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Aiming to address soft sensing model degradation under changing working conditions, and to accommodate dynamic, nonlinear, and multimodal data characteristics, this paper proposes a nonlinear dynamic transfer soft sensor algorithm. The approach leverages time-delay data augmentation to capture dynamics and projects the augmented data into a latent space for constructing a nonlinear regression model. Two regular terms, distribution alignment regularity and first-order difference regularity, are introduced during data projection to address data distribution disparities. Laplace regularity is incorporated into the nonlinear regression model to ensure geometric structure preservation. The final optimization objective is formulated within the framework of partial least squares, and hyperparameters are determined using Bayesian optimization. The effectiveness of the proposed algorithm is demonstrated through experiments on three public datasets. • Adaptive soft sensor combines unsupervised domain adaptation and dynamic PLS for variable conditions. • Leverages time-delay data augmentation to capture dynamics and constructs a nonlinear regression model in a latent space. • Introduces regular terms for distribution alignment and first-order differences, and incorporates Laplace regularity for geometric structure preservation. • Optimization within partial least squares framework, hyperparameters tuned by Bayesian optimization. [ABSTRACT FROM AUTHOR]

This paper addresses the design problem of outpatient chemotherapy oncology departments. The objective is to identify the most effective resource configuration (e.g. number of chairs, number of oncologists) to reduce the patient waiting time and increase the average number of daily treatments. A stochastic simulation model was developed to emulate the patient flow in different oncology unit configurations. Then, it was used to generate the expected results of an extended design of experiments. To evaluate the impact of the different resources on the performance of the oncology departments the ANOVA analysis was employed. Besides, a Pareto multi-objective analysis was carried out to further support any decision-maker in the selection of the most suitable resource configuration. Particularly, to make easier the decision-making process, a comprehensive table chart was elaborated. Finally, a multiple non-linear regression model was generated in order to empower the health managers in easily assessing the performance of any real ward configuration. The analysis of results pointed out that the two mentioned objectives are rather conflicting. Furthermore, it proved that a higher number of resources not necessarily implies a significant improvement in the performance measures. [ABSTRACT FROM AUTHOR]

The main drawback of the typical fuzzy least squares approach is that the resulting fuzzy regression model is linear, and the model's accuracy decreases with the increases in the magnitudes and the number of independent variables. Some nonparametric methods, such as the kernel regression method, have been proposed to overcome these drawbacks. In this paper, we derive a novel fuzzy nonparametric regression method. A convex nonparametric least squares approach (CNLS) is employed for the fuzzy regression models with crisp input fuzzy output data (Fuzzy-CNLS). Like Diamond's fuzzy least squares method, the fuzzy regression model is divided into three submodels, the Center, the Left endpoint, and the Right endpoint. We employ CNLS for each sub-model. Hence, the resulting Fuzzy-CNLS regression model consists of three sets of CNLS results (hyperplanes) for each sub-model, representing a fuzzy nonlinear regression model. One of the advantages of the original CNLS over ordinary least squares (OLS) is that the coefficient of determination of CNLS must be greater than that of OLS. Hence, the goodness of fit of Fuzzy-CNLS is better than the fuzzy least squares methods. On the other hand, CNLS can accommodate the concavity or convexity constraints for the regression functions following the concavity or convexity pattern, respectively. With these shape (concavity or convexity) constraints, it is considered that Fuzzy-CNLS is less sensitive to outliers. A similarity-distance measure is used to select the shape constraints and to evaluate the performance of Fuzzy-CNLS. An illustrative example and an application example are given. The numerical results show that Fuzzy-CNLS is better than Diamond's least squares method and fuzzy least absolute linear regression method in terms of the similarity measure. [ABSTRACT FROM AUTHOR]

