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Visually Guided Acquisition of Contact Dynamics and Case Study in data-driven Haptic Texture modeling

IEEE TRANSACTIONS ON HAPTICS 2020年第3期13卷 611-627页

作者： Abdulali, Arsen Atadjanov, Ibragim R. Jeon, Seokhee Kyung Hee Univ Dept Comp Sci & Engn Yongin 17104 South Korea

data-driven modeling of human hand contact dynamics starts with a tedious process of data collection. The data of contact dynamics consist of an input describing an applied action and response stimuli from the environment. The quality and stability of the model mainly depend on how well data points cover the model space. Thus, in order to build a reliable data-driven model, a user usually collects data dozens of times. In this article, we aim to build an interactive system that assists a user in data collection. We develop an online segmentation framework that partitions a multivariate streaming signal. Real-time segmentation allows for tracking the process of how the model space is being populated. We applied the proposed framework for a haptic texture modeling use-case. In order to guide a user in data collection, we designed a user interface mapping applied input to alternative visual modalities based on the theory of direct perception. A combination of the segmentation framework and user interface implements a human-in-loop system, where the user interface assigns the target combination of input variables and the user tries to acquire them. Experimental results show that the proposed data collection schema considerably increases the approximation quality of the model, whereas the proposed user interface considerably reduces mental workload experienced during data collection.

关键词： data collection data models Haptic interfaces Solid modeling Visualization User interfaces Robots data-driven modeling data segmentation visual guidance data collection interface

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Confidence score: a data-driven measure for inclusive systematic reviews considering unpublished preprints

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2024年第4期31卷 809-819页

作者： Tong, Jiayi Luo, Chongliang Sun, Yifei Duan, Rui Saine, M. Elle Lin, Lifeng Peng, Yifan Lu, Yiwen Batra, Anchita Pan, Anni Wang, Olivia Li, Ruowang Marks-Anglin, Arielle Yang, Yuchen Zuo, Xu Liu, Yulun Bian, Jiang Kimmel, Stephen E. Hamilton, Keith Cuker, Adam Hubbard, Rebecca A. Xu, Hua Chen, Yong Univ Penn Ctr Hlth Analyt & Synth Evidence CHASE Dept Biostat Epidemiol & Informat Philadelphia PA 19104 USA Univ Penn Perelman Sch Med Dept Biostat Epidemiol & Informat Philadelphia PA 19104 USA Washington Univ Div Publ Hlth Sci Sch Med St Louis MO 63110 USA Columbia Univ Dept Biostat New York NY 10032 USA Harvard Univ Harvard TH Chan Sch Publ Hlth Dept Biostat Cambridge MA 02115 USA Univ Arizona Dept Epidemiol & Biostat Tucson AZ 85724 USA Weill Cornell Med Dept Populat Hlth Sci New York NY 11101 USA Univ Penn Sch Arts & Sci Grad Grp Appl Math & Computat Sci Philadelphia PA 19104 USA Cedars Sinai Med Ctr Dept Computat Biomed West Hollywood CA USA Univ Texas Hlth Sci Ctr Houston McWilliams Sch Biomed Informat Houston TX 77030 USA Univ Texas Southwestern Med Ctr Dallas Peter ODonnell Jr Sch Publ Hlth Dallas TX 75390 USA Univ Florida Coll Med Dept Hlth Outcomes & Biomed Informat Gainesville FL 32611 USA Univ Florida Coll Publ Hlth & Hlth Profess Dept Epidemiol Gainesville FL 32610 USA Univ Florida Coll Med Gainesville FL 32610 USA Hosp Univ Penn Dept Med Philadelphia PA 19104 USA Univ Penn Perelman Sch Med Dept Med Philadelphia PA 19104 USA Univ Penn Perelman Sch Med Dept Pathol & Lab Med Philadelphia PA 19104 USA Yale Sch Med Sect Biomed Informat & Data Sci New Haven CT 06510 USA Penn Med Leonard Davis Inst Hlth Econ Philadelphia PA 19104 USA Ctr Evidence Based Practice CEP Philadelphia PA 19104 USA Penn Inst Biomed Informat IBI Philadelphia PA 19104 USA

Objectives COVID-19, since its emergence in December 2019, has globally impacted research. Over 360 000 COVID-19-related manuscripts have been published on PubMed and preprint servers like medRxiv and bioRxiv, with preprints comprising about 15% of all manuscripts. Yet, the role and impact of preprints on COVID-19 research and evidence synthesis remain uncertain. Materials and Methods We propose a novel data-driven method for assigning weights to individual preprints in systematic reviews and meta-analyses. This weight termed the "confidence score" is obtained using the survival cure model, also known as the survival mixture model, which takes into account the time elapsed between posting and publication of a preprint, as well as metadata such as the number of first 2-week citations, sample size, and study type. Results Using 146 preprints on COVID-19 therapeutics posted from the beginning of the pandemic through April 30, 2021, we validated the confidence scores, showing an area under the curve of 0.95 (95% CI, 0.92-0.98). Through a use case on the effectiveness of hydroxychloroquine, we demonstrated how these scores can be incorporated practically into meta-analyses to properly weigh preprints. Discussion It is important to note that our method does not aim to replace existing measures of study quality but rather serves as a supplementary measure that overcomes some limitations of current approaches. Conclusion Our proposed confidence score has the potential to improve systematic reviews of evidence related to COVID-19 and other clinical conditions by providing a data-driven approach to including unpublished manuscripts.

