Inaccurate Model Research Articles

Cryo2StructData: A Large Labeled Cryo-EM Density Map Dataset for AI-based Modeling of Protein Structures

The advent of single-particle cryo-electron microscopy (cryo-EM) has brought forth a new era of structural biology, enabling the routine determination of large biological molecules and their complexes at atomic resolution. The high-resolution structures of biological macromolecules and their complexes significantly expedite biomedical research and drug discovery. However, automatically and accurately building atomic models from high-resolution cryo-EM density maps is still time-consuming and challenging when template-based models are unavailable. Artificial intelligence (AI) methods such as deep learning trained on limited amount of labeled cryo-EM density maps generate inaccurate atomic models. To address this issue, we created a dataset called Cryo2StructData consisting of 7,600 preprocessed cryo-EM density maps whose voxels are labelled according to their corresponding known atomic structures for training and testing AI methods to build atomic models from cryo-EM density maps. Cryo2StructData is larger than existing, publicly available datasets for training AI methods to build atomic protein structures from cryo-EM density maps. We trained and tested deep learning models on Cryo2StructData to validate its quality showing that it is ready for being used to train and test AI methods for building atomic models.

Measuring source properties and quasinormal mode frequencies of heavy massive black-hole binaries with LISA

The Laser Interferometer Space Antenna (LISA) will be launched in the mid-2030s. It promises to observe the coalescence of massive black-hole (BH) binaries with signal-to-noise ratios (SNRs) reaching thousands. Crucially, it will detect some of these binaries with high SNR in both the inspiral and the merger-ringdown stages. Such signals are ideal for tests of general relativity (GR) using information from the whole waveform. Here, we consider astrophysically motivated binary systems at the high-mass end of the population observable by LISA and simulate their signals using the newly developed multipolar effective-one-body model: pSEOBNRv5HM. The merger-ringdown signal in this model depends on the binary properties (masses and spins) and also on parameters that describe fractional deviations from the GR quasinormal mode (complex) frequencies of the remnant BH. Performing full Bayesian analyses, we assess to which accuracy LISA will be able to constrain deviations from GR in the ringdown signal when using information from the whole signal. We find that these deviations can typically be constrained to within 10% and in the best cases to within 1%. We also show that with this model we can measure the binary masses and spins with great accuracy even for very massive BH systems with low SNR in the inspiral. In particular, individual source-frame masses can typically be constrained to within 10% and as precisely as 1%, and individual spins can typically be constrained to within 0.1 and, in the best cases, to within 0.001. We also probe the accuracy of the SEOBNRv5HM waveform family by performing synthetic injections of GR numerical-relativity waveforms. Using a novel method that we develop here to quantify the impact of systematic errors, we show that, already for sources with SNR O(100), we would measure erroneous deviations from GR due to waveform model inaccuracies. One of the main sources of error is the mismodeling of the relative alignment between harmonics. These results confirm the need for improving waveform models to perform tests of GR with binary BHs observed at high SNR by LISA. Published by the American Physical Society 2024

Acceleration model considering multi‐stress coupling effect and reliability modeling method based on nonlinear Wiener process

AbstractEstablishing an accurate accelerated degradation model is paramount for ensuring precise reliability evaluation results. Unfortunately, current accelerated degradation tests often lack test groups for investigating multi‐stress coupled phenomena. Consequently, existing multi‐stress accelerated models fail to adequately consider the impact of stress coupling when data with stress coupling information is absent. This limitation leads to the development of inaccurate models, ultimately affecting the precision of reliability assessment. To address this challenge, this paper introduces a new modeling method for multi‐stress accelerated degradation models that takes into account stress coupling effects. The proposed modeling method aims to improve the accuracy of reliability assessment under multi‐stress conditions. In the proposed model, the main effect function of stress is determined based on existing single‐stress accelerated models. The coupling effect is first examined through the Multivariate Analysis of Variance (MANOVA), and then the functional form of the coupling effect function is determined from the given candidate functions through correlation analysis. Next, the coupling effect is incorporated into a Wiener process to establish a multi‐stress accelerated degradation model, and the two‐step estimation method combining Least Squares Method (LSM) and Differential Evolution Algorithm (DEA) is proposed. The accuracy and effectiveness of the coupling effect test method, model establishment, and parameter estimation method were validated using two Monte Carlo simulation experimental data sets. Finally, the superiority of the proposed model is demonstrated through examples, providing feasible ideas and technical support for the research on multi‐stress accelerated degradation modeling considering stress coupling.

