Parametric Model Research Articles

Kinematic and Parametric Modeling of 6DOF(Degree-of-Freedom) Industrial Welding Robot Design and Implementation

Kinematic and Parametric Modeling of 6DOF(Degree-of-Freedom) Industrial Welding Robot Design and Implementation

A robust domain distribution alignment discriminative network driven by physical samples for rotor-bearing fault diagnosis

The diagnostic performance of most intelligent models depends on having a sufficient number of labeled samples, which are difficult to obtain in real-world scenarios. Physics-guided sample generation emerges as a promising remedy for addressing the scarcity of labeled samples during model training. However, existing studies have tended to overlook the coupling effect between the marginal and conditional distributions of physical and real samples, leading to domain confusion and a subsequent reduction in model generalization and diagnostic accuracy. Therefore, this study introduces a novel approach: the physically driven sample-based domain distribution alignment discriminative network (DDADN), which integrates dynamic modeling and domain adaptation. Specifically, a lumped parameter model is established to generate complete defect samples, subsequently optimized using the Pearson correlation coefficient (PCC). Next, an enhanced joint distribution adaptation (EJDA) mechanism is employed to harmonize distributions across domains, effectively aligning both overall and class-specific distributions within the domains and thus achieving adaptive domain confusion. Meanwhile, to ensure robust feature classification and extraction, an adaptive softmax (A-softmax) loss function is introduced. Finally, in unlabeled real operating conditions, the proposed multitask scheme adeptly transfers knowledge between physical and real signals, yielding impressive classification accuracies of 97.78% and 98.34% for the experimental and Case Western Reserve University (CWRU) datasets, respectively. These results underscore the superiority of the proposed scheme in terms of learning transferable features and maintaining high diagnostic accuracy and generalization capability.

Stealth Unmanned Aerial Vehicle Penetration Efficiency Optimization Based on Radar Detection Probability Model

Aerodynamic/stealth optimization is a key issue during the design of a stealth UAV. Balancing the weight of different incident angles of the RCS and combining stealth characteristics with aerodynamic characteristics are hotspots of aerodynamic/stealth optimization. To address this issue, this paper introduces a radar detection probability model to solve the weight balance problem of incident angles of the RCS and a penetration efficiency model to transfer the multi-object optimization into single-objective optimization. In this paper, a parameterized model of a flying-wing UAV is selected as the research object. A gradient-free optimization algorithm based on the genetic algorithm is used for maximizing efficiency. The optimization model balances the influence of the RCS mean value and RCS peak value on stealth performance. Moreover, the model achieves an optimal entire life cycle penetration efficiency coefficient by balancing aerodynamic and stealth optimization. The results show that the optimized model improves the penetration efficiency coefficient by 13.84% and increases maximum flight sorties by 1.8%. These results prove that the model has a reasonable combination of aerodynamic and stealth optimization for UAVs undertaking penetration missions.

Machine learning-based causal inference for evaluating intervention in travel behaviour research: A difference-in-differences framework

Causal inference with the difference-in-differences (DID) framework is popular in identifying causal effects with observational data and has started to be applied in recent travel behaviour studies. Most relevant transportation research adopts the conventional linear parametric DID model, which is known to be inflexible and restrictive. This study applies non-parametric DID estimators facilitated by machine learning (ML) models for causal inference in a variety of data scenarios. Semi-parametric and doubly robust estimators are established and integrated with the ML-based cross-fitting pipeline. Simulation studies and empirical case studies are conducted to showcase the ability of ML-based DID to detect causal effects from both simulated and real-world datasets. Results suggest that the proposed methods outperform conventional DID models in all data scenarios. Light working models are generally preferred over hyperparameter-dependent ones for their comparable performance, lower computational burden, and higher levels of compatibility to real-world empirical analysis. Empirical case studies also demonstrate how the proposed DID method could be applied to evaluate the impacts of various interventions on travel behaviour in different contexts. The present study adds to the existing travel behaviour literature by leveraging machine learning algorithms and non-parametric estimators to the impact evaluation of external interventions on travel characteristics and expanding the application of causal inference approaches in transportation research.

