Imperfect Model Research Articles

Editorial: Modeling and experimentation of imperfections in materials

Editorial: Modeling and experimentation of imperfections in materials

Modeling of Solar Radiation Pressure for BDS-3 MEO Satellites with Inter-Satellite Link Measurements

As the largest non-gravitational force, solar radiation pressure (SRP) causes significant errors in precise orbit determination (POD) of the BeiDou global navigation satellite system (BDS-3) medium Earth orbit (MEO) satellite. This is mainly due to the imperfect modeling of the satellite’s cuboid body. Since the BDS-3’s inter-satellite link (ISL) can enhance the orbit estimation of BDS-3 satellites, the aim of this study is to establish an a priori SRP model for the satellite body using 281-day ISL observations to reduce the systematic errors in the final orbits. The adjustable box wind (ABW) model is employed to refine the optical parameters for the satellite buses. The self-shadow effect caused by the search and rescue (SAR) antenna is considered. Satellite laser ranging (SLR), day-boundary discontinuity (DBD), and overlapping Allan deviation (OADEV) are utilized as indicators to assess the performance of the a priori model. With the a priori model developed by both ISL and ground observation, the slopes of SLR residual for the China Academy of Space Technology (CAST) and Shanghai Engineering Center for Microsatellites (SECM) satellites decrease from −0.097 cm/deg and 0.067 cm/deg to −0.004 cm/deg and −0.009 cm/deg, respectively. The standard deviation decreased by 21.8% and 26.6%, respectively. There are slight enhancements in the average values of DBD and OADEV, and a reduced β-dependent variation is observed in the OADEV of the corresponding clock offset. We also found that considering the SAR antenna only slightly improves the orbit accuracy. These results demonstrate that an a priori model established for the BDS-3 MEO satellite body can reduce the systematic errors in orbits, and the parameters estimated using both ISL and ground observation are superior to those estimated using only ground observation.

Combining Temperature and Precipitation to Constrain the Aerosol Contribution to Observed Climate Change

Abstract Using the past to improve future predictions requires an understanding and quantification of the individual climate contributions to the observed climate change by aerosols and greenhouse gases (GHGs), which is hindered by large uncertainties in aerosol forcings and responses across climate models. To estimate historical aerosol responses, we apply detection and attribution methods to attribute a joint change in temperature and precipitation to forcings by combining signals of observed changes in tropical wet and dry regions, the interhemispheric temperature asymmetry, global mean temperature (GMT), and global mean land precipitation (GMLP). Fingerprints representing the climate response to aerosols (AERs) and the remaining external forcings (noAER; mostly GHG) are derived from large ensembles of historical single- and ALL-forcing simulations from three models in phase 6 of the Coupled Model Intercomparison Project and selected using a perfect model study. Results from an imperfect model study and a hydrological sensitivity analysis support combining our choice of temperature and precipitation fingerprints into a joint study. We find that diagnostics including temperature and precipitation slightly better constrain the noAER signal than diagnostics based purely on temperature or GMT-only and allow for the attribution of AER cooling (even when GMT is not included in the fingerprint). These results are robust across fingerprints from different climate models. Estimated contributions for AER and noAER agree with other published estimates including those from the most recent IPCC report. Finally, we attribute the best estimate of 0.46 K ([−0.86, −0.05] K) of aerosol-induced cooling and 1.63 K ([1.26, 2.00] K) of noAER warming in 2010–19 relative to 1850–1900 using the combined signals of GMT and GMLP. Significance Statement Aerosols are small liquid or solid airborne particles. They are predominantly the secondary result of emissions of aerosol precursor gases emitted via industrial or natural processes. While greenhouse gases warm the climate, aerosols can have a cooling effect on the climate system, thus offsetting some of the greenhouse gas–related warming. We expect greenhouse gas concentrations in the atmosphere to continue to increase, while aerosol concentrations are likely going to decline due to their impacts on human health. Our study uses observed temperature and precipitation changes to quantify how much aerosols have offset warming from past greenhouse gas emissions. This can help constrain future predictions of global warming.