Spatial modelling and classification of altered coal using random forest-based methods at Moatize Basin, Mozambique

Coal basins affected by intrusion pose distinctive challenges, such as the complexity of determining the extent of impacted coal from borehole cuttings and the potential for inaccuracies in the spatial modelling of coal quality parameters. It is more challenging in complex thinly bedded coal seams, characterised by multiple sub-layers of varying thickness and coal quality. This study employs machine learning techniques in the highly complex thinly bedded Permian-aged Chipanga coal seam in the Moatize Basin, Mozambique. The study aims to assess the efficacy of these techniques for the spatial modelling and classification of altered coals. The results demonstrate that the random forest-based classifier can discriminate between altered and fresh coal with a range of accuracy of 97%–100%. Furthermore, among the five spatial methods assessed, geographical quantile regression forest emerges as one of the top-performing approaches for assessing the impact of magmatic intrusions on coal properties within the Moatize Basin. Although previous studies have shown the ability of these methods for spatial modelling and coal classification on single coal seam, these techniques have not been used in complex thin-bedded coal seams. Therefore, the findings of this study highlight the implications of these methods in navigating the complexities of thin-bedded coal seams.

Quantitative Elastohydrodynamic Lubrication—Seventeen Years In

Abstract Seventeen years have passed since the first full elastohydrodynamic lubrication (EHL) simulation employed the real pressure and shear dependence of viscosity measured in viscometers to accurately predict film thickness and friction. This is the appropriate time to enumerate the advances in understanding brought on by the application of high-pressure rheology to the EHL problem. The pressure dependence of the low-shear viscosity, which has been measured in viscometers for nearly a century, differs from the narratives taught to tribology students and often used to justify inaccurate models. The central film thickness often depends on the shear-thinning at low pressure and time–temperature–pressure superposition demands that the same shear dependence be active at the high pressure where friction is generated. In this article, some of the revelations resulting from quantitative EHL are reviewed. For example, it has been discovered that the minimum film thickness in point contacts depends upon the viscosity at the highest pressures of the contact. This explains the errors in the classical formulas, which were based upon the fictional narratives concerning piezoviscous response, and the assumption of film thickness governed by inlet conditions. Quantitative EHL provides quantitative predictions of contact behavior.

Detection of Outliers in Circular Regression Model Via DFBETAcIS Statistic

The outlier issues in circular regression models have recently received much attention. The presence of outliers may cause the sign and magnitude of regression coefficients to vary, resulting in inaccurate model development and incorrect prediction. Many methods for detecting outliers in a circular regression model have been proposed in previous studies such as COVRATIO, D, M, A, and Chord statistics, but it is suspected that they are not very successful in the presence of multiple outliers in a data set since the masking and swamping is not considered in their studies. This study aimed to develop an outlier detection procedure using DFBETAc statistic for circular cases, where this new statistic will investigate and identify multiple outliers in the Jammalamadaka and Sarma circular regression model (JSCRM) by considering masking and swamping effect. Monte Carlo simulations are used to determine the corresponding cut-off point and the power of performance is investigated. The performance of the proposed statistic is evaluated by the proportion of detected outliers and the rate of masking and swamping. The simulation procedure is applied at 10% and 20% contamination levels for varying sample sizes. The results show that the proposed DFBETAcIS statistic for JSCRM successfully detect the outliers. For illustration purposes, this process is applied to wind direction data.

