Surrogate Technique Research Articles

The diagnostic and predictive value of fecal calprotectin and capsule endoscopy for small-bowel Crohn's disease: a systematic review and meta-analysis.

there are no clarified non-invasive methods to evaluate small bowel inflammation in Crohn's disease. to evaluate the accuracy of fecal calprotectin and capsule endoscopy for the diagnosis of small bowel Crohn's disease and to predict relapse. a systematic literature search was performed for studies to diagnose and predict relapse of the disease with fecal calprotectin and capsule endoscopy. The relevant pooled data including sensitivity, specificity, diagnostic odds ratio, positive likelihood ratio, negative likelihood ratio and area under curve were calculated using Stata 14.0. twenty-one studies were included in the final analyses. The diagnostic accuracy of the disease and relapse were obtained for capsule endoscopy, with a pooled sensitivity of 0.90 and 0.82, specificity of 0.76 and 0.56, diagnostic odds ratio of 33 and 6, and area under curve of 0.92 and 0.82, respectively. Diagnostic accuracy of the disease was calculated for fecal calprotectin values of 50, 100 and 200 ug/g; the sensitivity values were 0.84, 0.66 and 0.45; specificity values were 0.49, 0.74 and 0.87; diagnostic odds ratio were 5, 5 and 5; and area under curve were 0.74, 0.76 and 0.75, respectively. A fecal calprotectin level of 100-140 ug/g for the prediction of relapse had a pooled sensitivity of 0.68, specificity of 0.91, diagnostic odds ratio of 21, and area under curve of 0.77. capsule endoscopy is effective in diagnosing small bowel Crohn's disease and predicting relapse. Fecal calprotectin is an accurate surrogate technique to diagnose small bowel inflammation in Crohn's disease. Furthermore, the best scenario for fecal calprotectin is the prediction of relapse.

Quadratic convergence of Levenberg-Marquardt method for general nonlinear inverse problems with two parameters

In this paper, we shall present the quadratic convergence of Levenberg-Marquardt (L-M) method for solving general ill-posed nonlinear inverse problems including two parameters to be identified. As the Tikhonov regularized minimizations are non-convex due to the nonlinearity of the nonlinear inverse problems, the L-M method transforms them into convex minimizations. The quadratic convergence of the L-M method is rigorously demonstrated under some basic assumptions. An application of the L-M method for the simultaneous identification of the Robin coefficient and heat flux in an elliptic system is presented, and the surrogate functional technique is introduced to solve the convex but still strongly ill-conditioned minimizations, resulting in an explicit minimizer of the minimization at each L-M iteration. Numerical experiments are presented to illustrate the efficiency and accuracy of the proposed algorithm.

A Python surrogate modeling framework with derivatives

The surrogate modeling toolbox (SMT) is an open-source Python package that contains a collection of surrogate modeling methods, sampling techniques, and benchmarking functions. This package provides a library of surrogate models that is simple to use and facilitates the implementation of additional methods. SMT is different from existing surrogate modeling libraries because of its emphasis on derivatives, including training derivatives used for gradient-enhanced modeling, prediction derivatives, and derivatives with respect to training data. It also includes unique surrogate models: kriging by partial least-squares reduction, which scales well with the number of inputs; and energy-minimizing spline interpolation, which scales well with the number of training points. The efficiency and effectiveness of SMT are demonstrated through a series of examples. SMT is documented using custom tools for embedding automatically tested code and dynamically generated plots to produce high-quality user guides with minimal effort from contributors. SMT is maintained in a public version control repository.11https://github.com/SMTorg/SMT.

Nonlinear and Non-Stationary Detection for Measured Dynamic Signal from Bridge Structure Based on Adaptive Decomposition and Multiscale Recurrence Analysis

To test the nonlinearity and non-stationarity of measured dynamic signals from a bridge structure with high-level noise and dense modal characteristics, a method that combines the adaptive signal decomposition with the recurrence analysis is proposed to solve the difficulty of testing nonlinearity and non-stationarity of bridge structure signals. A novel white noise assistance and cluster analysis are introduced to the ensemble empirical mode decomposition to alleviate mode-mixing issues and generate single-mode intrinsic mode functions. Combining the hypothesis-testing scheme of nonstationary and nonlinear synchronization and surrogate techniques, a data-driven recurrence quantification analysis method is proposed and a novel recurrence quantification measure pairs are set up. To demonstrate the efficacy of the proposed methodology, complex signals, which are collected from a carefully instrumented model of a cable-stayed bridge, are utilized as the basis for comparing with traditional nonlinear and non-stationary test methods. Results show that the proposed multiscale recurrence method is feasible and effective for applications to a nonlinear and non-stationary test for real complex civil structures.