Oil price forecasting is one of the most challenging issues since it is noisy, non-stationary, and chaotic. In this paper, we design a Bayesian Nonlinear Quantile method consisting of a Threshold Improved model and an Adaptive MCMC model to improve predicting performance. Specifically, the threshold improve model is introduced to solve the problems caused by the completely asymmetric distribution, and the Adaptive MCMC model is used to get the optimal threshold. Besides, the two-stage framework is applied to improve traditional methods' performance, including the Indirect GARCH model and Asymmetric Slope model. The experimental results show that our approach provides a promising alternative to oil price prediction, and the framework also improve the performance of the traditional methods. The contribution of this paper is to improve the accuracy of the oil price forecasting model, and the framework applies to other energy prices as well. [ABSTRACT FROM AUTHOR]

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The inclusion of the geographic information into regression models is becoming increasingly popular due to the increased availability of corresponding geo-referenced data. In this paper, a novel framework for combining spatio-temporal regression techniques and artificial neural network (ANN) regression models is presented. The key idea is to use the universal approximation property of the ANN function to account for an arbitrary spatial pattern in the dependent variable by including geographic coordinate variables as regressors. Moreover, the implicit location-specific effects are allowed to exhibit arbitrary interaction effects with other regressors such as a time variable. In contrast to other machine learning approaches for spatio-temporal data, the likelihood framework of the classic (linear) spatio-temporal regression model is preserved. This allows, inter alia, for inference regarding marginal effects and associated confidence. The framework also allows for non-normal conditional distributions, conditional spatial correlation, arbitrary trend and seasonality. These features are demonstrated in a simulation section and two data examples, using linear spatio-temporal models as a reference. [ABSTRACT FROM AUTHOR]

The construction of noise maps is of great significance for the management and control of urban noise and the protection of residents' physical and mental health. The European Noise Directive recommends using computational methods to construct strategic noise maps when possible. The current noise maps based on model calculation rely on complex noise emission and propagation models, and their huge number of regional grids needs to consume a lot of calculation time. This seriously restricts the update efficiency of noise maps, making it difficult to realize large-scale application and real-time dynamic update of noise maps. In order to improve the computational efficiency of noise maps, based on big data-driven technology, this paper combines the traditional CNOSSOS-EU noise emission modeling method with the multivariate nonlinear regression modeling method, and proposes an efficient calculation method of large-region dynamic traffic noise maps based on hybrid modeling method. First, this paper constructs the (daily and nightly) noise contribution prediction models of road sources with different classes, considering the daily and nightly periods and different urban road classes. Parameters of the proposed model are evaluated by using the multivariate nonlinear regression method to replace the complex nonlinear acoustic mechanism modeling. On this basis, in order to further improve the computational efficiency, noise contribution attenuations of the constructed models are parameterized and evaluated quantitatively. And then, the database containing the index table of the road noise sources-receivers and the corresponding noise contribution attenuations is constructed. The experimental results show that compared with the traditional calculation methods based on acoustic mechanism model, the noise map calculation method based on hybrid model proposed in this paper greatly reduces the model computations of noise map, improves the efficiency of noise mapping. It will provide technical support for constructing dynamic noise maps of large urban regions. Overall framework diagram of the proposed traffic noise mapping method. The schematic diagram of efficient update of the constructed dynamic noise map. [Display omitted] • A method of large-region traffic noise maps based on hybrid modeling is proposed. • The method is independent of acoustic calculation software and generous monitors. • The sound pressure level contribution prediction models are developed. • The method is applicable to hourly dynamic update of large-region noise maps. • The method is flexible, expandable and with great portability. [ABSTRACT FROM AUTHOR]