Selective maintenance of the complex system considering maintenance time uncertainty for system components with multiple repairpersons

AbstractThis research presents an innovative selected maintenance model for complex systems that considers the uncertainty in maintenance time (MT) for system components with multiple repairpersons. The computational model of uncertain MT for system components is established. An imperfect maintenance model is introduced, which has many imperfect maintenance levels not only considering do nothing, minimal repair, and replacement but also considering multiple intermediate maintenance levels. Furthermore, the system components maintenance assignment algorithm with multiple repairpersons is proposed to addresses the problem of how to assign multiple maintenance tasks to multiple repairpersons in order to minimize system MT. And it is innovatively integrated into the particle swarm optimization (PSO) to solve the proposed selective maintenance model, which enables heuristic algorithm to efficiently assign the multiple maintenance tasks with multiple repairpersons. The effectiveness and advantages of the proposed model and algorithm are verified by numerical experiments.

A novel TD3 for solving multi-level imperfect maintenance optimization problem

Abstract As we all know, multi-level imperfect maintenance strategy is usually more effective than single-level maintenance strategy for the actual production machines. At the same time, in the previous multi-level maintenance strategies, the majority of maintenance models only consider the constant maintenance thresholds, while variable maintenance thresholds are usually ignored. Under these contexts, a novel multi-level imperfect maintenance model with variable preventive maintenance (PM) thresholds, variable overhaul maintenance (OM) thresholds and variable number of PMs in each OM cycle is established. In order to deal with the concerned problem, a novel twin delayed deep deterministic policy gradient (TD3) algorithm that is a kind of reinforcement learning is designed, renamed as NTD3. Finally, through numerical simulation, we can find that (1) the average improvements between the proposed maintenance strategy and other three traditional strategies in the average cost rate (ACR) are 11.50%, 595.91% and 5.16%, respectively; and (2) the average improvement between the proposed NTD3 and other random search method is 5.53%. Thus, the effectiveness of the proposed maintenance strategy and the superiority of proposed NTD3 are all demonstrated.

The solar beryllium abundance revisited with 3D non-LTE models

The present-day abundance of beryllium in the solar atmosphere provides clues about mixing mechanisms within stellar interiors. However, abundance determinations based on the Be II313.107 nm line are prone to systematic errors due to imperfect model spectra. These errors arise from missing continuous opacity in the UV, a significant unidentified blend at 313.102 nm, departures from local thermodynamic equilibrium (LTE), and microturbulence and macroturbulence fudge parameters associated with one-dimensional (1D) hydrostatic model atmospheres. Although these factors have been discussed in the literature, no study has yet accounted for all of them simultaneously. To address this, we present 3D non-LTE calculations for neutral and ionised beryllium in the Sun. We used these models to derive the present-day solar beryllium abundance, calibrating the missing opacity on high resolution solar irradiance data and the unidentified blend on the centre-to-limb variation. We find a surface abundance of 1.21 ± 0.05 dex, which is significantly lower than the value of 1.38 dex that has been commonly adopted since 2004. Taking the initial abundance via CI chondrites, our result implies that beryllium has been depleted from the surface by an extra 0.11 ± 0.06 dex, or 22 ± 11%, on top of any effects of atomic diffusion. This is in tension with standard solar models, which predict negligible depletion, as well as with contemporary solar models that have extra mixing calibrated on the abundances of helium and lithium, which predict excessive depletion. These discrepancies highlight the need for further improvements to the physics in solar and stellar models.

Make Some Noise: Generating Data from Imperfect Factor Models

Researchers simulating covariance structure models sometimes add model error to their data to produce model misfit. Presently, the most popular methods for generating error-perturbed data are those by Tucker, Koopman, and Linn (TKL), Cudeck and Browne (CB), and Wu and Browne (WB). Although all of these methods include parameters that control the degree of model misfit, none can generate data that reproduce multiple fit indices. To address this issue, we describe a multiple-target TKL method that can generate error-perturbed data that will reproduce target RMSEA and CFI values either individually or together. To evaluate this method, we simulated error-perturbed correlation matrices for an array of factor analysis models using the multiple-target TKL method, the CB method, and the WB method. Our results indicated that the multiple-target TKL method produced solutions with RMSEA and CFI values that were closer to their target values than those of the alternative methods. Thus, the multiple-target TKL method should be a useful tool for researchers who wish to generate error-perturbed correlation matrices with a known degree of model error. All functions that are described in this work are available in the fungible R library. Additional materials (e.g., R code, supplemental results) are available at https://osf.io/vxr8d/.

Research on Flexible Data Model of MES System based on Time-series Database

In the process of advancing towards digitization and intelligence in manufacturing, Manufacturing Execution Systems (MES) have gradually become the core of production processes. The design of the data model is crucial as it affects the scalability, maintainability, and stability of the system. This study focuses on addressing issues in MES systems such as high investment costs, imperfect data models, lack of flexibility, and inefficient data flow. It proposes a novel flexible data model based on time-series databases.This paper introduces the definition and characteristics of time-series databases, analyzes the shortcomings of existing MES system data models, and proposes a novel flexible MES data model based on time-series databases. Finally, the practicality and efficiency of the new data model are validated through experiments.

