Model-based Analysis Method Research Articles

Fuzzy logic as a method of analysis of hot forging process of 80MnSi8-6 steel

A novel approach was proposed, based on the application of the fuzzy logic (FL) method for the fast analysis of the hot deformation process of 80MnSi8-6 steel. In the first stage, the curves developed from plastometric tests and the results of studies of the microstructure of the deformed samples were used as input data for the analysis. Input and output variables were adopted and a set of rules based on cause-and-effect relationships was defined, defining the interactions between the variables. A fast FL-controller was designed, and the correctness of its operation was verified by comparison with experimental results and the results of finite element method (FEM) analysis, carried out taking into account the evolution of the microstructure. The process of hot compression under isothermal conditions of 80MnSi8-6 steel specimens was simulated on the Warmumformsimulator (WUMSI), assuming such parameters and other conditions as were used in real tests. It was confirmed that the proposed method, based on the analysis of flow curves and prior austenite grain size using a fuzzy controller, gave satisfactory results. Subsequently, a novel FL-controller was developed to analyze the kinetics of dynamic recrystallization (DRX), using data obtained from the author’s model of this phenomenon for its construction and calibration. The correctness of the controller was confirmed by comparing the results of its DRX volume fraction calculations with the distributions of this value determined by the model and the model-based FEM analysis method, respectively. It was shown that FL is applicable also when a model of the analyzed phenomenon is available. Unlike model-based calculations, a properly designed controller allows the indication of deviations from general trends that can be pointed out and interpreted by a human expert, but significantly faster. It can also serve as a component of a system analyzing complex processes, such as hot multi-stage forging. Fuzzy controller can be used in parallel with modeling or replace models in calculations.

In silico data-based comparison of the accuracy and error source of various methods for noninvasively estimating central aortic blood pressure

Background and objectivesThe higher clinical significance of central aortic blood pressure (CABP) compared to peripheral blood pressures has been extensively demonstrated. Accordingly, many methods for noninvasively estimating CABP have been proposed. However, there still lacks a systematic comparison of existing methods, especially in terms of how they differ in the ability to tolerate individual differences or measurement errors. The present study was designed to address this gap. MethodsA large-scale ‘virtual subject’ dataset (n = 600) was created using a computational model of the cardiovascular system, and applied to examine several classical CABP estimation methods, including the direct method, generalized transfer function (GTF) method, n-point moving average (NPMA) method, second systolic pressure of periphery (SBP2) method, physical model-based wave analysis (MBWA) method, and suprasystolic cuff-based waveform reconstruction (SCWR) method. The errors of CABP estimation were analyzed and compared among methods with respect to the magnitude/distribution, correlations with physiological/hemodynamic factors, and sensitivities to noninvasive measurement errors. ResultsThe errors of CABP estimation exhibited evident inter-method differences in terms of the mean and standard deviation (SD). Relatively, the estimation errors of the methods adopting pre-trained algorithms (i.e., the GTF and SCWR methods) were overall smaller and less sensitive to variations in physiological/hemodynamic conditions and random errors in noninvasive measurement of brachial arterial blood pressure (used for calibrating peripheral pulse wave). The performances of all the methods worsened following the introduction of random errors to peripheral pulse wave (used for deriving CABP), as characterized by the enlarged SD and/or increased mean of the estimation errors. Notably, the GTF and SCWR methods did not exhibit a better capability of tolerating pulse wave errors in comparison with other methods. ConclusionsClassical noninvasive methods for estimating CABP were found to differ considerably in both the accuracy and error source, which provided theoretical evidence for understanding the specific advantages and disadvantages of each method. Knowledge about the method-specific error source and sensitivities of errors to different physiological/hemodynamic factors may contribute as theoretical references for interpreting clinical observations and exploring factors underlying large estimation errors, or provide guidance for optimizing existing methods or developing new methods.

Generalized models for estimating cerebral lateralisation of young children using functional transcranial Doppler ultrasound.