关键词： evidence synthesis data-driven modeling preprint systematic review

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A data-driven Approach with Improved Generalization Performance to modeling Transient Behaviors of DC-DC Converters

A Data-driven Approach with Improved Generalization Performa...

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IEEE Asia Power and Energy Engineering Conference (APEEC)

作者： Hanchen Ge Zhongxi Ou Zhicong Huang Shien-Ming Wu School of Intelligent Engineering South China University of Technology Guangzhou China Zhuhai Power Supply Bureau Guangdong Power Grid Co. Ltd. Zhuhai China

Nowadays, the data-driven approaches to modeling power electronics (PE) systems are mostly based on sequential neural networks (NNs). These approaches may require too much data since the NNs can not generalize across a wide range of inputs. To address this issue, this paper proposes a new data-driven approach to modeling the transient behaviors of DC-DC converters, which is based on fully-connected NNs. The proposed method introduced prior knowledge about linear systems and thus significantly improved the generalization performance. In this method, circuit parameters are first mapped into linear system characteristics by fully-connected NNs, and then the outputs are calculated by the inputs and the system characteristics. Experiment results show that the entire circuit topology with configurable parameter settings and initial conditions can be successfully modeled. Parameter change events are also supported by this approach.

关键词： Linear systems Electrical engineering data-driven modeling Artificial neural networks DC-DC power converters Predictive models Power electronics

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data-driven interpretable machine learning for prediction of porosity and permeability of tight sandstone reservoir

ADVANCES IN GEO-ENERGY RESEARCH

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ADVANCES IN GEO-ENERGY RESEARCH 2025年第1期16卷 21-35页

作者： Cao, Liu Jiang, Fujie Chen, Zhangxing Gao, Yang Huo, Lina Chen, Di China Univ Petr Natl Key Lab Petr Resources & Engn Beijing 102249 Peoples R China China Univ Petr Coll Geosci Beijing 102249 Peoples R China Eastern Inst Technol Inst Digital Twins Ningbo 315200 Peoples R China Univ Calgary Schulich Sch Engn Chem & Petr Engn Calgary AB T2N 1N4 Canada PetroChina Res Inst Petr Explorat & Dev Beijing 100089 Peoples R China

Porosity and permeability are crucial indicators in the identification of high-quality reservoirs and favorable "sweet spot" zones, as well as key parameters when predicting and evaluating the development potential of fossil fuels like oil and gas. However, it is impracticable to collect enough core samples on vertical and horizontal planes for analysis due to the associated time and cost demand. Machine learning algorithms have shown remarkable capabilities in predicting the petrophysical properties by capturing non-linear relationships among logging data. In this study, to quantify the selection of logging curves and reduce the redundant logging data input, a novel and interpretable Permutation Importance-Set algorithm is proposed on the basis of logging data from the Upper Triassic Xujiahe Formation in the Sichuan Basin. The results indicate that, because of compaction, burial depth is the primary feature affecting the physical properties of tight sandstone reservoirs. Acoustic and spontaneous potential logs are critical for porosity, while density and spontaneous potential logs are pivotal for permeability, reflecting the complex diagenesis caused by the widespread sand-mud interbedding. Basin-level prediction models for porosity and permeability were developed using ten machine learning algorithms, then ablation studies confirmed the effectiveness of our feature selection and the reduced model complexity and over-fitting. This study offers a concise, interpretable prediction model with superior accuracy and interpretability for tight sandstone reservoirs.