The construction of noise maps is of great significance for the management and control of urban noise and the protection of residents' physical and mental health. The European Noise Directive recommends using computational methods to construct strategic noise maps when possible. The current noise maps based on model calculation rely on complex noise emission and propagation models, and their huge number of regional grids needs to consume a lot of calculation time. This seriously restricts the update efficiency of noise maps, making it difficult to realize large-scale application and real-time dynamic update of noise maps. In order to improve the computational efficiency of noise maps, based on big data-driven technology, this paper combines the traditional CNOSSOS-EU noise emission modeling method with the multivariate nonlinear regression modeling method, and proposes an efficient calculation method of large-region dynamic traffic noise maps based on hybrid modeling method. First, this paper constructs the (daily and nightly) noise contribution prediction models of road sources with different classes, considering the daily and nightly periods and different urban road classes. Parameters of the proposed model are evaluated by using the multivariate nonlinear regression method to replace the complex nonlinear acoustic mechanism modeling. On this basis, in order to further improve the computational efficiency, noise contribution attenuations of the constructed models are parameterized and evaluated quantitatively. And then, the database containing the index table of the road noise sources-receivers and the corresponding noise contribution attenuations is constructed. The experimental results show that compared with the traditional calculation methods based on acoustic mechanism model, the noise map calculation method based on hybrid model proposed in this paper greatly reduces the model computations of noise map, improves the efficiency of noise mapping. It will provide technical support for constructing dynamic noise maps of large urban regions. Overall framework diagram of the proposed traffic noise mapping method. The schematic diagram of efficient update of the constructed dynamic noise map. [Display omitted] • A method of large-region traffic noise maps based on hybrid modeling is proposed. • The method is independent of acoustic calculation software and generous monitors. • The sound pressure level contribution prediction models are developed. • The method is applicable to hourly dynamic update of large-region noise maps. • The method is flexible, expandable and with great portability. [ABSTRACT FROM AUTHOR]

One of the most versatile and powerful algorithms for the identification of nonlinear dynamical systems is the NARMAX (Nonlinear Auto-regressive Moving Average with eXogenous inputs) approach. The model represents the current output of a system by a nonlinear regression on past inputs and outputs and can also incorporate a nonlinear noise model in the most general case. In recent papers, one of the authors introduced a NARX (no noise model) formulation based on Gaussian Process (GP) regression and derived the corresponding expressions for Higher-order Frequency Response Functions (HFRFs). This paper extends the theory for the GP-NARX framework by providing a means of converting the GP prediction bounds in the time domain into bounds on the HFRFs. The approach is demonstrated on the Duffing oscillator. [ABSTRACT FROM AUTHOR]

This paper presents the investigation about a problem situation that Electric Distributor Companies are facing in Chile resulting from transit accidents. The number of vehicle crashes to power distribution poles and street lighting has grown. This situation causes discomfort to citizen and mainly to the neighbors due to power cuts and even on occasion, losses of human lives because of the accident that have occurred. Based on previous research, the accidents are not random nor chance dependent, but the majority of transit accident follow parameters or variables from the scenery where it occurs. In order to analyze the variables and the degree this variables affect the accidents, a model of Perceptron and Multipercetron Artificial Neural Networks and a Multiple Nonlinear Regression model are proposed. An empirical study was made; collecting data from a distributor company and from Chilean National Traffic Safety Commission, where the more frequent variables involved in accidents were determined to develop the mentioned models. These variables were investigated and also their influence on the occurrence of vehicle crashes to power distribution poles could be confirmed. With this data, the prediction of post crashes was developed, where through the application of the neural network and multiple nonlinear regression, revealed 95.7% of acceptable predictions. This study will bring benefits to power distribution companies considering a risk index in the streets, based on the number of crashes of poles per street; this will allow optimal decisions in future electrical distribution projects avoiding critical areas. [ABSTRACT FROM AUTHOR]