Applicability and comparison of surrogate techniques for modeling of selected heating problems

Possibilities of using surrogate techniques for modeling selected strongly nonlinear coupled problems of heating are evaluated. The main purpose is to significantly reduce the computing time in the case of computations of many variants of a given task by the finite element method on the condition of obtaining results of a still acceptable accuracy. Frequently used surrogate techniques (based on Kriging, neural network etc.) are tested on a particular problem of induction-assisted laser welding that represents a very complicated 3D problem. Here, the most important output quantities are the internal structure of weld (decisive for its mechanical parameters) and its depth that depend on a number of input parameters (power of laser beam, velocity of shift of the welded parts, overall geometry and material properties etc.) and must be known before the process of welding itself. The paper presents both full model of this process and considered surrogate algorithms, and compares the results obtained. It is shown that a careful selection of the surrogate technique together with suitable choice of its input data is very beneficial and may result in high savings in design of the process. Implementation performance and suitability of particular techniques of this kind are also evaluated.

A note on using centroid weights in additive multi-criteria decision analysis

In this short communication, the authors challenge the comparison experiments made by several authors regarding the performances of surrogate weights frequently used in additive multicriteria decision analysis. Statistical tests compare the rankings obtained, either with random ‘true weights’ from a given weight simplex, or with the considered surrogate techniques. It is shown that the performance ranking of the surrogate techniques entirely depends on the way the weight simplex is defined. As an example, the rank-order-centroid weights, often found in this way to be the most performant ones, are only first-ranked when a point-allocation elicitation approach is adopted.

Optimization design on the riser system of next generation subsea production system with the assistance of DOE and surrogate model techniques

The Next Generation Subsea Production System (NextGen SPS) is a new concept for petroleum development in ultra-deep water (UDW) areas. It can improve the structural performance of riser as well as provide several operational benefits to subsurface well completion (SWC) equipment. The design of NextGen SPS’s riser system which includes rigid riser and flexible jumper—like the free standing hybrid riser (FSHR), is a very important issue for the definition of NextGen SPS. This paper details an optimization design on the NextGen SPS’s riser system, with the assistance of the design of experiments (DOE) and surrogate model techniques. The optimization model pertaining to riser system is formulated firstly. The DOE is a statistical technique that guides a sensitive study on the behavior of the riser system before the optimization analysis. Structural responses are obtained by the fully coupled methodology. Through such a preliminary study, the effective contribution of each design variable at the riser performance will be known and some general conclusive remarks will be obtained. Based on the DOE results, design variables are screened to improve the efficiency of optimization process. Particle swarm optimization (PSO) method is employed to conduct the optimization analysis. In this analysis, surrogate models, which are developed by back propagation neural network (BPNN), replace the time consuming dynamic analysis to predict structural responses. Latin hypercube sampling (LHS) method is adopted to generate training sample and testing sample for the BPNN. NextGen SPS that operates at a depth of 3000 m is used as the case for this investigation. The efficiency of optimization design is improved by DOE and surrogate techniques, and a reduction of approximately 46% for the riser system cost is achieved. The obtained conclusions have applicability in reference to the engineering design of FSHR and the study procedure will provide reference for study on other new structure concept.

Safety benefits of arterials’ crash risk under connected and automated vehicles

This paper aims to investigate the safety impact of connected vehicles and connected vehicles with the lower level of automation features under vehicle-to-vehicle (V2V) and infrastructure-to-vehicle (I2V) communication technologies. Examining the lower level of automation is more realistic in the foreseeable future. This study considered two automated features such as automated braking and lane keeping assistance which are widely available in the market with low penetration rates. Driving behavior of connected vehicles (CV) and connected vehicles lower level automation (CVLLA) were modeled in the C++ programming language with considering realistic car following models in VISSIM. To this end, safety impact on both segment and intersection crash risks were explored through surrogate safety assessment techniques under various market penetration rates (MPRs). Segment crash risk was analyzed based on both time proximity-based and evasive action-based surrogate measures of safety: time exposed time-to-collision (TET), time integrated time-to-collision (TIT), time exposed rear-end crash risk index (TERCRI), lane changing conflicts (LCC), and number of critical jerks (NCJ). However, the intersection crash risk was evaluated through the number of conflicts extracted from microsimulation (VISSIM) using the Surrogate Safety Assessment Model (SSAM). A logistic regression model was also developed to quantify the crash risk in terms of observed conflicts obtained in the intersection influence areas. The results suggest that both CV and CVLLA reduce segment crash risk significantly in terms of the five surrogate measures of safety. Furthermore, the logistic regression results clearly showed that both CV and CVLLA have lower intersection crash risks compared to the base scenario. In terms of both segment and intersection crash risks, CVLLA significantly outperforms CV when MPRs are 60% or higher. Thus, the results indicate a significant safety improvement resulting from implementing CV and CVLLA technologies at both segments and intersections on arterials.