Thompson et al., 2023 (Generalized models for quantifying laterality using functional transcranial Doppler ultrasound. Human Brain Mapping, 44(1), 35-48) introduced generalised model-based analysis methods for determining cerebral lateralisation from functional transcranial Doppler ultrasound (fTCD) data which substantially decreased the uncertainty of individual lateralisation estimates across several large adult samples. We aimed to assess the suitability of these methods for increasing precision in lateralisation estimates for child fTCD data. We applied these methods to adult fTCD data to establish the validity of two child-friendly language and visuospatial tasks. We also applied the methods to fTCD data from 4- to 7-year-old children. For both samples, the laterality estimates from the complex generalised additive model (GAM) approach correlated strongly with the traditional methods while also decreasing individual standard errors compared to the popular period-of-interest averaging method. We recommend future research using fTCD with young children consider using GAMs to reduce the noise in their LI estimates.

Integrating reliability analysis into MBSE for FPGA-based safety critical I&C system design in nuclear power plants

Abstract With the widespread application of Field Programmable Gate Array (FPGA) technology in the instrumentation and control (I&C) systems of nuclear power plants (NPPs), its design reliability has become the key to ensuring the subsequent safe operation of NPPs. However, the traditional document-based design method is no longer sufficient to meet the current design needs of complex systems, and there is a lack of research related to the reliability and security analysis of FPGA-based systems. Therefore, the overall goal of this study is to integrate reliability analysis into the model-based systems engineering (MBSE)-based design of FPGA-based I&C systems. Based on the typical application characteristics of the FPGA, a digital design framework for FPGA-based systems is proposed to realize a traceable design process from requirements to implementation. Subsequently, a model-based forward comprehensive reliability analysis method is proposed, which uses a SysML-based mapping mechanism to integrate Failure Mode and Effects Analysis (FMEA) and Fault Tree Analysis (FTA) methods into the design process, so as to comprehensively identify and analyze failure modes from systems to components and optimize and improve system design. Finally, the proposed method is applied to the analog output design of FPGA-based systems. The proposed method can provide guidance for improving the reliability of FPGA-based DI&C systems and provide theoretical basis for the further engineering application.

Failure behavior and bearing capacity prediction of steel wire-ring nets under rockfall impact using a coordination model-based analysis method

Flexible barriers have been widely used to protect against rockfall disasters. Steel wire-ring nets, which are key components of the barrier system and directly bear impacts, present several unresolved issues, including the prediction of tensile-flexural stiffness and energy absorption considering ring-scale mechanical behaviors, as well as failure behavior under rockfall impact. In this paper, a coordination model for the tensile-flexural stiffness and elastic–plastic energy distribution of steel wire-ring nets is established, and the model parameters, including failure criteria, are calibrated through tests. The failure mechanism and resistance capacity evolution of net rings considering the influence of contact radius is revealed. The full-scale impact tests of nets were conducted to verify the accuracy of the model to predict net failure. The study demonstrates that the coordination model incorporating nonlinear factors in ring-scale can accurately reproduce the stiffness development and energy absorption of the net-ring, laying a foundation for predicting the failure of nets. The predicted net failure energy has an error within 5% of the results from destructive impact tests. The shape and impact attitude of blocks significantly influence the net failure energy, indicating potential safety hazards in net design posed by the assumption of a 26-face polyhedron impact block in current standards.

A surface registration-based approach for assessment of 3D angles in guided growth interventions in the growing femur.