The present work shows the first application of the PC-transport approach in the context of Large Eddy Simulation (LES) of turbulent combustion. Detailed kinetic mechanisms, together with advanced computational tools, are needed to advance our knowledge of turbulent reacting systems. However, the cost related to high-fidelity simulations of turbulent reacting flows is still prohibitive for realistic configurations. Therefore, there is a need to reduce the complexity of the problem by identifying low-dimensional manifolds. To this end, the potential offered by Principal Component Analysis (PCA) in parameterizing the thermo-chemical state-space is very appealing. The present paper extends the PC-transport framework to three-dimensional Large Eddy Simulation (LES), coupling PCA with Gaussian Process Regression (GPR). To demonstrate the potential of the method, LES simulations of Sandia flames D, E and F are shown. Results show the great potential of the PC-GPR model, as indicated by the accuracy of the simulation results when compared with experimental data, using only 2 principal components. The sensitivity to the kinetic mechanism and subgrid closure model is also investigated. [ABSTRACT FROM AUTHOR]

This paper studies the relationship between financial development and economic growth in a large sample of developing, emerging and advanced economies over the recent period. Estimation results based on various nonlinear threshold regression models do not confirm the too-much-finance-is-bad hypothesis. We cannot indeed identify a tipping point beyond which financial development has a clear negative relation to economic development. Our results also show that banking and market finance are complementary. The positive effect of bank credit on growth is larger in stock markets that are deeper. But the thresholds above which complementarity kicks in are rather low level. Finally, the effects of bank and market finance do not seem to depend on economic development and trade openness. [ABSTRACT FROM AUTHOR]

Circular hollow sections (CHS) are widely used in construction, particularly in space structures. This paper aims at investigating the feasibility of employing a machine leaning approach to predict the axial strength of longitudinal branch plate-to-CHS connections subjected to branch loadings. Using nonlinear finite element analysis, numerical models are developed for the connections subjected to tensile or compressive branch loading. The difference between the load–displacement responses for the connections under compressive and under tensile loading is established. Furthermore, the influence of various geometric and material parameters on the strength is investigated in depth. Using the results, two different strength models for the prediction of both tensile and compressive strength of longitudinal branch plate-to-CHS T-connections are developed. One prediction model is attained using traditional nonlinear regression analysis, and the other model is attained through symbolic regression. Finally, theoretical predictions are compared with numerical results, and strengths predicted with current design methodologies. [ABSTRACT FROM AUTHOR]

Profile monitoring is a useful technique in statistical process control used when quality of the product or process is represented by a function over a time period. This function represents the relationship between a response variable and one or more explanatory variables. Most existing control charts for monitoring profiles are based on the assumption that the observations within each profile are independent of each other, which is often violated in practice. Sometimes there are one or more outliers in each profile which lead to poor statistical performance of tile control chart. This paper focuses on Phase II monitoring of a simple linear profile with autocorrelation within profile data in tile presence of outliers. In this paper, we propose a new combined control chart based on tile robust Holt-Winter model to decrease tile effect of outliers. We first evaluate the effect of outliers on tile performance of tile proposed combined control chart. Then, we apply robust Holt-Winter and design a robust combined control chart to overcome tile effect of outliers. The performance of tile proposed robust Holt-Winter control chart is evaluated through extensive simulation studies. The results show that tile proposed robust control chart performs well. [ABSTRACT FROM AUTHOR]

High-quality data are the precondition for analyzing and using statistics and for guaranteeing the value of the data. In this paper, the Iterative data quality management system is proposed. The methodology consists of two methods developed by the authors - the Iterative method for the reducing the impact of outlying data points in 2015 and the Data Quality Scale in 2018. The novelty of the Iterative method for the reducing the impact of outliers is the following: an iterative approach for determining the outlying data points is proposed; outliers are determined considering the impact of conjoined factors; estimation of weight coefficients of the outliers and estimation of the total measurement error of the nonlinear regression model is carried out. The Iterative method received the Young Statistician Prize of the International Association for Official Statistics (IAOS) in 2015. The Data Quality Scale has good expansibility and adaptability as makes it possible to evaluate the quality of data at various levels of detail: at indicators' level, at the level of dimensions, and to determine the entire quality of data. The Data Quality Scale gives an opportunity to identify certain shortcomings of the quality of statistical data and to develop proposals to improve the quality of the data. The research results enrich the theoretical scope of the statistical data quality and lay a solid foundation for the future by establishing an assessment approach and studying evaluation algorithms. [ABSTRACT FROM AUTHOR]