The effect of the Solar System Ephemeris on the search for the nano-Hz gravitational wave background

ABSTRACT The detection of the nano-Hz gravitational-wave background (GWB) is one of the main targets of Pulsar timing arrays (PTAs). The detection can be achieved via searching for a common signal with quadrapolar correlation between pulsar pairs. Errors in the Solar-System ephemeris (SSE) can induce dipolar correlations in PTA data, which may affect the results of GWB searches, especially when the data quality is not high enough to constrain the correlation pattern. We investigate the effect of unmodelled SSE errors on GWB searches with PTAs, using simulations with properties based on the European Pulsar Timing Array data set. When the GWB signal is strong, SSE errors have little effect on the GWB search results, including parameter inference and model selection. When the GWB signal is weak, SSE errors can lead to overestimation of the GWB amplitude. However, model comparison would show strong support for dipolar correction, which implies the source of the common signal to be SSE-related and helps avoid its misidentification as a GWB signal. This indicates that SSE error is unlikely to be the main source of the common signal detected recently with real PTA data. We also use simulations to test the ability of the SSE model LINIMOSS in absorbing SSE-error signals and leaving the GWB signal intact. We show that marginalizing LINIMOSS planetary parameters with infinite priors is good at absorbing SSE errors, but may also lead to improper absorption of a GWB signal. Caution is therefore required when setting appropriate limits on the priors of SSE parameters.

Mechanisms and implications of autonomous vehicle market penetration: Insights from a Markov forecasting model

Due to the rapid evolution of autonomous driving technology and the complexity of market penetration mechanisms, establishing a reliable quantitative research approach for measuring autonomous vehicle (AV) penetration and effectively validating forecasted outcomes poses significant challenges. To address this issue, this paper overcomes data limitations by starting from the perspective of Chinese automotive market. It introduces a quantifiable Markov forecasting model that establishes the link between transition probabilities and penetration influencing factors. Through a penetration network, it visually represents the correlation and evolutionary states of AVs. Building upon the model, a framework for data quantification and analysis is formed. By quantifying model indicators with market data such as car performance and historical sales, the network parameters and transition probabilities are continuously updated in real-time. This drives the model to output short-term forecasts for AV penetration in the automotive market. In addition, we devise a two-stage simulation algorithm to accomplish parameter calibration and model validation. Through validation and comparative analysis, it is observed that, compared to direct learning from historical data, our model can more accurately forecast real market penetration trends. Furthermore, sensitivity analysis experiments on market strategies indicate that, compared to technical investment, the market exhibits a higher sensitivity to price adjustments. A strategy combination of increased technical investment in high-level vehicles and judiciously raising prices proves more advantageous for intelligent transformation in the automotive sector than a singular strategy. Additionally, as the AV market evolves, the sensitivity to favorable strategies will gradually increase. Therefore, the developmental stage of the market is a crucial factor for both car companies and investors to consider. The insights gleaned from this paper offer actionable guidance for policymakers and automotive corporations in shaping future market strategies, thereby fostering the continued growth of autonomous driving technologies within the industry.

Reverse Engineering Workflows for the Structural Assessment of Historical Buildings

ABSTRACT Supported by advancements in 3D scanning and parametric modeling tools within the architecture, engineering, and construction sectors, reverse engineering processes for cultural heritage (CH) have recently gained popularity. While many studies have focused on simple 3D reconstructions to create virtual environments, a specialized subarea of this research field has targeted more specific digitization objectives, including, among many, structural analysis of building components. This emerging field has not yet been systematically developed due to the intrinsic challenges associated with CH. Within this context, this paper proposes and describes a reverse engineering-based method that utilizes terrestrial laser scanning and visual programming (VP) to analyze displacements and deformations that occurred over time in historical masonry buildings and applied it to the timber trusses, masonry facades, and columns of two selected Italian case study buildings. This method allows for comparing the surveyed condition of these components, considered the “deformed state”, with their ideal configuration, reconstructed using VP algorithms, considered the “original state”. The outcomes of this comparison facilitate the investigation of the components’ structural behavior and support joint considerations to assess the overall condition of the investigated building, providing helpful knowledge for guiding structural improvement interventions.

