Data-driven Algorithm Research Articles

Examining heterogeneity in dementia using data-driven unsupervised clustering of cognitive profiles.

Dementia is characterized by a decline in memory and thinking that is significant enough to impair function in activities of daily living. Patients seen in dementia specialty clinics are highly heterogenous with a variety of different symptoms that progress at different rates. Recent research has focused on finding data-driven subtypes for revealing new insights into dementia's underlying heterogeneity, rather than assuming that the cohort is homogenous. However, current studies on dementia subtyping have the following limitations: (i) focusing on AD-related dementia only and not examining heterogeneity within dementia as a whole, (ii) using only cross-sectional baseline visit information for clustering and (iii) predominantly relying on expensive imaging biomarkers as features for clustering. In this study, we seek to overcome such limitations, using a data-driven unsupervised clustering algorithm named SillyPutty, in combination with hierarchical clustering on cognitive assessment scores to estimate subtypes within a real-world clinical dementia cohort. We use a longitudinal patient data set for our clustering analysis, instead of relying only on baseline visits, allowing us to explore the ongoing temporal relationship between subtypes and disease progression over time. Results showed that subtypes with very mild or mild dementia were more heterogenous in their cognitive profiles and risk of disease progression.

A Data-Driven Optimization Framework for Static Rebalancing Operations in Bike Sharing Systems

Bike sharing systems have been widely deployed in urban cities for first- and last-mile transportation. However, because of the geographical and temporal imbalance of bike demand, bikes need to be reallocated system-wide among stations during the night to maintain a high service level while minimizing demand loss due to stockout or overcapacity. Two technical challenges remain in optimizing the static bike rebalancing operations. One challenge is to accurately predict bike pickup and dropoff demand at each station, considering demand substitution effects and subsequently determining the optimal rebalancing quantity for each station. The other is to efficiently optimize the routing of multiple rebalancing vehicles for large-scale bike sharing systems, considering outlier stations with rebalancing quantities exceeding vehicle capacity. To this end, we propose an end-to-end solution to tackle the aforesaid challenges. Specifically, we first develop deep learning-based predictors that capture the time dependencies of station-level demand, the impact of weather conditions, and the demand substitution effect by nearby stations. Based on the demand rate, a sequential simulation-based demand loss estimator is developed to find the optimal rebalancing quantities that lead to the minimum expected demand loss. Then, a mixed integer linear programming model is formulated to optimize the routing problem of rebalancing vehicles. To address the computational challenge, we propose a data-driven decomposition algorithm to support a multivehicle multivisit rebalancing strategy by decomposing the multivehicle routing problem into smaller and tractable single-vehicle routing problems, which can be solved in parallel. Finally, extensive numerical experiments using real-world data from New York City Citi Bike demonstrate the accuracy of the proposed bike demand predictors, the impact of demand substitution, and the efficiency of the data-driven optimization framework. History: Accepted by Ram Ramesh, Area Editor for Data Science & Machine Learning. Funding: This work was supported by the National Natural Science Foundation of China [Grant 72201222] and the Hong Kong Research Grants Council [Grants CityU 21500220 and CityU 11504322]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0182 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0182 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Vonoprazan: A novel agent used in eradicating Helicobacter pylori infection.

Helicobacter pylori is the most common chronic bacterial infection worldwide. Because of the risk for chronic gastritis, peptic ulcer disease, and gastric malignancy, all patients with H. pylori should be treated. Unfortunately, eradication rates remain low, and barriers to successful treatment include inadequate acid suppression, locoregional antibiotic resistance, and patient nonadherence to complicated treatment regimens. Vonoprazan, a new medication for the treatment of H. pylori infections, provides more potent acid suppression and is simpler to dose than proton pump inhibitors. This article provides a data-driven algorithm for incorporating vonoprazan into the treatment of patients with H. pylori infections.