• Analyzing impact of social reaction to disaster situations on electricity consumption. • Electricity consumption prediction using a multiple non-linear regression model. • Robust-stochastic optimization utilization for system resilience enhancement. • Introducing a novel resilience criterion in line with resilience assessment of MGs. In this paper, a new multi-objective two-stage robust-stochastic (MOTSRS) optimization approach for assessing microgrids and distribution system resilience is proposed. A novel approach based on deep learning is presented to model social behavior, specifically Social Reactions to Disasters (SRDs), in assessing microgrid and distribution system resilience. It is initially demonstrated that the proposed robust-stochastic approach is efficient for assessing the resilience of microgrids and distribution systems. A new resilience criterion is also proposed to be used for sensitivity analyses at the conservative level of the model. Subsequently, the effect of SRDs on system resilience is investigated through a supervised deep regression method. First, a large dataset is generated through a proposed nested Monte Carlo simulation. A numerical feature and five binary social behavior features are included in the dataset. Next, a feedforward neural network is utilized to predict the load during both normal and disaster circumstances based on the six features. The proposed supervised deep regression model with mean squared errors between 2e-5 and 2e-6 is capable of predicting electrical load during normal and disaster events. It is concluded that the resilience metrics of MGs, as well as expected operation costs and regret values, are influenced by the SRD effect. [ABSTRACT FROM AUTHOR]

Reliable relative permeability curves of polymer flooding are of great importance to the history matching, production prediction, and design of the injection and production plan. Currently, the relative permeability curves of polymer flooding are obtained mainly by the steady-state, nonsteady-state, and pore-network methods. However, the steady-state method is extremely time-consuming and sometimes produces huge errors, while the nonsteady-state method suffers from its excessive assumptions and is incapable of capturing the effects of diffusion and adsorption. As for the pore-network method, its scale is very small, which leads to great size differences with the real core sample or the field. In this paper, an inversion method of relative permeability curves in polymer flooding is proposed by combining the polymer-flooding numerical-simulation model and the Levenberg-Marquardt (LM) algorithm. Because the polymer-flooding numerical- simulation model by far offers the most-complete characterization of the flowing mechanisms of polymer, the proposed method is able to capture the effects of polymer viscosity, residual resistance, diffusion, and adsorption on the relative permeability. The inversion method was then validated and applied to calculate the relative permeability curve from the experimental data of polymer flooding. Finally, the effects of the influencing factors on the inversion error were analyzed, through which the inversion-error-prediction model of the relative permeability curve was built by means of multivariable nonlinear regression. The results show that the water relative permeability in polymer flooding is still far less than that in waterflooding, although the residual resistance of the polymer has been considered in the numerical-simulation model. Moreover, the accuracy of the polymer parameters has great effect on that of the inversed relative permeability curve, and errors do occur in the inversed water relative permeability curve-the measurements of the polymer solution viscosity, residual resistance factor, inaccessible pore-volume (PV) fraction, or maximum adsorption concentration have errors. [ABSTRACT FROM AUTHOR]

Abstract In this paper, two correlations for oil formation volume factor (B o) for volatile oil reservoirs are developed using non-linear regression technique and genetic programming using commercial software. More than 1200 measured values obtained from PVT laboratory analyses of five representative volatile oil samples are selected under a wide range of reservoir conditions (temperature and pressure) and compositions. Matching of PVT experimental data with an equation of state (EOS) model using a commercial simulator (Eclipse Simulator), was achieved to generate the oil formation volume factor (B o). The obtained results of the B o as compared with the most common published correlations indicate that the new generated model has improved significantly the average absolute error for volatile oil fluids. The hit-rate (R 2) of the new non-linear regression correlation is 98.99% and the average absolute error (AAE) is 1.534% with standard deviation (SD) of 0.000372. Meanwhile, correlation generated by genetic programming gave R 2 of 99.96% and an AAE of 0.3252% with a SD of 0.00001584. The importance of the new correlation stems from the fact that it depends mainly on experimental field production data, besides having a wide range of applications especially when actual PVT laboratory data are scarce or incomplete. [ABSTRACT FROM AUTHOR]