Lifecycle carbon footprints of buildings and sustainability pathways in China

The Paris Agreement, with a 2 °C temperature increase baseline and a 1.5 °C target temperature increase, imposes challenges to the sustainability of buildings. However, as an end-user or end-of-the-art, the building sector overlaps with other sectors, such as industry and transportation in building materials manufacturing (such as steel, concrete, cement, etc. ) and electricity use (such as building-to-vehicle charging), imposing difficulties in lifecycle carbon footprint quantification in buildings. In this study, the lifecycle carbon footprint of buildings and sustainability pathways in China are provided. Tools and platforms for lifecycle carbon footprint quantification in buildings are reviewed. A global database for lifecycle carbon footprint quantification in buildings is reviewed and compared, together with an analysis of the advantages and disadvantages. Furthermore, decarbonization technologies in the building operation stage are comprehensively reviewed, including the decarbonization potential, technology readiness level (TRL), techno-economic performance, and current technical status. Pathways on carbon neutrality in building sectors in China are provided. The results indicate that inconsistencies in global databases, unclear definitions of lifecycle carbon emissions, and inaccurate models of building energy consumption are the main challenges for accurate lifecycle carbon analysis in buildings. Various carbon neutrality transformation technologies can be classified into ultralow energy building and near zero-energy building technologies and into efficient heat pump and smart metering technologies. Pathways for low-carbon transition in building sectors include building energy saving (330 million tCO2 with 22%), renewable energy supply (299 million tCO2 with 20%), building electrification and power sector decarbonization (450 million tCO2 with 30%), and carbon capture, utilization and storage (CCUS) technology (420 million tCO2 with 28%). These research results can pave the way for upcoming studies on the lifecycle carbon footprint in buildings and sustainability pathways in China.

System Identification and Dynamic Analysis of the Propulsion Shaft Systems Using Response Surface Optimization Technique

Marine vessels rely heavily on propeller shaft systems to adjust the engine torque and propeller thrust. However, these systems are subjected to various dynamic excitations during operation, such as transverse, longitudinal, and torsional excitations. These excitations can arise from factors like non-uniform stern flow fields, misaligned components, and the whirling motion of the shafts, which can affect the integrity and reliability of the vehicle. To analyze the dynamic response of the propulsion shaft system and ensure its reliability, numerical/analytical models are currently in practice. The finite element method (FEM) is a popular choice, but uncertainties in bearings and connectors stiffness lead to inaccuracies in the Finite Element model, resulting in significant differences between the experimental and theoretical models. This paper proposes the response surface optimization (RSO) technique to estimate unknown bearing stiffness in the propulsion shaft system. The experimental model of the propeller shaft system is constructed using steady-state response with step sine excitation. The RSO technique is then used to update the natural frequencies and vibration amplitude of the FE (Finite Element) model. The updated model shows less than a 10% difference in natural frequencies and vibration amplitude compared to the experimental model, demonstrating that the proposed technique is an efficient tool for marine shaft dynamic analysis.

Hierarchical-stochastic model predictive control for a grid-interactive multi-zone residential building with distributed energy resources

Using Model Predictive Control (MPC) is a promising method for enabling grid-interactive efficient buildings. Since MPC relies on a building model and the forecasts of external disturbances to derive optimal inputs, the uncertainties due to forecast errors and model inaccuracies can deteriorate the control performance. Most existing MPC studies for the built environment are deterministic MPC without consideration of uncertainties even though several different methods (e.g., stochastic MPC) are available in the literature to deal with them. Even studies that consider forecast uncertainties often neglect model inaccuracies. Hence, in the present paper, a novel hierarchical-stochastic MPC is proposed considering forecast uncertainties and model inaccuracies, and its performance is compared with deterministic, stochastic, and hierarchical MPC for power management in a residential building with distributed energy resources. The control objective is the cost-optimal scheduling of a heat pump, a battery for energy storage, and a rooftop photovoltaic system. Measurement data is used to identify the building model. The results for one-week simulation in winter show that (1) the deterministic MPC results in an unacceptable level of temperature constraint violations in two out of five rooms; (2) the hierarchical MPC can reduce the temperature constraint violations to an acceptable level at the expense of increased cost; (3) the stochastic MPC achieves the same reduction in temperature constraint violations as the hierarchical MPC but at slightly lower costs; and (4) the new proposed hierarchical-stochastic MPC results in both lower temperature constraint violations and lower financial expenses than the use of stochastic or hierarchical MPC individually.

