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Groundwater level simulation and forecasting using interior search algorithm-least square support vector regression (ISA-LSSVR)

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GROUNDWATER FOR SUSTAINABLE DEVELOPMENT 2020年 11卷

作者： Moravej, Mojtaba Amani, Pouria Hosseini-Moghari, Seyed-Mohammad Lund Univ Water Resources Engn Div LTH Lund Sweden Univ Queensland Adv Water Management Ctr St Lucia Qld 4072 Australia Univ Queensland Sch Chem Engn St Lucia Qld 4072 Australia Chinese Acad Sci Inst Geog Sci & Nat Resources Res Key Lab Water Cycle & Related Land Surface Proc Beijing 100101 Peoples R China

Least square support vector regression (LSSVR) is a powerful data-driven method for simulation and forecasting, with two parameters to tune. In this study, these parameters were automatically tuned using the interior search algorithm (ISA) and genetic algorithm (GA). The main purpose is in situ simulation and forecast of monthly groundwater level in Karaj plain, Iran, using historical groundwater level, precipitation, and evaporation data. The results of the interior search algorithm-least support vector regression (ISA-LSSVR) and genetic algorithm-least support vector regression (GA-LSSVR) compared with genetic programming (GP) and adaptive neural fuzzy inference system (ANFIS). Based on average Nash-Sutcliffe criterion, the results revealed that the ISA-LSSVR improves the simulation and forecasting accuracy compared to other methods. Also, the results of the different model structure selection indicate that including precipitation and evaporation does not necessarily improve simulation and forecasting accuracy, but it would increase uncertainty. This increase suggests that groundwater level in the case study is affected by groundwater flow, recharge from leaky urban water infrastructure, and reduced recharge from precipitation due to impervious surfaces in urban areas rather than being solely governed by precipitation and evaporation. Finally, a sensitivity analysis was performed to assess the impacts of optimization algorithm parameters on the simulation and forecasting accuracy. The results indicate high and low sensitivity associated with GA and ISA, respectively. In conclusion, ISA-LSSVR was suggested as the best model due to computational efficiency, low sensitivity to its parameters, and high accuracy compared to other methods.

关键词： data-driven methods Genetic algorithm Optimization Karaj aquifer Sensitivity analysis Support vector machines

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Facial expressions elicit multiplexed perceptions of emotion categories and dimensions

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CURRENT BIOLOGY 2022年第1期32卷 200-+页

作者： Liu, Meng Duan, Yaocong Ince, Robin A. A. Chen, Chaona Garrod, Oliver G. B. Schyns, Philippe G. Jack, Rachael E. Univ Glasgow Sch Psychol & Neurosci 62 Hillhead St Glasgow G12 8QB Lanark Scotland

Human facial expressions are complex, multi-component signals that can communicate rich information about emotions,(1-5) including specific categories, such as "anger," and broader dimensions, such as "negative valence, high arousal."(6-8) An enduring question is how this complex signaling is achieved. Communication theory predicts that multi-component signals could transmit each type of emotion information-i.e., specific categories and broader dimensions-via the same or different facial signal components, with implications for elucidating the system and ontology of facial expression communication.(9) We addressed this question using a communication-systems-based method that agnostically generates facial expressions and uses the receiver's perceptions to model the specific facial signal components that represent emotion category and dimensional information to them.(10)(-12) First, we derived the facial expressions that elicit the perception of emotion categories (Le., the six classic emotions(13) plus 19 complex emotions(3)) and dimensions (i.e., valence and arousal) separately, in 60 individual participants. Comparison of these facial signals showed that they share subsets of components, suggesting that specific latent signals jointly represent-i.e., multiplex-categorical and dimensional information. Further examination revealed these specific latent signals and the joint information they represent. Our results-based on white Western participants, same-ethnicity face stimuli, and commonly used English emotion terms-show that facial expressions can jointly represent specific emotion categories and broad dimensions to perceivers via multiplexed facial signal components. Our results provide insights into the ontology and system of facial expression communication and a new information-theoretic framework that can characterize its complexities.