Cosmetic mask is a popular skincare product widely accepted by the youth and female. Most cosmetic masks in the current market offer very few sizes to choose from, thus producing misfit masks with reduced wearing comfort and skincare functionality. This paper describes how to realize customized design of cosmetic masks using three-dimensional (3D) parametric face models derived from a large amount of scanned facial data. The parametric models approximate individual faces using a nonlinear regression model controlled by a set of facial parameters easy to be measured. They serve as effective reference geometry to conduct 3D mask design. A prototyping mask design system implementing the parametric modeling method demonstrates the customized design process. The system allows the user to construct the mask shape directly on 3D meshes of a face model by specifying inner and outer boundary curves. An automatic flattening function unfolds the trimmed meshes into a two-dimensional (2D) pattern with a reduced shape distortion. This research enhances the practical value of large-scale anthropometric data by realizing human centric design customization using cosmetic facial mask as an example. [ABSTRACT FROM AUTHOR]

The skid mark is valuable for accident reconstruction as it provides information about the braking behaviour drivers and the speed of heavy vehicles. However, despite its importance, there is currently no mathematical model available to estimate skidding distance (SD) as a function of vehicle characteristics and road conditions. This paper attempts to develop a non-linear regression model that is capable of reliably predicting the skidding distance of heavy vehicles under various road conditions and vehicle characteristics. To develop the regression model, huge data sets were derived from complex heavy vehicle multi-body dynamic simulation. An emergency braking simulation was conducted to examine the skidding distance of a heavy vehicle model subject to various Gross Vehicle Weight (GVW) and vehicle speeds, as well as the coefficient of friction of the road under wet and dry conditions. The results suggested that the skidding distance is significantly affected by Gross Vehicle Weight, speeds, and coefficient of friction of the road. The improved non-linear regression model provides a better prediction of the skidding distance than that of the conventional approach thus suitable to be employed as an alternative model for skidding distance of heavy vehicles in accident reconstruction. [ABSTRACT FROM AUTHOR]

Abstract: Estimating the time since discharge of a spent cartridge or a firearm can be useful in criminal situations involving firearms. The analysis of volatile gunshot residue remaining after shooting using solid-phase microextraction (SPME) followed by gas chromatography (GC) was proposed to meet this objective. However, current interpretative models suffer from several conceptual drawbacks which render them inadequate to assess the evidential value of a given measurement. This paper aims to fill this gap by proposing a logical approach based on the assessment of likelihood ratios. A probabilistic model was thus developed and applied to a hypothetical scenario where alternative hypotheses about the discharge time of a spent cartridge found on a crime scene were forwarded. In order to estimate the parameters required to implement this solution, a non-linear regression model was proposed and applied to real published data. The proposed approach proved to be a valuable method for interpreting aging-related data. [Copyright &y& Elsevier]