Nonlinear characterization and complexity analysis of cardiotocographic examinations using entropy measures

The nonlinear analysis of biological time series provides new possibilities to improve computer aided diagnostic systems, traditionally based on linear techniques. The cardiotocography (CTG) examination records simultaneously the fetal heart rate (FHR) and the maternal uterine contractions. This paper shows, at first, that both signals present nonlinear components based on the surrogate data analysis technique and exploratory data analysis with the return (lag) plot. After that, a nonlinear complexity analysis is proposed considering two databases, intrapartum (CTG-I) and antepartum (CTG-A) with previously identified normal and suspicious/pathological groups. Approximate Entropy (ApEn) and Sample Entropy (SampEn), which are signal complexity measures, are calculated. The results show that low entropy values are found when the whole examination is considered, $$\hbox {ApEn}=0.3244\pm 0.1078$$ and $$\hbox {SampEn}=0.2351\pm 0.0758$$ ($$\hbox {average}\pm \hbox {standard}$$ deviation). Besides, no significant difference was found between the normal ($$\hbox {ApEn}=0.3366\pm 0.1250$$ and $$\hbox {SampEn}=0.2532\pm 0.0818$$) and suspicious/pathological ($$\hbox {ApEn}=0.3420\pm 0.1220$$ and $$\hbox {SampEn}=0.2457\pm 0.0850$$) groups for the CTG-A database. For a better analysis, this work proposes a windowed entropy calculation considering 5-min window. The windowed entropies presented higher average values ($$\hbox {ApEn}=0.6505\pm 0.2301$$ and $$\hbox {SampEn}=0.5290\pm 0.1188$$) for the CTG-A and ($$\hbox {ApEn}=0.5611\pm 0.1970$$ and $$\hbox {SampEn}=0.4909\pm 0.1782$$) for the CTG-I. The changes during specific long-term events show that entropy can be considered as a first-level indicator for strong FHR decelerations ($$\hbox {ApEn}=0.1487\pm 0.0341$$ and $$\hbox {SampEn}=0.1289\pm 0.0301$$), FHR accelerations ($$\hbox {ApEn}=0.1830\pm 0.1078$$ and $$\hbox {SampEn}=0.1501\pm 0.0703$$) and also for pathological behavior such as sinusoidal FHR ($$\hbox {ApEn}=0.1808\pm 0.0445$$ and $$\hbox {SampEn}=0.1621\pm 0.0381$$).

Diastolic Stress Testing Along the Heart Failure Continuum.

This review summarizes recent developments highlighting the clinical utility of diastolic stress testing along the heart failure continuum. Invasive hemodynamic assessment of cardiac filling pressures during physiological stress is the gold-standard technique for unmasking diastolic dysfunction. Non-invasive surrogate techniques, such as Doppler ultrasound, have shown excellent agreement with invasive approaches and are now recommended by the American Society of Echocardiography and the European Association of Cardiovascular Imaging. While cycle exercise is often advocated, recent evidence supports the use of isometric handgrip as a viable alternative stressor. Diastolic stress testing is a powerful tool to enhance detection of diastolic dysfunction, is able to differentiate between cardiac and non-cardiac pathology, and should be incorporated into routine clinical assessment.

Parameter calibration in global soil carbon models using surrogate-based optimization

Abstract. Soil organic carbon (SOC) has a significant effect on carbon emissions and climate change. However, the current SOC prediction accuracy of most models is very low. Most evaluation studies indicate that the prediction error mainly comes from parameter uncertainties, which can be improved by parameter calibration. Data assimilation techniques have been successfully employed for the parameter calibration of SOC models. However, data assimilation algorithms, such as the sampling-based Bayesian Markov chain Monte Carlo (MCMC), generally have high computation costs and are not appropriate for complex global land models. This study proposes a new parameter calibration method based on surrogate optimization techniques to improve the prediction accuracy of SOC. Experiments on three types of soil carbon cycle models, including the Community Land Model with the Carnegie–Ames–Stanford Approach biogeochemistry submodel (CLM-CASA') and two microbial models show that the surrogate-based optimization method is effective and efficient in terms of both accuracy and cost. Compared to predictions using the tuned parameter values through Bayesian MCMC, the root mean squared errors (RMSEs) between the predictions using the calibrated parameter values with surrogate-base optimization and the observations could be reduced by up to 12 % for different SOC models. Meanwhile, the corresponding computational cost is lower than other global optimization algorithms.