关键词： facial expressions emotion categories affective dimensions communication perception data-driven methods information theory reverse correlation

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Large-Scale Two-Sample Comparison of Support Sets

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JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION 2024年第546期119卷 1604-1618页

作者： Geng, Haoyu Cui, Xiaolong Ren, Haojie Zou, Changliang Nankai Univ Sch Stat & Data Sci LPMC KLMDASR Tianjin Peoples R China Nankai Univ LEBPS Tianjin Peoples R China Shanghai Jiao Tong Univ Sch Math Sci Shanghai Peoples R China

Two-sample multiple testing has a wide range of applications. Most of the literature considers simultaneous tests of equality of parameters. The article takes a different perspective and investigates the null hypotheses that the two support sets are equal. This formulation of the testing problem is motivated by the fact that in many applications where the two parameter vectors being compared are both sparse, one might be more concerned about the detection of differential sparsity structures rather than the difference in parameter magnitudes. Focusing on this type of problem, we develop a general approach, which adapts the newly proposed symmetry data aggregation tool combined with a novel double thresholding (DT) filter. The DT filter first constructs a sequence of pairs of ranking statistics that fulfill global symmetry properties and then chooses two data-driven thresholds along the ranking to simultaneously control the False Discovery Rate (FDR) and maximize the number of rejections. Several applications of the methodology are given including high-dimensional linear models and Gaussian graphical models. We show that the proposed method is able to asymptotically control the FDR and have power guarantee under certain conditions. Numerical results confirm the effectiveness and robustness of DT in FDR control and detection ability. for this article are available online.

关键词： data-driven methods Gaussian graphical models High-dimensional models Multiple testing Sample-splitting Uniform convergence

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data-driven XGBoost-based filter for target tracking

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JOURNAL OF ENGINEERING-JOE 2019年第20期2019卷 6683-6687页

作者： Zhai, Bowen Yi, Wei Li, Ming Ju, Hao Kong, Lingjiang Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu Sichuan Peoples R China

In recent years, the data-driven approach has been introduced in the field of target tracking as a powerful tool developing the end-to-end mapping relationship between input features and outputs. Typically, in data-driven methods, neural networks serve as a supplement of traditional Bayesian filters for improved estimation accuracy. However, these hybrid methods are somehow complicated to realise. In this study, inspired by the idea of direct-mapping from measurements to states, simpler method by developing a data-driven XGBoost-based Filter (DXGBF) is proposed. The DXGBF consists of four components, namely data generator, sliding window, centralisation strategy and XGBoost learner (XL). The data generator generates simulated data from the probabilistic model in the training phase. By intercepting the measurements, the sliding window enables DXGBF to track targets online. The centralisation strategy extracts the relevant kinematic information from different tracks that enables DXGBF to track randomly initialised targets. The XL is responsible for learning a function that mapping to estimate states. Simulation results show that the estimation accuracy of DXGBF is higher than those of Kalman filter, sampling importance resampling particle filter and the data-driven random-forest-based filter.

关键词： Computer vision and image processing techniques end-to-end mapping relationship centralisation strategy data generator learning (artificial intelligence) data-driven XGBoost-based Filter probability data-driven approach Other topics in statistics Knowledge engineering techniques sliding window particle filtering (numerical methods) randomly initialised targets Filtering methods in signal processing Bayes methods neural nets Kalman filters different tracks data-driven methods data-driven random-forest-based filter data-driven XGBoost-based filter traditional Bayesian filters target tracking DXGBF

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A data-driven nonlinear frequency response approach based on the Loewner framework: preliminary analysis

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IFAC-PapersOnLine 2023年第1期56卷 234-239页

作者： Ion Victor Gosea Luka A. Živković Dimitrios S. Karachalios Tanja Vidaković-Koch Athanasios C. Antoulas Max Planck Institute Magdeburg Germany DRI group Max Planck Institute Magdeburg Germany EEC group Electrical and Computer Engineering (ECE) Department Rice University Houston Max Planck Institute Magdeburg Germany (DRI group) and Baylor College of Medicine Houston USA

We propose a hybrid method based on the combination of computed-aided nonlinear frequency response analysis with the Loewner framework, for the characterization of nonlinear dynamical processes with application in electrochemistry. The method is data-driven, i.e., requiring only samples of the first two generalized transfer functions of the underlying system, given as values of the sampled nonlinear frequency response. Then, the established system fitting and complexity reduction approach (in the frequency domain), known as the Loewner framework, is used to extract the system's invariant quantities. In this analysis, we have used a nonlinear electrical circuit model as a test case, for which the new method provides good results.