Abstract: Financial exploitation of forests comprises an important part of man activity. There are efforts being made to conserve the sustainable exploitation while simultaneously avoiding degradation of the environment. One tool used in these efforts is the modeling of tree features, such as total tree height, sawn-timber tree height, merchantable tree height, and total or sawn-timber tree volume, which yields an estimate of the forest in finance recoverable goods. Sustainable forest management design must be supported by the adjustment of computational techniques. The purpose of this paper is to assess a reliable modeling approach for estimating individual tree heights for the maturity of trees for logging through determining the applicability of different types of neural network models and identifying a neural network procedure for accurate estimation of these variables. These models serve as an alternative to the traditional regression approach. All types of model estimations are evaluated and compared in this paper. Back Propagation Artificial Neural Network (BPANN), Cascade Correlation Artificial Neural Network (CCANN), and Generalized Regression Neural Network (GRNN) models are developed to estimate individual tree heights for the logging of mature trees, such as sawn-timber height and merchantable height. The results reported in this research suggest that the selected BPANN and CCANN models are reliable and demonstrate their adequacy and potential for estimating sawn-timber and merchantable tree height. The results also illustrate that the CCANN models are superior to the BPANN and GRNN models and lead to higher estimation accuracy. Moreover, the NN models were found to be superior to the tested nonlinear regression models. [Copyright &y& Elsevier]

This paper considers the black-box approximation problem where the goal is to create a regression model using only empirical data without incorporating knowledge about the character of nonlinearity of the approximated function. This paper reports on ongoing work on a nonlinear regression methodology called IBHM which builds a model being a combination of weighted nonlinear components. The construction process is iterative and is based on correlation analysis. Due to its iterative nature, the methodology does not require a priori assumptions about the final model structure which greatly simplifies its usage. Correlation based learning becomes ineffective when the dynamics of the approximated function is too high. In this paper we introduce weighted correlation coefficients into the learning process. These coefficients work as a kind of a local filter and help overcome the problem. Proof of concept experiments are discussed to show how the method solves approximation tasks. A brief discussion about complexity is also conducted. [ABSTRACT FROM AUTHOR]

This paper presents a new extension of nonlinear regression models constructed by assuming the normal mean–variance mixture of Birnbaum–Saunders distribution for the unobserved error terms. A computationally analytical EM-type algorithm is developed for computing maximum likelihood estimates. The observed information matrix is derived for obtaining the asymptotic standard errors of parameter estimates. The practical utility of the methodology is illustrated through both simulated and real data sets. [ABSTRACT FROM AUTHOR]

Design of drilling fluids, spacers, cement slurries, and fracturing fluids is often done by trial and error in the laboratory. In the first step, the required properties of these fluids are categorized and then efforts will be started with a rough idea of the optimal composition. This first guess usually depends on the experience of the laboratory analyst or fluid engineer. Afterward, the trial-and-error testing starts, and it continues until the fluid design moves closer to the desired fluid criteria. There are several test data that would not be used in this method, and it is difficult to digest a large amount of information by the user. Trial and error could be time-consuming, very costly, and misleading. Today, there is a need for an intelligent system that uses all the available data (big data), even if the data sets are not close to the desired goal, and offers insights for fluid designs. This paper conducted a study on the application of machine-learning-based methodologies, including Gaussian-process regression (GPR) and artificial neural networks (ANNs), to reduce the costs of testing, integrate available experimental data, and eliminate the need for personnel supervision. These practical nonlinear-regression methods empower efficient and fast prediction tools that do not require including complex physics of the underlying system while integrating all available data from different sources. GPR, which is also known as Kriging in geostatistics literature, has exceptional advantages over traditional regression methods because it does not require a known form for regression function and also has the capability of determining the estimation error and the confidence interval. This machine-learning-based tool offers insights for intelligent fluid design and could reduce costs. [ABSTRACT FROM AUTHOR]

This paper is concerned with the fault prediction for the nonlinear stochastic system with incipient faults. Based on the particle filter and the reasonable assumption about the incipient faults, the modified fault estimation algorithm is proposed, and the system state is estimated simultaneously. According to the modified fault estimation, an intuitive fault detection strategy is introduced. Once each of the incipient fault is detected, the parameters of which are identified by a nonlinear regression method. Then, based on the estimated parameters, the future fault signal can be predicted. Finally, the effectiveness of the proposed method is verified by the simulations of the Three-tank system. [ABSTRACT FROM AUTHOR]