Leveraging Optimal Sparse Sensor Placement to Aggregate a Network of Digital Twins for Nuclear Subsystems

Nuclear power plants (NPPs) require continuous monitoring of various systems, structures, and components to ensure safe and efficient operations. The critical safety testing of new fuel compositions and the analysis of the effects of power transients on core temperatures can be achieved through modeling and simulations. They capture the dynamics of the physical phenomenon associated with failure modes and facilitate the creation of digital twins (DTs). Accurate reconstruction of fields of interest (e.g., temperature, pressure, velocity) from sensor measurements is crucial to establish a two-way communication between physical experiments and models. Sensor placement is highly constrained in most nuclear subsystems due to challenging operating conditions and inherent spatial limitations. This study develops optimized data-driven sensor placements for full-field reconstruction within reactor and steam generator subsystems of NPPs. Optimized constrained sensors reconstruct field of interest within a tri-structural isotropic (TRISO) fuel irradiation experiment, a lumped parameter model of a nuclear fuel test rod and a steam generator. The optimization procedure leverages reduced-order models of flow physics to provide a highly accurate full-field reconstruction of responses of interest, noise-induced uncertainty quantification and physically feasible sensor locations. Accurate sensor-based reconstructions establish a foundation for the digital twinning of subsystems, culminating in a comprehensive DT aggregate of an NPP.

An Approach of BIM-Based Dynamic Adaptive Zoning for Group Piles Construction Multi-Work Areas

In large-scale pile foundation drilling projects, the absence of digital work area management hampers dynamic construction management, affecting efficiency. This article explores multi-work area management during pile foundation drilling using a BIM parameterized model, focusing on informatization. The results indicate the following: (i) A dynamic zoning method for pile foundation construction using BIM models was developed to support information management systems and address resource allocation challenges amid dynamic construction team changes. (ii) Adaptive zoning methods were proposed, incorporating the dynamic adjustment of construction work areas, including the division of virtual work areas and adaptive adjustment of pile foundation partition parameters. (iii) Work area modeling and zoning were applied on site, with pile foundation modeling aligning with engineering design distribution, and work area zoning accurately reflecting the on-site construction status. (iv) This method enables adaptive synchronization between pile foundation model attributes and work area information, integrating zoning management into the information system to enhance the construction unit’s information management system and digital management level.

Semiparametric efficiency gains from parametric restrictions on propensity scores

Abstract We explore how much knowing a parametric restriction on propensity scores improves semiparametric efficiency bounds in the potential outcome framework. For stratified propensity scores, considered as a parametric model, we derive explicit formulas for the efficiency gain from knowing how the covariate space is split. Based on these, we find that the efficiency gain decreases as the partition of the stratification becomes finer. For general parametric models, where it is hard to obtain explicit representations of efficiency bounds, we propose a novel framework that enables us to see whether knowing a parametric model is valuable in terms of efficiency even when it is high dimensional. In addition to the intuitive fact that knowing the parametric model does not help much if it is sufficiently flexible, we discover that the efficiency gain can be nearly zero even though the parametric assumption significantly restricts the space of possible propensity scores.

Expectation-Maximization enables Phylogenetic Dating under a Categorical Rate Model.

Dating phylogenetic trees to obtain branch lengths in time unit is essential for many downstream applications but has remained challenging. Dating requires inferring substitution rates that can change across the tree. While we can assume to have information about a small subset of nodes from the fossil record or sampling times (for fast-evolving organisms), inferring the ages of the other nodes essentially requires extrapolation and interpolation. Assuming a distribution of branch rates, we can formulate dating as a constrained maximum likelihood (ML) estimation problem. While ML dating methods exist, their accuracy degrades in the face of model misspecification where the assumed parametric statistical distribution of branch rates vastly differs from the true distribution. Notably, most existing methods assume rigid, often unimodal, branch rate distributions. A second challenge is that the likelihood function involves an integral over the continuous domain of the rates and often leads to difficult non-convex optimization problems. To tackle these two challenges, we propose a new method called Molecular Dating using Categorical-models (MD-Cat). MD-Cat uses a categorical model of rates inspired by non-parametric statistics and can approximate a large family of models by discretizing the rate distribution into k categories. Under this model, we can use the Expectation- Maximization (EM) algorithm to co-estimate rate categories and branch lengths in time units. Our model has fewer assumptions about the true distribution of branch rates than parametric models such as Gamma or LogNormal distribution. Our results on two simulated and real datasets of Angiosperms and HIV and a wide selection of rate distributions show that MD-Cat is often more accurate than the alternatives, especially on datasets with exponential or multimodal rate distributions.