关键词： data-driven methods Nonlinear Dynamics Nonlinear Frequency Response Transfer Functions Frequency Response Functions Model Reduction Non-intrusive Approach

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Experiment design for impulse response identification with signal matrix models ⁎

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IFAC-PapersOnLine 2021年第7期54卷 625-630页

作者： Andrea Iannelli Mingzhou Yin Roy S. Smith Automatic Control Lab ETH Zürich 8092 Switzerland

This paper formulates an input design approach for truncated infinite impulse response identification in the context of implicit model representations recently used as basis for data-driven simulation and control approaches. Precisely, the considered model consists of a linear combination of the columns of a data (or signal) matrix. An optimal combination for the case of noisy data was recently proposed using a maximum likelihood approach, and the objective here is to optimize the input entries of the data matrix such that the mean-square error matrix of the estimate is minimized. A least-norm problem is derived in terms of the optimality criteria typically considered in the experiment design literature. Numerical results showcase the improved estimation fit achieved with the optimized input.

关键词： System identification Experiment design data-driven methods IIR estimation

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Meta-Learning of Neural State-Space Models Using data From Similar Systems

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IFAC-PapersOnLine 2023年第2期56卷 1490-1495页

作者： Ankush Chakrabarty Gordon Wichern Christopher R. Laughman Mitsubishi Electric Research Laboratories Cambridge MA 02139 USA

Deep neural state-space models (SSMs) provide a powerful tool for modeling dynamical systems solely using operational data. Typically, neural SSMs are trained using data collected from the actual system under consideration, despite the likely existence of operational data from similar systems which have previously been deployed in the field. In this paper, we propose the use of model-agnostic meta-learning (MAML) for constructing deep encoder network-based SSMs, by leveraging a combination of archived data from similar systems (used to meta-train offline) and limited data from the actual system (used for rapid online adaptation). We demonstrate using a numerical example that meta-learning can result in more accurate neural SSM models than supervised or transfer-learning, despite few adaptation steps and limited online data. Additionally, we show that by carefully partitioning and adapting the encoder layers while fixing the state-transition operator, we can achieve comparable performance to MAML while reducing online adaptation complexity.

关键词： Learning modeling data-driven methods Koopman operator system identification

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Prediction of hydrogel swelling states using machine learning methods

ENGINEERING REPORTS

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ENGINEERING REPORTS 2024年第11期6卷

作者： Wang, Yawen Wallmersperger, Thomas Ehrenhofer, Adrian Tech Univ Dresden Inst Festkorpermechan D-01062 Dresden Germany Tech Univ Dresden Dresden Ctr Intelligent Mat Dresden Germany

In the field of material informatics, artificial neural networks (ANNs) contribute to the investigation of the processing-structure-properties-performance relationship of materials. This inspires us to leverage the capabilities of ANNs to decode properties of hydrogels, thereby customizing these active materials for sensors or actuators. In the current work, we introduce an approach to predict discrete swelling states of temperature-responsive hydrogels, especially PNIPAAm, based on their synthesis parameters, utilizing ANN models. To build the database, we analyze literature on temperature-responsive hydrogels and compile essential synthesis parameters. The corresponding data points related to these synthesis parameters are then extracted. We propose different variants of ANN models and compare their accuracy on the acquired dataset. The selected model can predict the swelling states of hydrogel samples within the test dataset with relative prediction error of 0.11. This approach is applied to predict the expected properties. Subsequently, the hydrogels can be synthesized, and their properties can be experimentally verified. Our approach can be extended to other types of hydrogels and in the prediction of additional properties. The identified synthesis parameters serve as a valuable foundation for the expansion of the database with further literature resources. An enriched database will enhance the performance of the data-driven model, thereby improving its predictive capabilities. Artificial neural networks (ANNs) can be used to model the processing-structure-property-performance (PSPP) relationship of hydrogels. The current article is focused on regression model A, which predicts the swelling properties of hydrogels, directly based on the synthesis parameters, thus leapfrogging the inner structure description. Based on our model, the research of the PSPP relationship can be completed in the future to predict the influence of synthesis parameters towards the perfo