关键词： data-driven modeling interpretable machine learning permutation importance-set reservoir characterization

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data-driven Traffic Simulation: A Comprehensive Review

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2024年第4期9卷 4730-4748页

作者： Chen, Di Zhu, Meixin Yang, Hao Wang, Xuesong Wang, Yinhai Hong Kong Univ Sci & Technol Guangzhou Syst Hub Guangzhou 511453 Peoples R China Hong Kong Univ Sci & Technol Civil & Environm Engn Dept Hong Kong Peoples R China Guangdong Prov Key Lab Integrated Commun Sensing & Hong Kong Peoples R China Johns Hopkins Univ Dept Civil & Syst Engn CaSE Baltimore MD 21218 USA Tongji Univ Sch Transportat Engn Shanghai 201804 Peoples R China Tongji Univ Key Lab Rd & Traff Engn Minist Educ Shanghai 201804 Peoples R China Univ Washington Dept Civil & Environm Engn Seattle WA 98195 USA

Autonomous vehicles (AVs) have the potential to significantly revolutionize society by providing a secure and efficient mode of transportation. Recent years have witnessed notable advancements in autonomous driving perception and prediction, but the challenge of validating the performance of AVs remains largely unresolved. data-driven microscopic traffic simulation has become an important tool for autonomous driving testing due to 1) availability of high-fidelity traffic data;2) its advantages of enabling large-scale testing and scenario reproducibility;and 3) its potential in reactive and realistic traffic simulation. However, a comprehensive review of this topic is currently lacking. This paper aims to fill this gap by summarizing relevant studies. The primary objective of this paper is to review current research efforts and provide a futuristic perspective that will benefit future developments in the field. It introduces the general issues of data-driven traffic simulation and outlines key concepts and terms. After overviewing traffic simulation, various datasets and evaluation metrics commonly used are reviewed. The paper then offers a comprehensive evaluation of imitation learning, reinforcement learning, deep generative and deep learning methods, summarizing each and analyzing their advantages and disadvantages in detail. Moreover, it evaluates the state-of-the-art, existing challenges, and future research directions.

关键词： Traffic control Behavioral sciences Reviews Roads Testing Autonomous vehicles Microscopy Traffic simulation autonomous driving data-driven modeling learning methods

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data-driven analysis of structural instabilities in electroactive polymer bilayers based on a variational saddle-point principle

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INTERNATIONAL JOURNAL OF SOLIDS AND STRUCTURES 2024年 291卷

作者： Sriram, Siddharth Polukhov, Elten Keip, Marc-Andre Univ Stuttgart Inst Appl Mech Dept Civil & Environm Engn Pfaffenwaldring 7 D-70569 Stuttgart Germany

In this work, we use a data-driven approach to model the onset of wrinkling in composite dielectric elastomer (DE) bilayer structures that are subjected to combined electro-mechanical loading conditions. Specifically, the critical surface-charge density required to activate wrinkling and the resulting number of half-waves are determined as functions of the tunable geometrical and material features of the DE bilayer using supervised machine learning (ML) models. The required data for the ML surrogates is generated using a finite-elementbased framework for structural stability analysis of the DE bilayer, which is rooted in a variational saddle-point formulation for non-linear electro-elastostatics. Within the considered broad design space for the DE bilayer, the data points are sampled according to a Latin-hypercube-type design, following which training and test datasets containing the values of the target variables for the sampled input-feature vectors are generated using the adopted finite-element framework. Subsequently, we implement and compare the capabilities of different ML models to capture accurately the non-linear dependence of the wrinkling characteristics on the tunable features of the DE bilayer.

关键词： Dielectric elastomers Structural instabilities Saddle-point formulation data-driven modeling Machine learning

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Review of data-driven Models in Wind Energy: Demonstration of Blade Twist Optimization Based on Aerodynamic Loads

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ENERGIES 2024年第16期17卷 3897页

作者： Roetzer, James Li, Xingjie Hall, John Univ North Carolina Charlotte William States Lee Coll Engn Mech Engn & Engn Sci Charlotte NC 28223 USA Univ North Carolina Charlotte Coll Sci Dept Math & Stat Charlotte NC 28223 USA

With the increasing use of data-driven modeling methods, new approaches to complex problems in the field of wind energy can be addressed. Topics reviewed through the literature include wake modeling, performance monitoring and controls applications, condition monitoring and fault detection, and other data-driven research. The literature shows the advantages of data-driven methods: a reduction in computational expense or complexity, particularly in the cases of wake modeling and controls, as well as various data-driven methodologies' aptitudes for predictive modeling and classification, as in the cases of fault detection and diagnosis. Significant work exists for fault detection, while less work is found for controls applications. A methodology for creating data-driven wind turbine models for arbitrary performance parameters is proposed. Results are presented utilizing the methodology to create wind turbine models relating active adaptive twist to steady-state rotor thrust as a performance parameter of interest. Resulting models are evaluated by comparing root-mean-square-error (RMSE) on both the training and validation datasets, with Gaussian process regression (GPR), deemed an accurate model for this application. The resulting model undergoes particle swarm optimization to determine the optimal aerostructure twist shape at a given wind speed with respect to the modeled performance parameter, aerodynamic thrust load. The optimization process shows an improvement of 3.15% in thrust loading for the 10 MW reference turbine, and 2.66% for the 15 MW reference turbine.