Developing an automatic integration approach to generate brick model from imperfect building information modelling

Recent advanced architectural algorithms, such as real-time optimization control and fault diagnostics, have garnered significant interest for their ability to reduce building energy consumption and improve equipment maintenance efficiency. However, deploying these algorithms in actual buildings often involves significant manual effort and time to align building data points with algorithmic requirements. In response, standardized ontologies like the Brick Schema have been introduced. This ontology streamlines the representation of building topology, equipment, and data points, as well as algorithm inputs, enabling quicker algorithm integration and deployment. Despite these advancements, manually constructing Brick models from scratch remains time-consuming, laborious, and prone to errors, presenting a new challenge in rapid model development. To overcome these issues, some researchers have turned to universal BIM formats, such as IFC/Revit, for accelerated Brick modeling. Nevertheless, the prevalence of errors in human-generated BIM models often impedes effective conversion. To efficiently convert imperfect IFC models into Brick models, this paper presents a novel BIM to Brick methodology comprising three key steps: IFC inspection and repair, IFC to Brick semantic modeling, and dataset mapping. This method ensures high-quality reuse of even imperfect IFC models. Our case study demonstrates that this method can produce an ideal Brick model from a flawed BIM model, meeting the semantic metadata requirements specified by the BuildingMOTIF tool. This innovative approach substantially reduces the effort required to generate Brick models, effectively handling the inaccuracies and noise inherent in real-world BIM.

Model orthogonalization and Bayesian forecast mixing via principal component analysis

One can improve predictability in the unknown domain by combining forecasts of imperfect complex computational models using a Bayesian statistical machine learning framework. In many cases, however, the models used in the mixing process are similar. In addition to contaminating the model space, the existence of such similar, or even redundant, models during the multimodeling process can result in misinterpretation of results and deterioration of predictive performance. In this paper we describe a method based on the principal component analysis that eliminates model redundancy. We show that by adding model orthogonalization to the proposed Bayesian model combination framework, one can arrive at better prediction accuracy and reach excellent uncertainty quantification performance. Published by the American Physical Society 2024

Stochastic Dynamic Buckling Analysis of Cylindrical Shell Structures Based on Isogeometric Analysis

In this paper, we extend our previous work on the dynamic buckling analysis of isogeometric shell structures to the stochastic situation where an isogeometric deterministic dynamic buckling analysis method is combined with spectral-based stochastic modeling of geometric imperfections. To be specific, a modified Generalized-α time integration scheme combined with a nonlinear isogeometric Kirchhoff–Love shell element is used to simulate the buckling and post-buckling problems of cylindrical shell structures. Additionally, geometric imperfections are constructed based on NURBS surface fitting, which can be naturally incorporated into the isogeometric analysis framework due to its seamless CAD/CAE integration feature. For stochastic analysis, the method of separation is adopted to model the stochastic geometric imperfections of cylindrical shells based on a set of measurements. We tested the accuracy and convergence properties of the proposed method with a cylindrical shell example, where measured geometric imperfections were incorporated. The ABAQUS reference solutions are also presented to demonstrate the superiority of the inherited smooth and high-order continuous properties of the isogeometric approach. For stochastic dynamic buckling analysis, we evaluated the buckling load variability and reliability functions of the cylindrical shell with 500 samples generated based on seven nominally identical shells reported in the geometric imperfection data bank. It is noted that the buckling load variability in the cylindrical shell obtained with static nonlinear analysis is also presented to show the differences between dynamic and static buckling analysis.

