantly hinders the ap-plications of data-driven methods is the data incompleteness in most landslide inventories, particularly the lack of accurate landslide time that is a vital link between each landslide and its triggering rainstorm. This study systematically compares the performances of three categories of data-driven methods for landslide prediction and proposes a novel machine learning model featured by probabilistic landslide modelling for spatio-temporal landslide prediction. The integrated machine learning model can be developed on a realistic landslide database, regardless of whether the landslide timing information is known or not. It not only promptly predicts the spatio-temporal evolution of landslides during a rainstorm but also reliably characterises the factual landslide risk, which provides a powerful real-time decision-making tool for landslide early warning and risk management. The model is validated against the landslide incidents in Hong Kong in the past 35 years both spatially and temporally, and outperforms other data-driven models in both prediction ability and accuracy.

关键词： Rain-induced landslides Landslide forecasting Landslide risk data-driven methods Machine learning

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data-driven eigensolution analysis based on a spatio-temporal Koopman decomposition, with applications to high-order methods

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JOURNAL OF COMPUTATIONAL PHYSICS 2022年 449卷 110798-110798页

作者： Kou, Jiaqing Le Clainche, Soledad Ferrer, Esteban Univ Politecn Madrid ETSIAE UPM Sch Aeronaut Plaza Cardenal Cisneros 3 E-28040 Madrid Spain NUMECA Int SA Chaussee Hulpe 187 B-1170 Brussels Belgium Univ Politecn Madrid Ctr Computat Simulat Campus Montegancedo Madrid 28660 Spain

We propose a data-driven method to perform eigensolution analyses and quantify numerical errors in a non-intrusive manner. In classic eigensolution analysis methods, explicit matrices need to be constructed, whilst in our approach only solution snapshots from numerical simulations are required to quantify the numerical errors (dispersion and diffusion) in time and/or space. This new approach is based on a recent data-driven method: the Spatio-Temporal Koopman Decomposition (STKD), that approximates spatio-temporal data as a linear combination of standing or travelling waves growing or decaying exponentially in time and/or space. We validate our approach with classic matrix-based approaches, where accurate predictions of the dispersion-dissipation behaviour for both temporal and spatial eigensolution analyses are reported. (C) 2021 The Author(s). Published by Elsevier Inc.

关键词： Eigensolution analysis Dispersion-diffusion analysis Flux reconstruction Spectral/hp methods data-driven methods Koopman analysis

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