Postoperative assessment of surgical interventions for correcting femoral rotational deformities necessitates a comparative analysis of femoral rotation pre- and post-surgery. While 2D assessment methods are commonly employed, ongoing debate surrounds their accuracy and reliability. To address the limitations associated with 2D analysis, we introduced and validated a 3D model-based analysis method for quantifying the angular and rotational impact of corrective rotational osteotomy in the growing femur. The method is based on surface registration of the pre- and post-intervention 3D femoral models. To this end, 3D triangulated surface models were generated using CT images for the right femurs of 11 skeletally immature pigs, each scanned at two distinct time points with a 12-week interval between scans. In our validation procedures, femoral corrective rotational osteotomy of the post-12-week femur was simulated at varying angles of 5, 10, 15and 20 degrees in three dimensions. Subsequently, a surface 3D/3D registration-based approach was applied to determine the 3D femoral angulation and rotation between the two modelsto assess the method's detection accuracy of the predefined twist angles as ground truth references. The results document the precision and accuracy of the registration-based method in evaluating rotation angles. Consistently high accuracy was observed across all angles, with an accuracy rate of 92.97% and a coefficient of variance of 8.14%. This study has showcased the potential for improving post-operative assessments with significant implications for experimental studies evaluating the effects of correcting rotational deformities in the growing femur. Not applicable.

An improved adaptive Kriging model-based metamodel importance sampling reliability analysis method

An improved adaptive Kriging model-based metamodel importance sampling reliability analysis method

Updating subsystem-level fault-symptom relationships for Temperature and Humidity Control Systems with redundant functions

As we aim for deep space exploration, supporting vital systems, such as the Temperature and Humidity Control System (THCS) in the Environmental Control and Life Support System (ECLSS), through timely onboard fault detection and diagnosis becomes paramount for mission success. Many existing fault diagnosis approaches assume that the function that models the relationship between faults and associated symptoms (fault-symptom relationships) will remain constant throughout the THCS’ lifetime. Therefore, many of these diagnosis methods are not robust enough to automatically account for changes in fault-symptom relationships as a result of changes in the habitat (e.g., system reconfiguration). The work highlighted here is on (i) surveying existing work on adaptable fault diagnosis methods and (ii) showcasing a real-life case study, in which we identified the need for an automatically adaptable fault diagnosis method. The case study focuses on a reconfigured terrestrial THCS analog, the Heating, Ventilation, and Air Conditioning (HVAC) system, where the original fault-symptom relationship is revealed to be no longer accurate. We then apply current adaptable fault-symptom relationship generation methods, such as Model-Based Dependability Analysis (MBDA) methods and data-driven causal discovery methods. Through this analysis, we detail our procedure in (i) identifying relevant fault-free system information, such as redundancy, to revise fault-symptom relationships used in fault diagnosis and (ii) evaluating the fault diagnosis performance in a THCS with the original and revised fault-symptom relationship. Our contribution lies in identifying the shortcomings of current methods and pinpointing future steps in creating an adaptable fault diagnosis framework. We found that although the MBDA methods can automatically generate fault-symptom relationships given system flow information and fault mode of components, they also required manual revision of the aforementioned information to create fault-symptom relationships that reflect redundancies. On the other hand, we concluded that the causal discovery methods can detect fault-free system information, such as redundancies, that may help us revise fault-symptom relationships, but suspect variables that contribute to redundancies may have to be hand-picked.

A BERT-ABiLSTM Hybrid Model-Based Sentiment Analysis Method for Book Review

Aiming at the problem of low accuracy rate of current sentiment analysis methods for book review texts, a book review sentiment analysis method based on BERT-ABiLSTM hybrid model is proposed. First, the overall framework of sentiment analysis is constructed by integrating sentiment vocabulary and deep learning methods, and the fine-grained sentiment analysis is divided into three stages: topic identification, sentiment identification and thematic sentiment identification. Then, a dynamic character-level word vector containing contextual information is generated using a bidirectional encoder representation from transformers (BERT) pre-trained language model. Then, the contextual information in the text data is fully learned by introducing the bidirectional long short-term memory (BiLSTM) model. Finally, the accurate analysis of book review sentiment is achieved by using Attention mechanism to highlight important features and improve the efficiency of resource utilization. Through an experimental comparison with existing advanced algorithms, the proposed method in this study has improved at least 4.2%, 3.9% and 3.79% in precision, recall and F1 values, respectively. The experimental results show that the proposed BERT-ABiLSTM is higher than the existing models under different metrics, indicating that the proposed model has a good application prospect in the fields of book review analysis and book recommendation.