关键词： wind energy data-driven modeling wind turbine performance wake modeling gaussian process regression particle swarm optimization fault detection aerostructure twist reduced computational expense

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Air-Side Heat Transfer Performance Prediction for Microchannel Heat Exchangers Using data-driven Models with Dimensionless Numbers

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Frontiers in Heat and Mass Transfer 2024年第6期22卷 1613-1643页

作者： Long Huang Junjia Zou Baoqing Liu Zhijiang Jin Jinyuan Qian Institute of Process Equipment College of Energy EngineeringZhejiang UniversityHangzhou310027China Institute of Wenzhou Zhejiang UniversityWenzhou310027China School of Intelligent Manufacturing Ecosystem Xi’an Jiaotong-Liverpool UniversitySuzhou215123China

This study explores the effectiveness of machine learning models in predicting the air-side performance of microchannel heat *** data were generated by experimentally validated Computational Fluid Dynam-ics(CFD)simulations of air-to-water microchannel heat exchangers.A distinctive aspect of this research is the comparative analysis of four diverse machine learning algorithms:Artificial Neural Networks(ANN),Support Vector Machines(SVM),Random Forest(RF),and Gaussian Process Regression(GPR).These models are adeptly applied to predict air-side heat transfer performance with high precision,with ANN and GPR exhibiting notably superior ***,this research further delves into the influence of both geometric and operational parameters—including louvered angle,fin height,fin spacing,air inlet temperature,velocity,and tube temperature—on model ***,it innovatively incorporates dimensionless numbers such as aspect ratio,fin height-to-spacing ratio,Reynolds number,Nusselt number,normalized air inlet temperature,temperature difference,and louvered angle into the input *** strategic inclusion significantly refines the predictive capabilities of the models by establishing a robust analytical framework supported by the CFD-generated *** results show the enhanced prediction accuracy achieved by integrating dimensionless numbers,highlighting the effectiveness of data-driven approaches in precisely forecasting heat exchanger *** advancement is pivotal for the geometric optimization of heat exchangers,illustrating the considerable potential of integrating sophisticated modeling techniques with traditional engineering metrics.

关键词： Machine learning microchannel heat exchangers heat transfer data-driven modeling computational fluid dynamics

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A data-driven Approach to Predict Hourly Load Profiles From Time-of-Use Electricity Bills

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IEEE ACCESS 2023年 11卷 60501-60515页

作者： Lazzeroni, Paolo Lorenti, Gianmarco Repetto, Maurizio Politecn Torino Dipartimento Energia Galileo Ferraris I-10129 Turin Italy

The design of renewable-based and collective energy systems requires consumption data with fine temporal and spatial resolution. Despite the increasing deployment of smart meters, obtaining such data directly from measurements can still be challenging, particularly when attempting to gather historical data over a reasonable period for many end users. As a result, there is a need for models to simulate or predict these consumption data (e.g., hourly load profiles). Typically, these models rely on numerous specific and detailed observations, such as load type, household size for residential customers, or operating hours for commercial ones. However, gathering this level of detail becomes increasingly difficult as the number and diversity of end users increase. Therefore, this paper proposes a data-driven approach to predict hourly load profiles of heterogeneous end users using only their monthly time-of-use electricity bills as inputs. We create a training set using a limited number of hourly measurements from diverse categories of end users and, differently from other approaches aimed at classifying the end users, we develop a regression model to map monthly electricity bills to typical load profiles. Experimental results using one year of data from various end-user categories demonstrate an average normalized mean absolute error of approximately 26% for instantaneous consumption and less than 4% for time-of-use values. Comparative analysis with standard load profiles and a two-step data-driven approach based on classification reveals that our proposed method outperforms the others in terms of prediction accuracy and statistical metrics.

关键词： & nbsp Energy consumption electricity demand load modeling data-driven modeling nearest neighbor methods

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Notes on Input Design: From Multi-Sine Design to data-driven Procedures

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IEEE CONTROL SYSTEMS LETTERS 2024年 8卷 1943-1948页

作者： Gerencser, Laszlo Michaletzky, Gyorgy Bokor, Jozsef Polcz, Peter HUN REN Inst Comp Sci & Control Syst & Control Lab H-1518 Budapest Hungary Eotvos Lorand Univ Fac Sci H-1117 Budapest Hungary Pazmany Peter Catholic Univ Fac Informat Technol & Bion H-1088 Budapest Hungary

We show that a class of optimal input design problems have only discrete spectral measures as solutions. If we fix any finite set of possible frequencies then a randomized version of the resulting convex problem has a unique (sparse) solution with probability 1. We also propose a data-driven approach to optimal input design via virtual off-line estimators that coincide with the optimized PE estimator modulo a negligible error, both for open loop and closed loop systems.

关键词： Transfer functions Technological innovation Stochastic processes Electronic mail Closed loop systems System identification Noise data-driven modeling performance evaluation system identification

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