Machine Learning-Based Optimization Models for Defining Storage Rules in Maritime Container Yards

This paper proposes an integrated approach to define the best consignment strategy for storing containers in an export yard of a maritime terminal. The storage strategy identifies the rules for grouping homogeneous containers, which are defined simultaneously with the assignment of each group of containers to the available blocks (bay-locations) in the yard. Unlike recent literature, this study focuses specifically on weight classes and their respective limits when establishing the consignment strategy. Another novel aspect of this work is the integration of a data-driven algorithm and operations research. The integrated approach is based on unsupervised learning and optimization models and allows us to solve large instances within a few seconds. Results obtained by spectral clustering are treated as input datasets for the optimization models. Two different formulations are described and compared: the main difference lies in how containers are assigned to bay-locations, shifting from a time-consuming individual container assignment to the assignment of groups of containers, which offers significant advantages in computational efficiency. Experimental tests are organized into three campaigns to evaluate the following: (i) The computational time and solution quality (i.e., space utilization) of the proposed models; (ii) The performance of these models against a benchmark model; (iii) The practical effectiveness of the proposed solution approach.

Data-driven parameter identification of an equivalent mechanical model for large amplitude liquid sloshing

The accurate extraction of model parameters is vital to ensure that the equivalent mechanical model can precisely describe the dynamic behavior of large amplitude liquid sloshing. In this paper, the parameters of the moving pulsating ball model (MPBM), which is an equivalent mechanical model used to represent the large amplitude liquid sloshing, are extracted using a combination of computational fluid dynamics (CFD), experimental data, and data-driven algorithms. The arbitrary Lagrangian-Eulerian finite element method (ALE-FEM) is adopted to simulate three-dimension large amplitude liquid sloshing in the tank with high precision. The calculated results from the presented algorithm are compared with the experimental data to verify the reliability and validity. The typical parameters required by the MPBM mainly include equivalent sloshing mass, equivalent ball radius, and damping coefficient. These parameters are extracted by using a data-driven parameter optimization algorithm, which is based on the numerical calculation results of ALE-FEM. The accuracy of the MPBM with the equivalent model parameters extracted by using data-driven parameter optimization algorithm is investigated under two types of excitations: harmonic excitation and step excitation. The results show that the MPBM with equivalent model parameters extracted by a data-driven parameter optimization algorithm can precisely imitate the large amplitude liquid sloshing behavior and the method presented can provide significant reference for the overall design of spacecraft dynamics system.

A Generalized Deep Reinforcement Learning Model for Distribution Network Reconfiguration with Power Flow-Based Action-Space Sampling

Distribution network reconfiguration (DNR) is used by utilities to enhance power system performance in various ways, such as reducing line losses. Conventional DNR algorithms rely on accurate values of network parameters and lack scalability and optimality. To tackle these issues, a new data-driven algorithm based on reinforcement learning is developed for DNR in this paper. The proposed algorithm comprises two main parts. The first part, named action-space sampling, aims at analyzing the network structure, finding all feasible reconfiguration actions, and reducing the size of the action space to only the most optimal actions. In the second part, deep Q-learning (DQN) and dueling DQN methods are used to train an agent to take the best switching actions according to the switch states and loads of the system. The results show that both DQN and dueling DQN are effective in reducing system losses through grid reconfiguration. The proposed methods have faster execution time compared to the conventional methods and are more scalable.

Hybrid WARIMA-WANN algorithm for data prediction in bridge health monitoring system

Application of data-driven algorithm to model and predict the future trend of the structural health monitoring (SHM) data serves as important references for the future safety evaluation and decision-making of the bridges. An innovative hybrid WARIMA-WANN algorithm combining the Wavelet model, the autoregressive moving average (ARIMA) model and the artificial neural network (ANN) model is proposed to predict massive monitoring data in the bridge SHM system. The WPT serves as the decomposition function for the linear and nonlinear components of the hybrid model. The separated linear/nonlinear components are individually predicted with the ARIMA/ANN models and then combined to obtain the prediction values of the future data. The hybrid method is applied to predict the deflection values of a single-tower cable-stayed bridge. Multiple parameters in individual models are discussed and selected to increase the credibility of the prediction results for the example bridge. The results show that the proposed hybrid algorithm successfully captures the linear and nonlinear coupling relationships inside the SHM data from the frequency perspective of view and shows the best prediction performance when compared with the other four existing models. Different prediction steps obviously influence prediction performance due to the periodic nature of the bridge SHM data.

Data-Driven Model Predictive Control for Redundant Manipulators With Unknown Model.