Product refurbishment in an imperfect production inventory system under carbon regulations: An interval type-2 fuzzy environment

This study develops an imperfect production inventory model focusing on the proper handling of imperfect products and addressing carbon emissions in different stages of a production process. The proposed inventory system consists of three stages: production, screening, and refurbishing. The produced products undergo a screening process, with perfect quality items being delivered to the primary market, while imperfect quality products are routed through a refurbishing process, subsequently sold in the secondary market. It is observed that in the existing literature on refurbished products, all such types of products have been sold at the end of the business period, this means that there is no continuous demand of refurbished product at the remaining time points of the business period which does not support the practical situation in a real business system. In this regards, our main objective in this study is consideration of continuous demand of refurbished products along with perfect ones. Again, we incorporate interval type-2 fuzzy uncertainty in both markets’ demand to better reflect market fluctuations introducing a new defuzzification method. In this framework, we evaluate the effectiveness of two emission regulatory strategies: carbon tax and carbon cap and trade policies. The findings of this study offer valuable insights into various aspects. Notably, our analysis reveals that if the difference between the primary and secondary markets’ demands reduces to a large extent, a negative impact on the profit is observed. It is also shown that an increase in demand for refurbished products allows for a higher permissible rate of imperfections in production. Comparing the outcomes under two emission regulations, we find that the cap and trade policy is beneficial from both economic and environmental perspectives. Additionally, it becomes evident that the manufacturer should carefully consider the duration of each inventory stage, as it leads to a significant increase in overall emissions. Regarding the uncertain nature of the market, it is observed that demand volatility in the secondary market negatively affects the profit.

Identifying arguments within a text: Categorizing errors and their impact in arguments' relation prediction

The automatic identification of argument units within a text is a crucial task, as it is the first step that should be performed by an end-to-end argument mining system. In this work, we propose an approach for categorizing errors in predicted argument units, which allows the evaluation of segmentation models from an argumentative perspective. We assess the ability of several models to generalize knowledge across different text domains and, through the proposed categorization, we show differences in their behavior that may not be noticeable using standard classification metrics. Furthermore, we assess how the errors in predicted argument units impact on a task that rely on accurate unit identification, an aspect that has not been studied in previous research, and that helps to evaluate the usability of an imperfect segmentation model beyond the segmentation task itself.

Imperfect maintenance modelling and estimation for interval-censored data

The paper proposes a new maintenance model for a repairable system. The system is inspected at deterministic times. If a failure occurs between two successive inspections, a minimal repair is performed. At inspection times, two types of maintenance actions can be performed. The first originality of the paper is to consider that the choice between these two types of maintenance depends on the fact that there has been a failure or not since the previous inspection. If no failure has occurred since the last inspection, the maintenance, simply denoted PM, corresponds to a basic preventive maintenance action, whose effect is planned by the initial maintenance plan. If at least one failure has occurred since the last inspection, the maintenance, denoted PCM, includes both the preventive action planned by the initial maintenance plan, and a specific corrective action which aims to fix the defects that have happened since the last inspection. Both types of maintenances are assumed to be imperfect, of the ARA (Arithmetic Reduction of Age) type, with different parameters. Moreover, the paper derives several statistical inference methods for this model. The usual case of complete data is first considered. The second originality of the paper is to analyse interval-censored data in this imperfect maintenance framework. This situation holds when failure times are not observed, and the only information is the fact that failures have occurred or not between two inspections. The quality of the proposed methods is assessed on simulated data. Finally, a real dataset from a gas transmission system operator is provided and analysed with the proposed approach.

PRECISION: A Physics-Constrained and Noise-Controlled Diffusion Model for Photon Counting Computed Tomography.

Recently, the use of photon counting detectors in computed tomography (PCCT) has attracted extensive attention. It is highly desired to improve the quality of material basis image and the quantitative accuracy of elemental composition, particularly when PCCT data is acquired at lower radiation dose levels. In this work, we develop a physics-constrained and noise-controlled diffusion model, PRECISION in short, to address the degraded quality of material basis images and inaccurate quantification of elemental composition mainly caused by imperfect noise model and/or hand-crafted regularization of material basis images, such as local smoothness and/or sparsity, leveraged in the existing direct material basis image reconstruction approaches. In stark contrast, PRECISION learns distribution-level regularization to describe the feature of ideal material basis images via training a noise-controlled spatial-spectral diffusion model. The optimal material basis images of each individual subject are sampled from this learned distribution under the constraint of the physical model of a given PCCT and the measured data obtained from the subject. PRECISION exhibits the potential to improve the quality of material basis images and the quantitative accuracy of elemental composition for PCCT.