A comprehensive method for causation analysis of ship-ice collision risk in Arctic waters.

The influence of harsh Arctic environmental factors has caused ship-ice collision accidents to become the main threat faced by navigating ships. It is necessary to quantify the causation of ship accidents and perform effective risk management and control measures to ensure ship navigation safety. This study proposes a Bayesian network (BN) model-based risk analysis method for ship-ice collision accidents, which is used to quantitatively analyze the key risk factors and primary risk causation paths of ship accidents. First, the fault tree analysis (FTA) method is proposed to constructing the BN structure model, and the BN parameter solution method is further established. Consequently, a triangular fuzzy and defuzzification method is developed to quantify uncertain expert knowledge. Then, the BN inference method is used to analyze the collision risk causation where the North Atlantic and Arctic waters meet. The results indicate that the main risk factors in Arctic waters come from the environment. There are four primary risk causation paths; the proposed management and control measures of the risk causation paths (A), (B), (C), and (D) can effectively reduce the navigation risk by 29.95%, 9.98%, 25.05%, and 3.99%, respectively; the combination of these four measures can reduce the navigation risk by 54.63%.The proposed method has positive guiding significance for ensuring the safety of ship navigation in Arctic waters.

Polynomial chaos expansion approximation for dimension-reduction model-based reliability analysis method and application to industrial robots

Polynomial chaos expansion (PCE) is considered an excellent method for accurately and efficiently reliability analysis in various engineering problems. However, it becomes practically infeasible in scenarios characterized by high-dimensional input random variables and thousands of training data. To solve this problem, this paper proposes a new PCE-based surrogate-assisted method. To start with, the interacting variables are screened out from original high-dimensional input random variables by contribution-degree analysis (CDA). The original high-dimensional performance function is decomposed into a lower-dimensional component function composed of the interacting variables and multiple one-dimensional component function containing only one non-interacting variable. Then, PCE combined with a sample points selection strategy is used to fit the lower-dimensional component function, and a full PCE is utilized for fitting each one-dimensional component function. Three methods, namely the Hadamard's inequality of the design matrix, the rank revealing QR factorization of the design matrix and the maximum entropy, are implemented to realize sample point selection. Four examples are investigated to demonstrated the effectiveness of the proposed method.

Designing line transect surveys for complex survey regions

Line transect surveys are widely used to estimate the density and/or size of cetacean populations. Good survey design is essential for obtaining reliable results using standard (design-based) analysis methods. Even for more complex (model-based) analysis methods, a good survey design is valuable. A ‘good’ design is one (a) that employs randomisation in laying out transects; (b) that is stratified if density is known to vary on a large scale; (c) where each location within a stratum has an equal probability of being surveyed (uniform coverage probability); (d) that produces an even distribution of transects throughout each stratum (e.g. systematic random designs); (e) that produces at least 10-20 transects per stratum; (f) that, given the previous points, gives maximum efficiency per unit effort – for example by minimising time spent travelling between survey lines (off-effort time). We discuss strategies for creating good designs given the constraints inherent in many shipboard surveys of cetaceans: severely limited ship time and complex topography. We advocate the use of computer software, such as the program Distance, to create designs and compare their properties using simulation. We provide a link between the concepts and their implementation through a concrete example of survey design: a multi-species survey of cetaceans in coastal British Columbia. The design uses an equally spaced zig-zag configuration of transects in more open strata combined with sub-stratification to minimise off-effort time. In the highly convex inshore stratum we develop a systematic cluster sampling algorithm, and within the selected clusters use a systematic parallel line layout to ensure equal coverage probability in the long, narrow fjords. To aid those wishing to learn automated design methods, we provide Distance project files online.