Simulation-driven formulation of transportation fuel surrogates

An alternative way to formulate transportation fuel surrogates using model predictions of gas-phase combustion targets is explored and compared to conventional approaches. Given a selection of individual fuel components, a multi-component chemical mechanism describing their oxidation kinetics, and a database of experimental measurements for key combustion quantities such as ignition delay times and laminar burning velocities, the optimal fractional amount of each fuel is determined as the one yielding the smallest error between experiments and model predictions. Using a previously studied three-component jet fuel surrogate containing n-dodecane, methyl-cyclohexane, and m-xylene as a case study, this article investigates in a systematic manner how the surrogate composition affects model predictions for ignition delay time and laminar burning velocities over a wide range of temperature, pressure and stoichiometry conditions, and compares the results to existing surrogate formulation techniques, providing new insights on how to define surrogates for simulation purposes. Finally, an optimisation algorithm is described to accelerate the identification of optimal surrogate compositions in this context.

On the Global Sensitivity Analysis Methods in Geotechnical Engineering: A Comparative Study on a Rock Salt Energy Storage

The large number of input factors involved in a sophisticated geotechnical computational model is a challenge in the concept of probabilistic analysis. In the context of model calibration and validation, conducting a sensitivity analysis is substantial as a first step. Sensitivity analysis techniques can determine the key factors which govern the system responses. In this paper, three commonly used sensitivity analysis methods are implemented on a sophisticated geotechnical problem. The computational model of a compressed air energy storage, mined in a rock salt formation, includes many input parameters, each with large amount of uncertainties. Sensitivity measures of different variables involved in the mechanical response of the cavern are computed by different global sensitivity methods, namely, Sobol/Saltelli, Random Balance Design, and Elementary Effect method. Since performing sensitivity analysis requires a large number of model evaluations, the concept of surrogate modelling is utilised to decrease the computational burden. In the following, the accuracy levels of various surrogate techniques are compared. In addition, a comparative study on the applied sensitivity analysis methods shows that the applied sensitivity analysis techniques provide identical parameter importance rankings, although some may also give more information about the system behaviour.

Adaptive iterative generalized demodulation for nonstationary complex signal analysis: Principle and application in rotating machinery fault diagnosis

Abstract Effective identification of signal frequency contents and their time variability is a key to success in rotating machinery fault diagnosis under nonstationary conditions. Fine time-frequency resolution and free from both inner and outer interferences are necessary to achieve this purpose. Iterative generalized demodulation (IGD) can separate a nonstationary multi-component signal into constituent mono-components, and derive quality time-frequency representation based on Hilbert spectrum of each mono-component, thus offering an effective approach to nonstationary complex signal analysis. Nevertheless, it requires prior expertise knowledge about signal structure to ascertain true constituent components and construct proper demodulation phase functions. This leads to a major difficulty in real signal analysis tasks, where expertise knowledge is usually unavailable and signal time-frequency structure identification via visual observation is susceptible to noise interferences. To address this issue, the capability of surrogate test technique in recognizing true signal components is exploited, and thereby an adaptive iterative generalized demodulation (AIGD) is proposed. Proper demodulation phase functions corresponding to constituent components are derived accordingly, and Hilbert spectra of all constituent mono-components are superposed to generate time-frequency representation (TFR). This method features good merits, such as fine time-frequency resolution and free of both inner and outer interferences. Additionally, it neither relies on prior expertise knowledge about signals, nor needs to construct any basis function. It is highly adaptive to reveal the frequency contents and track their time variability of a signal, and provides an effective approach to nonstationary complex multi-component signal analysis. It is illustrated and validated via numerical simulations, lab experiments of a planetary gearbox and rolling bearings, and on-site tests of a hydraulic turbine in a hydraulic power plant.

Estimation of laser weld parameters using surrogate modelling technique

Abstract A surrogate technique based on Gaussian Process (GP) is used for predicting quality of laser in case of laser welding process that may be supported by induction preheating. FEM-based solution of the problem is computationally expensive because it combines computation of 3D coupled nonlinear electromagnetic and temperature fields. The quality of laser welds is quantified with weld depth, which depends on a number of input parameters. The paper deals with two of them —thickness of the welded steel sheet and power of the laser beam. First, selected FEM simulations allow finding data describing the dependency between the two input parameters and weld depth. These data allow creating a surrogate model that is able to predict weld depth at any point close to the points where the results are known. The principal goal is to essentially save the computational time. The surrogate model also allows estimating prediction plausibility and running the full FEM calculation in cases where the prediction is not sufficiently accurate. The methodology is illustrated with a typical example whose results are discussed.