Full waveform inversion and Lagrange multipliers

SUMMARY Full-waveform inversion (FWI) is an effective method for imaging subsurface properties using sparsely recorded data. It involves solving a wave propagation problem to estimate model parameters that accurately reproduce the data. Recent trends in FWI have seen a renewed interest in extended methodologies, among which source extension methods leveraging reconstructed wavefields to solve penalty or augmented Lagrangian (AL) formulations have emerged as robust algorithms, even for inaccurate initial models. Despite their demonstrated robustness on synthetic data, challenges remain, such as the lack of a clear physical interpretation and reliance on difficult-to-compute least-squares (LS) wavefields. Moreover, the literature lacks a general and through comparison of these source extension methods with each other and with the standard FWI. This paper is divided into three critical parts. In the first, a novel formulation of these methods is explored within a unified Lagrangian framework. This novel perspective permits the introduction of alternative algorithms that use LS multipliers instead of wavefields. These multiplier-oriented variants appear as regularizations of the standard FWI, are suitable to the time domain, offer tangible physical interpretations, and foster enhanced convergence efficiency. The second part of the paper delves into understanding the underlying mechanisms of these techniques. This is achieved by solving the associated non-linear equations using iterative linearization and inverse scattering methods. The paper provides insight into the role and significance of Lagrange multipliers in enhancing the linearization of the equations. It explains how different methods estimate multipliers or make approximations to increase computing efficiency. Additionally, it presents a new physical understanding of the Lagrange multiplier used in the AL method, highlighting how important it is for improving algorithm performance when compared to penalty methods. In the final section, the paper presents numerical examples that compare different methods within a unified iterative algorithm, utilizing benchmark Marmousi and 2004 BP salt models.

Improved Jacobian-Torsor model for optimizing mechanical product quality

The quality of an assembly largely depends on the predictive accuracy of the assembly deviation model in the selective assembly process. Presently, three-dimensional deviation analysis methods only offer tolerances that indicate the overall deviation of a part feature. This limitation fails to accurately represent the fit of the parts during the assembly process, resulting in initial inaccuracies in the selective assembly model. To address this issue, an improved Jacobian-Torsor model is proposed that incorporates measured data from part assembly features into the deviation analysis model. Initially, the actual contact state of the assembly is determined by measuring the contact surface of the plane fit and the shaft-hole fit. Next, the Jacobian matrix, representing the transmission path of assembly deviation, is constructed by analyzing the assembly connection diagram. Subsequently, by integrating the actual contact state characterized by small displacement torsor (SDT) into the unified Jacobian-Torsor model, the advantage of high credibility tolerance propagation is provided. Finally, the feasibility of the proposed improved Jacobian-Torsor model is verified using the double-joint manipulator.

Multi-stage Multi-energy Flow Integrated Energy Systems of Electricity, Gas, and Heat Based on Heterogeneous Energy Flow Characteristics

INTRODUCTION: The development of integrated energy systems (IES) is of paramount significance in addressing climate change and other challenges. Ensuring the rapid and accurate calculation of energy flow states is crucial for their efficient operation. However, the difference in response time of various heterogeneous energy flows in IES will lead to the inaccuracy of the steady-state model.
 OBJECTIVES: This paper proposes a model for multi-stage multi-energy flow IES of electricity, gas, and heat based on heterogeneous energy flow characteristics.
 Methods: IES was divided into fast variable networks and slow variable networks, and a multi-energy flow multi-stage model was established. Suitable models were matched for different subnets at different stages to improve the calculation accuracy.
 RESULTS: Selected a practical Electrical-Gas-Heat IES as a case study for simulation. Through case studies, the effectiveness and accuracy of the proposed method are demonstrated.
 CONCLUSION: The multi-stage model proposed in this paper can improve the accuracy of multi-energy flow in IES.

Convex model-based regularization method for force reconstruction

In the process of reconstructing structural forces, the influence of measurement errors and inherent model inaccuracies cannot be ignored. These errors exhibit a degree of correlation, and the presence of such correlation inevitably affects the quantification of uncertainties in force reconstruction. Objectively, the inherent ill-posed nature of structural inverse problems makes it difficult to obtain the forces to be identified, subject to uncertainties and highly susceptible to perturbations. Consequently, this paper introduces a force reconstruction regularization approach that explicitly considers the correlation of uncertainty parameters based on the truncated singular value regularization method. The primary objective is to refine the influence of uncertainties on the reconstructed force bounds with greater precision. When determining robust regularization parameters, the generalized cross-validation method and convex modelling approach are introduced to consider the uncertainty and its correlation in solving inverse problems. The proposed approach is rigorously validated through a comprehensive numerical case study. Error indexes and dispersion indices are employed to analyze the impact of different levels of noise and correlation on force reconstruction results. The force bounds obtained using the proposed method are compared with Monte Carlo simulation results. Finally, the validity of the proposed method is verified by an experiment with a four-story shear frame.