Effective comparison of two potentially crossing hazard rate curves

In survival data analysis, comparison of two hazard rate curves is critically important for evaluating a treatment effect. In many applications, the two hazard curves could potentially cross each other, violating the proportional hazards assumption in the Cox's model. In such cases, the traditional tests like the log-rank test and the Peto-Peto test that were developed based on that assumption would be ineffective. There have been some discussions in the literature on comparison of two potentially crossing hazard curves, based on either parametric modeling or nonparametric testing approaches. However, the assumed models of the existing parametric methods are often difficult to justify in practice. On the other hand, the existing nonparametric tests are usually based on the maximization with respect to an unknown crossing point, leading to complex null distributions for the corresponding test statistics. We suggest a novel method in this paper for comparing two hazard curves based on a nonparametric testing procedure. Its test statistic avoids the maximization mentioned above and consequently has the desirable asymptotic normality property under some regularity conditions. We show that the new method is effective for comparing two potentially crossing hazard curves.

Informative design exploration of tower building geometry typology and solar potential using SOM-MLPNN with generalized form-description-based parametric modelling

To synchronize designers early in the design process and help them understand the building forms and performance metrics needed to meet both qualitative and quantitative requirements is crucial for sustainable building designs. While the prevalent design exploration technique, which integrates self-organizing mapping with a Supervised Learning-based Agent Model (SOM-MLPNN), offers insights into design geometry and solar potential, it still has limitations in the range of the design exploration space, the efficiency and accuracy of data approximation, and the uncertainty in dealing with performance metrics related to more complex environmental factors. Using the solar potential of tower buildings as an example, this study proposes a method based on SOM-MLPNN by integrating a Generalized Tower Generator (GTG) and form description, testing its usability for the inclusion of complex surrounding environments, and improving its design exploration breadth as well as the efficiency and accuracy of data approximations. The results demonstrate improved efficiency and accuracy in data approximation across an expansive design space, facilitating visual and interactive exploration of design-performance relationships. The proposed method emphasizes the critical interplay between building geometry and environment-related performance, providing designers with a refined toolkit for discerning the correlation between design and performance, thereby enhancing their design decision-making abilities.

Excess Mortality and Loss of Life Expectancy After Myocardial Infarction: A Registry-Based Matched Cohort Study.

The effect of myocardial infarction (MI) on life expectancy is difficult to study because the prevalence of MI hinders direct comparison with the life expectancy of the general population. We sought to assess this in relation to age, sex, and left ventricular ejection fraction (LVEF) by comparing individuals with MI with matched comparators without previous MI. We included patients with a first MI between 1991 and 2022 from the nationwide SWEDEHEART registry (Swedish Web-System for Enhancement and Development of Evidence-Based Care in Heart Disease Evaluated According to Recommended Therapies), each matched with up to 5 comparators on age, sex, and region of residence. Flexible parametric survival models were used to estimate excess mortality risk and mean loss of life expectancy (LOLE) depending on index year, age, sex, and LVEF, and adjusted for differences in characteristics. A total of 335 748 cases were matched to 1 625 396 comparators. A higher LOLE was observed in younger individuals, women, and those with reduced LVEF (<50%). In 2022, the unadjusted and adjusted mean LOLE spanned from 11.1 and 9.5 years in 50-year-old women with reduced LVEF to 5 and 3.7 months in 80-year-old men with preserved LVEF. Between 1992 and 2022, the adjusted mean LOLE decreased by 36% to 55%: from 4.4 to 2.0 years and from 3.3 to 1.9 years in 50-year-old women and men, respectively, and from 1.7 to 1.0 years and from 1.4 to 0.9 years in 80-year-old women and men, respectively. LOLE is higher in younger individuals, women, and those with reduced LVEF, but is attenuated when adjusting for comorbidities and risk factors. Advances in MI treatment during the past 30 years have almost halved LOLE, with no clear sign of leveling off to a plateau.