The tracking control of redundant manipulators plays a crucial role in robotics research and generally requires accurate knowledge of models of redundant manipulators. When the model information of a redundant manipulator is unknown, the trajectory-tracking control with model-based methods may fail to complete a given task. To this end, this article proposes a data-driven neural dynamics-based model predictive control (NDMPC) algorithm, which consists of a model predictive control (MPC) scheme, a neural dynamics (ND) solver, and a discrete-time Jacobian matrix (DTJM) updating law. With the help of the DTJM updating law, the future output of the model-unknown redundant manipulator is predicted, and the MPC scheme for trajectory tracking is constructed. The ND solver is designed to solve the MPC scheme to generate control input driving the redundant manipulator. The convergence of the proposed data-driven NDMPC algorithm is proven via theoretical analyses, and its feasibility and superiority are demonstrated via simulations and experiments on a redundant manipulator. Under the drive of the proposed algorithm, the redundant manipulator successfully carries out the trajectory-tracking task without the need for its kinematics model.

Two-dimensional reinforcement learning model-free fault-tolerant control for batch processes against multi- faults

Aiming at the characteristics of batch process changing along with time and batch directions, the existence of unmodeled dynamics, and the partial failure of actuators or/and sensors, we propose a novel 2D reinforcement learning (RL) fault tolerant control strategy without considering model parameters. Firstly, a two-Dimensional (2D) augmented state space model and 2D Q function-based fault tolerant control (FTC) framework is established. The 2D Bellman equation can be acquired by analyzing the relationship between the 2D value function and the 2D Q function. Based on the extended model and Q-learning concept of RL, a data-driven FTTC independent of model parameters is designed, and a 2D data-driven Q-learning algorithm is proposed. Finally, taking the pressure holding phase in the injection process as the experimental object, the control effect is compared with that of the traditional model-based FTC, and better tracking performance and unbiasedness to the probing noise can be proved.

Modeling solvation dynamics of transition metal redox ion through on-the-fly multi-objective Bayesian-optimized force field.

Modeling solvation dynamics and properties is crucial for developing electrolytes for electrochemical energy storage and conversion devices. This work reports an on-the-fly multi-objective Bayesian optimization (OTF-MOBO) method to parameterize force fields for modeling ionic solvation structures, thermodynamics, and transport properties using molecular dynamics simulations. By leveraging solvation-free energy and solvation radii as training data, we employ the data-driven OTF-MOBO algorithm to actively optimize the force field parameters. The modeling accuracy was evaluated in molecular dynamics simulations until the Pareto front in the parameter space was reached through minimized prediction errors in both solvation-free energy and solvation radii. Using transition metal redox ions (Fe3+/Fe2+, Cr3+/Cr2+, and Cu2+/Cu+) in aqueous solution as examples, we demonstrate that simple force fields combining the Lenard-Jones potential and Coulombic potential can achieve relative error below 2% in both solvation free energy and solvation radii. The optimized force fields can be further extrapolated to predict solvation entropy and diffusivities with relative error below 10% compared with experiments.

Predicting indoor personalized heat stress using wearable sensors and data-driven models

Rising global temperatures pose heightened heat stress challenges, especially impacting vulnerable populations such as older adults in low-income urban areas of color, who disproportionately face energy inefficiencies and environmental burdens. Traditional research often overlooks individual metabolic rates and behaviors in heat stress assessments, relying instead on isolated environmental measures in laboratory settings. This paper presents a novel, occupant-centric approach that integrates wearable sensor data, building information, and air-conditioning usage to predict extreme heat events and assess their impact on occupants through calibrated thermal building simulations for seven older adults in Houston, Texas. We developed a personalized heat stress model based on existing heat index equations and dynamic predicted mean votes, enhanced by measured metabolic rates. A data-driven machine learning algorithm was then used to predict extreme heat events with this model serving as the ground truth. Our findings reveal that elevated activity levels significantly contribute to discomfort during extreme heat events, often undetected by the heat index equation—an environmental metric—alone. Furthermore, we observe that combining metabolic rates with measured indoor temperatures consistently predicts extreme heat events with over 95 % accuracy, demonstrating almost 13 % improvement compared to using indoor temperature in isolation. This study provides a valuable proof of concept that underscores the merits of integrating multiple data sources to raise the accuracy and predictability of indoor extreme heat events. Our research not only offers potential to enhance early warning systems and guide architectural decisions but also influences building material choices, significantly mitigating the adverse effects of extreme heat.