A method for reliability analysis based on active learning

The surrogate model-based reliability analysis method has received a lot of researches. Deep learning (DL) is a powerful method for building a surrogate model. However, DL needs a lot of training data to train the neural network model, which will lead to higher computational cost. Thus, this paper develops a method to reduce the amount of training data based on active learning. By quantifying the epistemic uncertainty of neural network, the most uncertain data will be selected and added to the training dataset. Then, the neural network model is iteratively trained based on the updated training dataset. The reliability analysis of a truss structure is used to validate the developed method, and the result shows that the developed method can apparently reduce the amount of training data.

A reliability analysis method based on quantile regression and feedforward neural network

The surrogate model-based reliability analysis method has drawn researchers' studies in recent years. Deep learning is a powerful method to construct a surrogate model. Due to the data's existing noise which will lead to aleatoric uncertainty, this paper proposes the Monte Carlo (MC) quantile regression method to build the surrogate model based on quantile regression and feedforward neural network. During the training process, the quantile levels for different inputs are randomly changed in each iteration. In the prediction stage, the same input is attached with varying quantiles to quantify the aleatoric uncertainty, and the input is attached with quantile level 0.5 to predict the output. Two examples are used to validate the effectiveness of the proposed MC quantile regression method. The results show that the MC quantile regression method can accurately quantify the aleatoric uncertainty and construct an accurate surrogate model to analyze system reliability.

A Buffeting-Net for buffeting response prediction of full-scale bridges

In order to predict power spectral density (PSD) and root of mean squares (RMS) of buffeting responses of full-scale bridges in real operation conditions, a neural network-based model in frequency domain (Buffeting-Net) is proposed. Inspired by the mechanism of buffeting, the model is composed of two sub-neural networks that output the buffeting force and the generalized frequency response function. These two sub-neural networks are connected according to the model of buffeting response in the frequency domain to output the predicted spectrum of response. Sub-neural networks are constructed by bi-directional gated recurrent unit neural networks for their capacity of modeling complex mappings among sequences. Monitoring data of two long-span bridges are used for model validation, and the accuracy of prediction is evaluated by the cosine similarity of the shape of power spectrum and the relative error of RMS responses. The prediction results of Buffeting-Net are also compared with the traditional FEM-based buffeting method. The results indicate that trained models can make much higher accurate predictions for the bridges in real operational conditions including typhoon and season wind than regular finite element model-based buffeting analysis method. The generalization of the Buffeting-Net is validated, the result indicates the Buffeting-Net can predict the buffeting response with high prediction accuracy even when the wind speed is much greater than that in training.

Analysis and prediction of relative survival trends in patients with non-Hodgkin lymphoma in the United States using a model-based period analysis method.

BackgroundSurvival rates are usually used to evaluate the effect of cancer treatment and prevention. This study aims to analyze the 5-year relative survival of non-Hodgkin lymphoma (NHL) in United States using population-based cancer registry data.MethodsA period analysis was used to evaluate the improvement in long-term prognosis of patients with NHL from 2004 to 2018, and a generalized linear model was developed to predict the 5-year relative survival rates of patients during 2019–2023 based on data from the SEER database stratified by age, sex, race and subtype.ResultsIn this study, relative survival improved for all NHL, although the extent of improvement varied by sex, age group and lymphoma subtype. Survival improvement was also noted for NHL subtypes, although the extent varied, with marginal-zone lymphoma having the highest 5-year relative survival rate (92.5%) followed by follicular lymphoma (91.6%) and chronic lymphocytic leukemia/small lymphocytic lymphoma (87.3%). Across all subtypes, survival rates were slightly higher in females than in males. Survival rates are lower in the elderly than in the young. Furthermore, the study demonstrated that black patients had lower NHL survival rates than white patients. Survival rates for NHL were higher in rural areas than in urban areas. Patients with extra-nodal NHL had a higher survival rate than patients with nodal NHL.ConclusionOverall, patient survival rates for NHL gradually improved during 2004–2018. The trend continues with a survival rate of 75.2% for the period 2019–2023. Analysis by NHL subtype and subgroups indicating that etiology and risk factors may differ by subtype. Identification of population-specific prevention strategies and treatments for each subtype can be aided by understanding these variations.