Addressing equifinality in agent-based modeling: a sequential parameter space search method based on sensitivity analysis

This study addresses the challenge of equifinality in agent-based modeling (ABM) by introducing a novel sequential calibration approach. Equifinality arises when multiple models equally fit observed data, risking the selection of an inaccurate model. In the context of ABM, such a situation might arise due to limitations in data, such as aggregating observations into coarse spatial units. It can lead to situations where successfully calibrated model parameters may still result in reliability issues due to uncertainties in accurately calibrating the inner mechanisms. To tackle this, we propose a method that sequentially calibrates model parameters using diverse outcomes from multiple datasets. The method aims to identify optimal parameter combinations while mitigating computational intensity. We validate our approach through indoor pedestrian movement simulation, utilizing three distinct outcomes: (1) the count of grid cells crossed by individuals, (2) the number of people in each grid cell over time (fine grid) and (3) the number of people in each grid cell over time (coarse grid). As a result, the optimal calibrated parameter combinations were selected based on high test accuracy to avoid overfitting. This method addresses equifinality while reducing computational intensity of parameter calibration for spatially explicit models, as well as ABM in general.

A Robust Switching Control Strategy for Three-Phase Voltage Source Converters with Uncertain Circuit Parameters

This study proposes a novel double closed-loop robust control strategy based on a power switching affine model of three-phase voltage source converters (VSCs). The aim is to overcome the challenges posed by inaccurate mathematical models, complex controller configurations, indirect switching control, and performance degradation under circuit parameters uncertainty or load variation in conventional methods. These conventional methods rely on linearization models, duty ratio regulation, and pulse width modulation (PWM) technologies. The contributions of work are the following: (1) A two-dimensional (2D) power switching affine model is constructed without any approximation or averaging. (2) The proposed approach achieves direct switching control of three-phase VSCs, eliminating the need for complex rotation coordinate transformation, PWM, and phase locking loop (PLL), which are utilized in traditional control methods. (3) The rigor of the system stability analysis is enhanced based on the 2D power switching model compared to the existing three-dimensional (3D) current switching model. (4) A simple control structure with only two control parameters is employed to address circuit parameter uncertainties. The effectiveness and superiority of the proposed method is validated through simulation and experimental comparison results.

Sensitivity of Cosmological Parameter Estimation to Nonlinear Prescription from Cosmic Shear

Several ongoing and upcoming large-scale structure surveys aim to explore the nonlinear regime of structure formation with high precision. Making reliable cosmological inferences from these observations necessitates precise theoretical modeling of the mildly nonlinear regime. In this work we explore how the choice of nonlinear prescription would impact parameter estimation from cosmic shear measurements for a Euclid-like survey. Specifically, we employ two different nonlinear prescriptions of halofit and the Effective Field Theory of the Large Scale Structure and compare their measurements for the three different cosmological scenarios of ΛCDM, wCDM, and (w 0, w a ) CDM. We also investigate the impact of different nonlinear cutoff schemes on parameter estimation. We find that the predicted errors on most parameters shrink considerably as smaller scales are included in the analysis, with the amount depending on the nonlinear prescription and the cutoff scheme used. We use predictions from the halofit model to analyze the mock data from DarkSky N-body simulations and quantify the parameter bias introduced in the measurements due to the choice of nonlinear prescription. We observe that σ 8 and n s have the largest measurement bias induced by inaccuracies of the halofit model.