关键词： active materials artificial neural networks data-driven methods hydrogel swelling materials informatics

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A dataset of Service Time and Related Patient Characteristics from an Outpatient Clinic

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data 2023年第3期8卷

作者： Feng, Haolin Jia, Yiwu Zhou, Siyi Chen, Hongyi Huang, Teng Sun Yat sen Univ Sch Business Guangzhou 510275 Peoples R China Sun Yat Sen Univ Lingnan Coll Guangzhou 510275 Peoples R China Hangu TCM Innovat & Res Inst Guangzhou 510627 Peoples R China

Outpatient clinics' productivity largely depends on their appointment scheduling systems. It is crucial for appointment scheduling to understand the intrinsic heterogeneity in patient and service types and act accordingly. This article describes an outpatient clinic dataset of consultation service time with heterogeneous characteristics. The dataset contains 6637 consultation records collected from 381 half-day sessions between 2018 and 2019. Each record includes encrypted session and patient IDs, consultation start and (approximated) end times, the month and day of the week, whether it was on a holiday, the patient's visit count for a specific medical condition, gender, whether the consultation was cancer-related, and the distance from the patient's mailing address to the clinic. These features can be used to classify patients into heterogeneous groups in studies of appointment scheduling. Therefore, this dataset with rich, heterogeneous patient characteristics provides a valuable opportunity for healthcare operations management researchers to develop, test, and benchmark the performance of their models and methods. It can also be used for studying appointment scheduling in other service industries. More generally, it provides pedagogical value in areas related to management science and operations research, applied statistics, and machine learning.

关键词： healthcare systems appointment scheduling outpatient clinic patient heterogeneity data-driven methods machine learning

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Unsupervised Deep Learning for Structural Health Monitoring

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BIG data AND COGNITIVE COMPUTING 2023年第2期7卷

作者： Boccagna, Roberto Bottini, Maurizio Petracca, Massimo Amelio, Alessia Camata, Guido Univ G Annunzio Chieti Pescara Dept Engn & Geol I-65127 Pescara Italy Asdea Software I-65128 Pescara Italy

In the last few decades, structural health monitoring has gained relevance in the context of civil engineering, and much effort has been made to automate the process of data acquisition and analysis through the use of data-driven methods. Currently, the main issues arising in automated monitoring processing regard the establishment of a robust approach that covers all intermediate steps from data acquisition to output production and interpretation. To overcome this limitation, we introduce a dedicated artificial-intelligence-based monitoring approach for the assessment of the health conditions of structures in near-real time. The proposed approach is based on the construction of an unsupervised deep learning algorithm, with the aim of establishing a reliable method of anomaly detection for data acquired from sensors positioned on buildings. After preprocessing, the data are fed into various types of artificial neural network autoencoders, which are trained to produce outputs as close as possible to the inputs. We tested the proposed approach on data generated from an OpenSees numerical model of a railway bridge and data acquired from physical sensors positioned on the Historical Tower of Ravenna (Italy). The results show that the approach actually flags the data produced when damage scenarios are activated in the OpenSees model as coming from a damaged structure. The proposed method is also able to reliably detect anomalous structural behaviors of the tower, preventing critical scenarios. Compared to other state-of-the-art methods for anomaly detection, the proposed approach shows very promising results.

关键词： artificial intelligence structural health monitoring autoencoders deep learning OpenSees numerical model sensors civil engineering numerical simulations data-driven methods

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