Flexible Modeling of Nonstationary Extremal Dependence using Spatially Fused LASSO and Ridge Penalties

Statistical modeling of a nonstationary spatial extremal dependence structure is challenging. Parametric max-stable processes (MSPs) are common choices for modeling spatially indexed block maxima, where an assumption of stationarity is usual to make inference feasible. However, this assumption is unrealistic for data observed over a large or complex domain. We develop a computationally efficient method for estimating extremal dependence using a globally nonstationary but locally stationary MSP construction, with the spatial domain divided into a fine grid of subregions, each with its own dependence parameters. We use LASSO ( L 1 ) or ridge ( L 2 ) penalties to obtain spatially smooth parameter estimates. We then develop a novel data-driven algorithm to merge homogeneous neighboring subregions. The algorithm facilitates model parsimony and interpretability. To make our model suitable for high-dimensional data, we exploit a pairwise likelihood to perform inference and discuss its computational and statistical efficiency. We apply our proposed method to model monthly maximum temperature data at over 1400 sites in Nepal and the surrounding Himalayan and sub-Himalayan regions; we show significant improvements in model fit compared to a stationary model. Furthermore, we demonstrate that the estimated merged partition is interpretable from a geographic perspective and leads to better model diagnostics by adequately reducing the number of parameters.

Comparative analysis of data-driven electric vehicle battery health models across different operating conditions

The work covers the development of a data-driven algorithm and computes the performance of learning models for lithium-ion battery state of health (SOH) estimation. A wide range of environmental and temperature conditions (15 °C, 25 °C, and 35 °C) at different charging and discharging rates of 1C and 2C are used for electric vehicle battery health estimation. The result of the tested data of cell ‘a’ is validated with a different set of cell ‘b’ on identical test parameters, and the results are tabulated and compared. At 25 °C, the mean absolute errors for the regression algorithms decision tree (DT), k-nearest neighbor (KNN), and random forest (RF) are 3.78641E-03, 3.62524E-03, and 6.16931E-03. The mean absolute percent error for regression algorithms DT, KNN, and RF is 1.48921E-03, 1.40631E-03, and 2.40260E-03. The root mean square error for regression algorithms DT, KNN, and RF is 1.26813E-02, 9.73320E-03, and 1.17238E-02, and the mean squared error for regression algorithms DT, KNN, and RF is 1.60816E-04, 9.47351E-05, and 1.37448E-04. The results show that the KNN and DT methods accurately estimate the SOH under diversified operating conditions in comparison with RF methods and can foster advanced battery health monitoring systems.

Data-driven optimal shrinkage of singular values under high-dimensional noise with separable covariance structure with application

We develop a data-driven optimal shrinkage algorithm, named extended OptShrink (eOptShrink), for matrix denoising with high-dimensional noise and a separable covariance structure. This noise is colored and dependent across samples. The algorithm leverages the asymptotic behavior of singular values and vectors of the noisy data's random matrix. Our theory includes the sticking property of non-outlier singular values, delocalization of weak signal singular vectors, and the spectral behavior of outlier singular values and vectors. We introduce three estimators: a novel rank estimator, an estimator for the spectral distribution of the pure noise matrix, and the optimal shrinker eOptShrink. Notably, eOptShrink does not require estimating the noise's separable covariance structure. We provide a theoretical guarantee for these estimators with a convergence rate. Through numerical simulations and comparisons with state-of-the-art optimal shrinkage algorithms, we demonstrate eOptShrink's application in extracting maternal and fetal electrocardiograms from single-channel trans-abdominal maternal electrocardiograms.

Object classification through heterogeneous fog with a fast data-driven algorithm using a low-cost single-photon avalanche diode array

This study presents a framework for classifying a wooden mannequin’s poses using a single-photon avalanche diode (SPAD) array in dynamic and heterogeneous fog conditions. The target and fog generator are situated within an enclosed fog chamber. Training datasets are continuously collected by configuring the temporal and spatial resolutions on the sensor's firmware, utilizing a low-cost SPAD array sensor priced below $5, consisting of an embedded SPAD array and diffused VCSEL laser. An extreme learning machine (ELM) is trained for rapid pose classification, as a benchmark against CNN. We quantitatively justify the selection of nodes in the hidden layer to balance the computing speed and accuracy. Results demonstrate that ELM can accurately classify mannequin poses when obscured by dynamic heavy fog to 35 cm away from the sensor, enabling real-time applications in consumer electronics. The proposed ELM achieves 90.65% and 89.58% accuracy in training and testing, respectively. Additionally, we demonstrate the robustness of both ELM and CNN as the fog density increases. Our study also discusses the sensor’s current optical limitations and lays the groundwork for future advancements in sensor technology.