Analysis of blade noise with different root boundary conditions considering fluid-structure interaction effect

It is of certain importance for noise reduction to investigate the relationship between the aeroacoustic characteristics of blade and root boundary conditions when considering the fluid-structure interaction (FSI) effect. In this study, a set of precise dynamic models and two-way FSI calculation methods are developed to predict the dynamic load and response of blades more accurately. The natural frequency inaccuracy of the blade model is kept within 5% by comparing the results of the modal test using the ploy-reference time domain method and adopting the sensitivity analysis method with the blade beam stiffness as the modification parameter. Besides, based on the numerical results from the computational fluid dynamics method considering the FSI effect, the aeroacoustic characteristics of blades with different root boundary conditions are analyzed by adopting the Ffowcs Williams-Hawkings equations. The aeroacoustic characteristics of the NACA blade in hover are thoroughly discussed, with emphasis on the impact of different root boundary conditions on rotation noise. The result demonstrates that, the sound pressure level (SPL) difference of loading noise can reach 1.4 dB between blades with various root boundary conditions, while the SPL difference of thickness is only 0.4 dB.

Ensemble learning based hierarchical surrogate model for multi-fidelity information fusion

Recently, multi-fidelity information fusion based surrogate modeling methods have made great progress in the engineering design and optimization tasks. Two main issues in this field are: (1) Since the overarching trend of the responses from the high-fidelity (HF) model is captured by the low-fidelity (LF) model, inaccurate LF model may negatively impact the final modeling results. (2) The responsiveness of multi-fidelity surrogate (MFS) models to variations in the relationship between LF and HF models leads to limited prediction performance. To this end, we propose an ensemble learning based MFS modeling method with a hierarchical framework, called EL-MFS. Specifically, to alleviate the impact of issue (1), we present an adaptive ensemble surrogate model, which aims to effectively mitigate the negative impact of inappropriate LF model selection on the HF approximation results. Furthermore, we propose to exploit the feature mapping and hierarchical framework to boost the versatility of the model as a way to mitigate the dependence of MFS performance on the relationship between HF and LF models. To assess the effectiveness of the proposed model, a sequence of numerical problems is tested, and some advanced surrogates are selected as the baseline models. Moreover, to demonstrate the potential of the proposed model in aiding intricate engineering design, one engineering case is investigated as well.

Digital Twinning of Cardiac Electrophysiology Models From the Surface ECG: A Geodesic Backpropagation Approach.

The eikonal equation has become an indispensable tool for modeling cardiac electrical activation accurately and efficiently. In principle, by matching clinically recorded and eikonal-based electrocardiograms (ECGs), it is possible to build patient-specific models of cardiac electrophysiology in a purely non-invasive manner. Nonetheless, the fitting procedure remains a challenging task. The present study introduces a novel method, Geodesic-BP, to solve the inverse eikonal problem. Geodesic-BP is well-suited for GPU-accelerated machine learning frameworks, allowing us to optimize the parameters of the eikonal equation to reproduce a given ECG. We show that Geodesic-BP can reconstruct a simulated cardiac activation with high accuracy in a synthetic test case, even in the presence of modeling inaccuracies. Furthermore, we apply our algorithm to a publicly available dataset of a biventricular rabbit model, with promising results. Given the future shift towards personalized medicine, Geodesic-BP has the potential to help in future functionalizations of cardiac models meeting clinical time constraints while maintaining the physiological accuracy of state-of-the-art cardiac models.

Improved Position Sensorless Resonant Frequency Tracking Control Method for Linear Oscillatory Machine

To improve the reliability of control system and obtain the maximum output efficiency, it is very necessary to carry out the position sensorless piston stroke control and the resonance frequency tracking control for linear oscillatory machine (LOM) at the same time. The conventional resonant frequency tracking control method based on model reference adaptive system (MRAS) has the problem of low estimation accuracy caused by inaccurate model parameters. In order to obtain more accurate resonant frequency, an improved position sensorless resonant frequency tracking control method is proposed in this article. In this method, the adjustable model is constructed based on the mechanical dynamics equation of LOM to track the system resonant frequency. A hybrid terminal sliding-mode observer (HTSMO) is designed to replace the conventional reference model, which can obtain more accurate velocity reference signal for adjustable model by introducing the current error feedback link, thereby further improving the resonant frequency tracking accuracy. Moreover, a second-order generalized integrator (SOGI) is introduced to filter the estimated velocity signal of HTSMO. The filtered velocity signal is not only used for the adjustable model to track the resonant frequency, but also used to obtain the smooth piston stroke signal for the stroke closed-loop control. Comprehensive simulation and experimental results have proved the effectiveness and the superiority of the proposed method.