Evaluation of the Goss-modified solvation parameter model for the characterization of biphasic systems and descriptor assignments

The solvation parameter model uses six descriptors identified as excess molar refraction, E, dipolarity/polarizability, S, overall hydrogen-bond acidity, A, overall hydrogen-bond basicity, B, McGowan's characteristic volume, V, and the gas-liquid partition constant on hexadecane at 25 °C, L to model the distribution of neutral compounds in biphasic systems. Abraham's version of this model uses all six descriptors with two separate linear free energy relationship models for the transfer of compounds from a gas phase to a condensed phase and between condensed phases. Goss proposed a modification to this model that uses a single calibration model regardless of the physical state for each phase and five of the descriptors employed in Abraham's model (E descriptor is eliminated). The capability of Abraham's model and the Goss-modified model to characterize the contribution of intermolecular interaction to retention for gas and reversed-phase liquid chromatographic systems and distribution in liquid-liquid partition systems is evaluated using the WSU compound descriptor database. These more accurate values for the Abraham descriptors have not been utilized previously for the evaluation of the Goss-modified model and should be more capable of discerning subtle differences in model performance. It is shown that model quality defined by statistical parameters favors Abraham's model over the Goss-modified model with differences in model quality greater for systems in which Abraham's model indicates a significant contribution from electron lone pair interactions and for systems in which one phase is a solvent containing perfluoroalkyl substituents. There is a small systematic difference for the terms describing the combined contributions of cavity formation and dispersion interactions and for interactions of a dipole-type. The contribution of hydrogen-bonding interactions is virtually identical for the two models. The model intercepts are generally different and potentially assigned to a larger contribution from lack-of-fit for the Goss-modified model. Although the Abraham model descriptors have been routinely employed for applications using the Goss-modified model the possibility that Goss-model specific descriptors should be employed was evaluated. Using the Solver method and Goss-model specific calibration models for chromatographic and liquid-liquid partition systems a new set of Goss-specific descriptors was calculated for 28 varied compounds. These descriptors show good statistical agreement with the Abraham descriptor values with an average deviation of 0.009, -0.003, -0.004, and -0.023, respectively, for the S, A, B, and L descriptors, corresponding to a relative absolute deviation in percent of 2.2 %, 3.9 %, 4.3 %, and 1.2 %, respectively.

Computational Fluid Dynamics Simulation of Combustion Efficiency for Full-Size Upstream Flare Experiments

Methane emissions from oil and gas production can occur throughout the value chain, but for many producers, one of the most significant sources is flaring. Understanding the influence of the operating conditions and the environmental factors the combustion efficiency and destruction and removal efficiency (CE/DRE) of flares is essential if their role in methane emissions, a potent but short-lived greenhouse gas, is to be better understood and mitigated. An industry-scale experimental study was focused on the emissions of un-assisted flares commonly encountered in upstream oil and gas production. This paper simulates two un-assisted flare tips combustions by using the commercial computational fluid dynamics (CFD) software package Fluent 21R2 to augment the physical experimental testing. Two three-dimensional (3D) flare tips models are built, and the k-omega SST turbulence model and flamelet generated manifold (FGM) combustion model are applied to simulate flaring combustion. The CFD model is first validated against full-scale industry flare tests that use extractive sampling of the combustion plume. CFD results are in good agreement with measured results when the vent gas net heating value (NHV) is greater than 300 BTU/SCF. Greater uncertainty exists for both CFD results and measured data if the NHV is less than 300 BTU/SCF. Then, the CFD model is extended to include high crosswind states up to 50 m/s that cannot be readily or safely examined empirically. The results emphasize the critical role of the vent gas net heating value (NHV) on flare combustion and crosswind in reducing the CE. The comparison helps pave the way for further use of CFD simulation to improve flare designs and modes of operation and supports the use of parametric models to track and report methane losses from flaring.

Survival outcomes and healthcare utilization between immigrant patients and Danish-born patients with hematological cancers: a Danish population-based study.