An efficient multi-fidelity Kriging surrogate model-based method for global sensitivity analysis

Global sensitivity analysis (GSA), particularly for Sobol index, is a powerful tool to quantify the variation of model response sourced from the uncertainty of input variables over the entire design space. However, GSA requires a large number of model evaluations to achieve satisfactory accuracy, which will lead to a great challenge in computational efforts when the model is expensive to be evaluated. To address this issue, an efficient method based on multi-fidelity Kriging (Cokriging) surrogate model is proposed. To this end, high dimensional model representation of Cokriging predictor is preformed to derive the analytical expressions of total and partial variances. Then, the sensitivity analysis is transformed into the computation of several one-dimensional integrals, which is beneficial to reduce the computational burden. Four examples are employed to validate the performance of the proposed method. The results demonstrate that Cokriging estimator is an efficient approach to yield promising accuracy and reduce computational costs in the sensitivity analysis.

Transonic flutter suppression with tuned mass damper by model-based stability analysis method

Flutter as a self-excited oscillation can cause catastrophic damage to the aircraft structure. Tuned mass damper (TMD) is one of the vibration suppression methods widely used in engineering. However, traditional linear aerodynamic methods are not accurate in transonic flow. In this study, we first establish an unsteady aerodynamic model of the NACA0012 airfoil using the system identification method based on the Auto-Regressive with eXogenous input (ARX) model. Then, a flutter suppression model is constructed by coupling the TMD with the aeroelastic system. The parameters of the TMD are investigated using the model-based aeroelastic stability analysis. It is found that the flutter instability mode of the original system has changed due to the participation of the TMD in the system modal coupling. The proposed reduced-order model (ROM) provides a fast stability analysis method for flutter suppression with TMD as well as significant guidance for TMD design and optimization.

AR model-based crosstalk cancellation method for operational transfer path analysis

AR model-based crosstalk cancellation method for operational transfer path analysis

Phenotypic and molecular characterization of a set of tropical maize inbred lines from a public breeding program in Brazil

BackgroundThe characterization of genetic diversity and population differentiation for maize inbred lines from breeding programs is of great value in assisting breeders in maintaining and potentially increasing the rate of genetic gain. In our study, we characterized a set of 187 tropical maize inbred lines from the public breeding program of the Universidade Federal de Viçosa (UFV) in Brazil based on 18 agronomic traits and 3,083 single nucleotide polymorphisms (SNP) markers to evaluate whether this set of inbred lines represents a panel of tropical maize inbred lines for association mapping analysis and investigate the population structure and patterns of relationships among the inbred lines from UFV for better exploitation in our maize breeding program.ResultsOur results showed that there was large phenotypic and genotypic variation in the set of tropical maize inbred lines from the UFV maize breeding program. We also found high genetic diversity (GD = 0.34) and low pairwise kinship coefficients among the maize inbred lines (only approximately 4.00 % of the pairwise relative kinship was above 0.50) in the set of inbred lines. The LD decay distance over all ten chromosomes in the entire set of maize lines with r2 = 0.1 was 276,237 kb. Concerning the population structure, our results from the model-based STRUCTURE and principal component analysis methods distinguished the inbred lines into three subpopulations, with high consistency maintained between both results. Additionally, the clustering analysis based on phenotypic and molecular data grouped the inbred lines into 14 and 22 genetic divergence clusters, respectively.ConclusionsOur results indicate that the set of tropical maize inbred lines from UFV maize breeding programs can comprise a panel of tropical maize inbred lines suitable for a genome-wide association study to dissect the variation of complex quantitative traits in maize, mainly in tropical environments. In addition, our results will be very useful for assisting us in the assignment of heterotic groups and the selection of the best parental combinations for new breeding crosses, mapping populations, mapping synthetic populations, guiding crosses that target highly heterotic and yielding hybrids, and predicting untested hybrids in the public breeding program UFV.