Getting away from the cutoff in regression discontinuity designs

Regression discontinuity (RD) designs are highly popular in economic research because of their strong internal validity and straightforward intuition. While RD estimates are local in nature, several recent articles propose methods that generalize RD estimates to units outside a small neighborhood of the cutoff. In this article, I introduce the getaway package, which implements the method proposed by Angrist and Rokkanen (2015, Journal of the American Statistical Association 110: 1331-1344) to extrapolate treatment-effect estimates “away from the cutoff”, relying on a classical unconfoundedness condition. Additionally, the package features a data-driven algorithm designed to identify a set of covariates that fulfills the unconfoundedness assumption. It also incorporates a toolkit intended for testing and visualization purposes.

Data-driven insights into cavitation phenomena: From spatiotemporal features to physical state transitions

The application of data-driven methods to study cavitation flow provides insights into the underlying mechanisms and richer physical details of cavitation phenomena. This paper aims to analyze the physically interpretable multi-state cavitation behavior. Initially, the spatiotemporal features of the cavitation flow are represented as network trajectories using principal component analysis. The k-means++ algorithm is then employed to obtain coarse-grained flow field states, and the centroid of each cluster served as a representative for the attributes of that state. Subsequently, the Markov state model is constructed to capture the dynamic transitions in the cavitation flow field. Through a detailed analysis of the dynamic transition model, the cavitation flow field states with genuine physical mechanisms are refined. Finally, proper orthogonal decomposition (POD) is utilized to extract the flow patterns corresponding to different states. The distribution characteristics of the flow field modes in different states correspond to their physical properties. These data-driven algorithm enables a detailed analysis of the typical states in periodic cavitation processes, such as cavity growth, development, shedding, and collapse, providing a deeper understanding of the cavitation flow characteristics in different typical states.

An optimization framework for achieving optimal hydrocyclone's performance aligning with decision-makers' preferences

Previous hydrocyclone optimization often neglected interactions among key performance objectives, which limits hydrocyclone wide applications to meet increasingly diverse industry demands. This study proposes an optimization framework to identify the most suitable hydrocyclone design and operating conditions with conflicting key performance objectives. An advanced multi-objective evolutionary algorithm (PICEA-g) is employed to generate Pareto-optimal solutions that capture trade-offs among multiple conflicting objectives. A novel data-driven predictive algorithm, INFO-ELM, is introduced to establish nonlinear relationships between key variables and performance objectives, thereby accelerating the search for Pareto-optimal solutions by PICEA-g. Furthermore, a multi-criteria decision-making method (TOPSIS) is utilized to determine the optimal solution based on decision-makers' preferences, ensuring consistency between preference information and decision outcomes. The framework's effectiveness is validated across various separation scenarios using two decision-making strategies. This study offers a comprehensive approach to address trade-offs in hydrocyclone optimization, widening its application in diverse separation scenarios.

Solving optimal predictor-feedback control using approximate dynamic programming

This paper is concerned with approximately solving the optimal predictor-feedback control problem of multiplicative-noise systems with input delay in infinite horizon. The optimal predictor-feedback control, provided by the analytical method, is determined by Riccati–ZXL equations and is hard to obtain in the case of unknown system dynamics. We aim to propose a policy iteration (PI) algorithm for solving the optimal solution by approximate dynamic programming. For convergence analysis of the algorithm, we first develop a necessary and sufficient stabilizing condition, in the form of several new Lyapunov-type equations, which parameterizes all predictor-feedback controllers and can be seen as an important addition to Lyapunov stability theory. We then propose an iterative scheme for the Riccati–ZXL equations computations, along with convergence analysis, based on the condition. Inspired by this scheme, a data-driven online PI algorithm, convergence implied in that of the iterative scheme, is proposed for the optimal predictor-feedback control problem without full system dynamics. Finally, a numerical example is used to evaluate the proposed PI algorithm.

2575: Title: Evaluation of data-driven motion algorithm OncoFreeze AI in a Siemens Biograph PET-CT

2575: Title: Evaluation of data-driven motion algorithm OncoFreeze AI in a Siemens Biograph PET-CT