Ensemble of surrogate based global optimization methods using hierarchical design space reduction

For computationally expensive black-box problems, surrogate models are widely employed to reduce the needed computation time and efforts during the search of the global optimum. However, the construction of an effective surrogate model over a large design space remains a challenge in many cases. In this work, a new global optimization method using an ensemble of surrogates and hierarchical design space reduction is proposed to deal with the optimization problems with computation-intensive, black-box objective functions. During the search, an ensemble of three representative surrogate techniques with optimized weight factors is used for selecting promising sample points, narrowing down space exploration and identifying the global optimum. The design space is classified into: Original Global Space (OGS), Promising Joint Space (PJS), Important Local Space (ILS), using the newly proposed hierarchical design space reduction (HSR). Tested using eighteen representative benchmark and two engineering design optimization problems, the newly proposed global optimization method shows improved capability in identifying promising search area and reducing design space, and superior search efficiency and robustness in identifying the global optimum.

Evaluation of Esophageal Mucosal Integrity in Patients with Gastroesophageal Reflux Disease

Background: Impaired esophageal mucosal integrity plays a role in causing symptoms of gastroesophageal reflux disease (GERD). Recently, the assessment of esophageal baseline impedance (BI) using the multichannel intraluminal impedance-pH (MII-pH) test was suggested as a surrogate technique for the study of esophageal mucosal integrity and was reported to be useful in distinguishing GERD from non-GERD. However, measuring BI requires a 24-h testing period, is complicated, and causes considerable patient discomfort. Summary: Recently, endoscopy-guided catheters that can measure mucosal impedance (MI) and mucosal admittance (MA), which is the inverse of impedance, were developed, and their usefulness in measuring MI and MA for the diagnosis of GERD has been reported. In these studies, esophageal MI values were significantly lower in patients with GERD than in those without GERD. In contrast, esophageal MA was significantly higher in patients with GERD than in those without. Furthermore, we reported that MA is inversely correlated with BI and correlated with acid exposure time. Key Messages: Endoscopy-guided real-time measurement of MI and MA may allow the estimation of mucosal integrity and may be a useful diagnostic tool for patients with GERD in a manner similar to 24-h MII-pH monitoring.

Passive acoustic measurement of bedload grain size distribution using self-generated noise

Abstract. Monitoring sediment transport processes in rivers is of particular interest to engineers and scientists to assess the stability of rivers and hydraulic structures. Various methods for sediment transport process description were proposed using conventional or surrogate measurement techniques. This paper addresses the topic of the passive acoustic monitoring of bedload transport in rivers and especially the estimation of the bedload grain size distribution from self-generated noise. It discusses the feasibility of linking the acoustic signal spectrum shape to bedload grain sizes involved in elastic impacts with the river bed treated as a massive slab. Bedload grain size distribution is estimated by a regularized algebraic inversion scheme fed with the power spectrum density of river noise estimated from one hydrophone. The inversion methodology relies upon a physical model that predicts the acoustic field generated by the collision between rigid bodies. Here we proposed an analytic model of the acoustic energy spectrum generated by the impacts between a sphere and a slab. The proposed model computes the power spectral density of bedload noise using a linear system of analytic energy spectra weighted by the grain size distribution. The algebraic system of equations is then solved by least square optimization and solution regularization methods. The result of inversion leads directly to the estimation of the bedload grain size distribution. The inversion method was applied to real acoustic data from passive acoustics experiments realized on the Isère River, in France. The inversion of in situ measured spectra reveals good estimations of grain size distribution, fairly close to what was estimated by physical sampling instruments. These results illustrate the potential of the hydrophone technique to be used as a standalone method that could ensure high spatial and temporal resolution measurements for sediment transport in rivers.

Fractal analysis - a surrogate technique for material characterization

Fractal analysis - a surrogate technique for material characterization

An optimized strategy for using asymptotic sampling for reliability analysis

Asymptotic sampling is a recently developed method for calculating small failure probabilities of high-dimensional problems. It is based on Monte Carlo simulation, however, extremely small probabilities can be estimated with reasonable computational effort. The present paper presents an optimal strategy for using asymptotic sampling. In particular, it is explained in which cases the accuracy of the results can be increased by applying low-discrepancy sampling. A new surrogate technique is presented for obtaining accurate results also in cases in which low-discrepancy sampling is not applicable effectually. Furthermore, an extension of the method is shown for estimating threshold exceedance probabilities.