Overall survival (OS) for patients with a hematological cancer may differ between immigrant and Danish-born patients due to disparities in socioeconomic status, health literacy, and language proficiency. This cohort study aimed to investigate survival and hospitalization according to immigrant status while controlling for confounders. Patients with newly diagnosed hematological cancer in 2000-2020 were identified in the Danish nationwide hematological registers and stratified into Danish-born, Western, and non-Western patients. Patients were followed from diagnosis until death, 31st December 2021, or emigration, whichever came first. Crude OS, standardized OS, and 5-years OS differences were computed using flexible parametric models and hazard ratios using Cox regression. Number of hospitalization days in the year before and after diagnosis, respectively, were calculated using Poisson regression. A total of 2,241 immigrants and 41,519 Danish-born patients with a hematological cancer were included. Standardized 5-years OS was similar between groups with 58% (95% confidence interval 57-58%) for Danish-born patients, 57% (55-60%) for Western, and 56% (53-58%) for non-Western immigrant patients. Subgroup analyses identified OS differences in selected subgroups. Non-Western immigrant patients had 1.3 (0.5-2.1) more hospitalization days in the year before diagnosis and an adjusted incidence rate ratio of hospitalization days of 1.14 (1.13-1.15) in the year after diagnosis compared with Danish-born patients. In conclusion, there were no overall differences in survival when comparing immigrant patients to Danish-born patients after controlling for relevant confounders. Healthcare utilization was slightly higher among non-Western immigrant patients before and after diagnosis, but differences were small on an individual patient level.

A Novel Joint Motion Compensation Algorithm for ISAR Imaging Based on Entropy Minimization.

Space targets move in orbit at a very high speed, so in order to obtain high-quality imaging, high-speed motion compensation (HSMC) and translational motion compensation (TMC) are required. HSMC and TMC are usually adjacent, and the residual error of HSMC will reduce the accuracy of TMC. At the same time, under the condition of low signal-to-noise ratio (SNR), the accuracy of HSMC and TMC will also decrease, which brings challenges to high-quality ISAR imaging. Therefore, this paper proposes a joint ISAR motion compensation algorithm based on entropy minimization under low-SNR conditions. Firstly, the motion of the space target is analyzed, and the echo signal model is obtained. Then, the motion of the space target is modeled as a high-order polynomial, and a parameterized joint compensation model of high-speed motion and translational motion is established. Finally, taking the image entropy after joint motion compensation as the objective function, the red-tailed hawk-Nelder-Mead (RTH-NM) algorithm is used to estimate the target motion parameters, and the joint compensation is carried out. The experimental results of simulation data and real data verify the effectiveness and robustness of the proposed algorithm.

Evaluation of Data Fitting Approaches for Speed/Flow Density Relationships

This paper presents guidance on data-fitting approaches in the context of pedestrian and evacuation dynamics research. In particular, it examines parametric and non-parametric regression techniques for analysing speed/flow density relationships. Parametric models assume predefined functional forms, while non-parametric models provide flexibility to capture complex relationships. This paper evaluates a range of traditional statistical approaches and machine-learning techniques. It emphasises the importance of weighting unbalanced datasets to enhance model accuracy. Practical applications are illustrated using traffic and pedestrian evacuation data. This paper is intended to stimulate discussion on best practices for developing, calibrating, and testing macroscopic and microscopic evacuation models. It does not prescribe a one-size-fits-all solution for evacuation data fitting approaches, but it provides an overview of existing methods and analyses their advantages and limitations.

Evaluation and prediction of bending properties of glulam beams made from young plantation-grown Eucalyptus nitens

ABSTRACT Interest in making glulam from plantation-grown Eucalyptus nitens has increased, but current structural timber standards were not designed for species grown in plantations. Additionally, the applicability of classical composite beam theory remains uncertain due to the unique features of this species. This study was designed to assess the hypothesis that the bending performance of glulam made from young plantation-grown E. nitens is reliable and predictable. We tested the hypothesis through experimental procedures and theoretical and statistical analyses. First, we assessed the bending performance of 20 glulam beams, which were manufactured in five configurations. Second, we compared our experimental results with predictive values to evaluate the accuracy of methods in current standards. Third, in our pursuit of improved predictive tools, we developed theoretical and parametric models using mechanical properties of the board elements. Our improved models showed a decreased deviation in both stiffness and strength values. A Monte Carlo simulation was used to determine the distribution for both stiffness and strength. The results validated that this product can be allocated a GL grade using current standards and the bending performance is predictable. The study enhances confidence in incorporating E. nitens into glulam products and offers valuable data for future specification modifications.