Exploration of diverse solutions for the calibration of imperfect climate models

Abstract. The calibration of Earth system model parameters is subject to data, time, and computational constraints. The high dimensionality of this calibration problem, combined with errors arising from model structural assumptions, makes it impossible to find model versions fully consistent with historical observations. Therefore, the potential for multiple plausible configurations presenting different trade-offs between skills in various variables and spatial regions remains usually untested. In this study, we lay out a formalism for making different assumptions about how ensemble variability in a perturbed physics ensemble relates to model error, proposing an empirical but practical solution for finding diverse near-optimal solutions. A meta-model is used to predict the outputs of a climate model reduced through principal component analysis. Then, a subset of input parameter values yielding results similar to a reference simulation is identified. We argue that the effective degrees of freedom in the model performance response to parameter input (the “parametric component”) are, in fact, relatively small, illustrating why manual calibration is often able to find near-optimal solutions. The results explore the potential for comparably performing parameter configurations that have different trade-offs in model errors. These model candidates can inform model development and could potentially lead to significantly different future climate evolution.

Strategic Geosteering Workflow with Uncertainty Quantification and Deep Learning: Initial Test on the Goliat Field Data

Continuous integration of real-time logging-while-drilling data into a subsurface model with relevant geological uncertainties enables strategic geosteering: a field-level optimization of the well-placement strategy. Model errors arising from oversimplified conceptual geological models and imperfect simulation of measurements result in unreliable subsurface-model updates. The model errors are particularly pronounced when synthetic measurements are approximated with a fast but imperfect model, such as a deep neural network (DNN).#xD;We present a practical data-assimilation workflow consisting of offline and online phases. The offline phase involves DNN training and building an uncertain prior near-well geo-model. The online phase utilizes the flexible iterative ensemble smoother (FlexIES) to perform real-time assimilation of extra-deep electromagnetic data while accounting for the model errors in the approximate DNN model. We demonstrate the proposed workflow on historic well-log data from the Goliat Field (Barents Sea). #xD;The median of our probabilistic estimation is on par with proprietary inversion, regardless of the number of layers in the chosen prior or the approximate DNN model. By estimating model errors, FlexIES automatically quantifies the uncertainty in the boundaries and resistivities of layers, which is not standard in proprietary inversion. #xD;This capability allows us to capture uncertainties more efficiently, thus providing input for future quantitative decision support methods. We demonstrate the potential of quantitive decision support by visually estimating the ahead-of-bit risk of reservoir exit that has occurred during the considered operation.

Modal spectrum analysis for an imperfect beam model with random vibration

The cross-Spectral Density (SD) is a method of analyzing the relationship between randomly-input forces and their responses in the frequency domain in spectrum analysis. It is essential for analyzing antiresonant eigenfrequencies that are difficult to understand using only direct-SD. Additionally, the mass in a beam is often analyzed in modern engineering to control the vibration characteristics of a simple beam or as a major model for error. Especially, the scalability can be expected in various fields such as nanosensors as well as macroscopic scale by studying imperfect mass. Firstly, the correlation function and SD are defined for an arbitrary vibration input in a beam. Then, the response function is established according to a harmonic input of some unit force amplitude. Moreover, the direct- and cross-SDs for the response are calculated and mathematically compared with both results. Further analysis is performed by comparing the variation of the SD with response position, the standard deviation (StDev) of the mean of maximum responses, and the bandwidth. The developed cross-SD of the imperfect beam extends the previously developed SD models of the perfect beam and can provide ideas for new types of eigenfrequency analysis, vibration isolation, etc.

Event-based secure control for cyber-physical systems against false data injection attacks

This paper mainly studies an event-based secure control problem for cyber-physical systems against false data injection (FDI) attacks. First, a dynamic event-triggered transmission protocol is established to schedule the transmitted data from the sensor to the controller. Subsequently, an imperfect FDI attack model is constructed considering the real network environment, which would bypass the traditional residual-based detection methods. To overcome this drawback, an attack detection strategy based on secure history information is designed, which effectively mitigates the negative impact of uncertainty on the detection results. Based on the detection results, an event-based secure linear quadratic Gaussian control scheme is designed, which can guarantee system performance and reduce the communication load effectively. Finally, a simulation experiment verifies that the proposed control method outperforms other algorithms in the literature, and the detection accuracy is increased by 91% to 98%.

Distributional effects of immigration and imperfect labour markets

In canonical models, the labour share is orthogonal to immigration shocks in the long run, regardless of the impact of immigration on productivity. In contrast, this paper provides evidence that immigration increases labour productivity while reducing the labour share. We produce this evidence using data from Great Britain with a shift-share instrument that exploits European Union expansions and changes in immigration to other high-income countries. Our results are consistent with the predictions from imperfect labour market models, where immigrant and native workers are heterogeneous in skills, and the former have lower labour supply elasticities than the latter. A significant implication of our analysis is that immigration redistributes income from workers to employers.