In a career spanning more than 50 years, George Arthur Frederick Seber has made significant contributions in many areas of statistics as well as foundational work in capture-recapture modeling. Seber was born in 1938 in Australia to New Zealand parents and moved back to New Zealand where he completed almost all of his schooling. After graduating with bachelors and masters degrees from the University of Auckland, Seber studied for his Ph.D. at Manchester on a Commonwealth Scholarship. In his first year, he was supervised by John Darroch with David Silvey taking over supervision for the final 18 months of the degree. Seber's Ph.D. thesis was remarkably fruitful leading to three papers in Biometrika, two in the Annals of Mathematical Statistics and one in the Journal of the Royal Statistical Society B. The topics covered were broad mixing foundational work on linear model theory with capture-recapture problems. His solution to the multi-sample single recapture problem anticipated the general solution to the open population problem that Seber published in 1965 concurrent with the work of George Jolly. Seber has been a prolific writer of books including classical texts on the linear model and nonlinear regression. His comprehensive book The Estimation of Animal Abundance appeared in two editions. He has recently completed tour books and is working on a book on open population capture recapture. Seber took up an invited personal Chair in Biometrics at the University of Otago in 1971, and then the foundational Chair in Statistics at the University of Auckland in 1973. Seber developed a remarkable statistics group built around people such as Jeffrey Hunter, Alan Lee, Alastair Scott, Chris Triggs and Chris Wild, all of whom went on to hold Chairs in Statistics and to head Departments of Statistics in New Zealand. Seber was elected a Fellow of the Royal Society of New Zealand (RSNZ) in 1997 and in 1999 was awarded the Hector Medal by the RSNZ. [ABSTRACT FROM AUTHOR]

In this paper a method for hybrid fuzzy regression for determining specific active power generation characteristic of hydro power plants is presented. The method uses the new definition of weighted fuzzy-arithmetic and the least-squares fitting criterion. The hybrid fuzzy least-squares nonlinear regression model is developed. A computer program is made in software package MATLAB to use this model, so graphical solutions can be obtained as a result. For verification of the proposed model, practical examples are realized. [ABSTRACT FROM AUTHOR]

Supercritical water oxidation (SCWO) has been recognized as one of the most promising technologies for the decomposition of a variety of wastes. It utilizes the unique characteristics of water above its critical point (374.15_C, 22.12 MPa) and the whole process and final products are environmentally innocuous. Kinetic analysis of waste destruction in SCWO is important to properly design the process and reactor. In this paper we focused on the treatment of synthetic sewage wastewater by SCWO. A batch reactor was employed to carry out the experiments at 663 K, 683 K, and 703 K and 35 wt.% hydrogen peroxide was used as an oxidant. Chemical Oxygen Demand (COD) measurement was performed on both initial feed and liquidphase products. The activation energy was determined to be 57.335 kJ mol-1 for the reduction of COD, when analyzed by nonlinear least squares technique. The comparison made between observed conversions and calculated values demonstrated favorable fit of the data provided by the estimated parameters. The proposed model was compared with those of other works treating municipal sewage with SCWO. It was found that the activation energy obtained by our experimental results is close to those reported by other studies. Differences in some parameters were possibly due to the different reaction conditions. [ABSTRACT FROM AUTHOR]

In this paper, thin-layer infrared drying behavior of dewatered municipal sewage sludge was systematically investigated. Drying experiments were conducted at drying temperatures from 65 to 135̊C and the thickness of sludge cake from 2 to 10 mm. Twenty thin-layer drying kinetic models were selected to model the drying process, and model parameters were evaluated by nonlinear regression method, of which, two different nonlinear error functions (R 2 , RSS) were examined to gauge the goodness-of-fit. The experimental results manifested that Hasibuan and Daud model is the best suitable model with highest R 2 values from 0.99951 to 0.99996 and lowest RSS values from 3.3×10 -4 to 0.00117 under our experimental conditions; drying rate increased with increase in drying temperature and decrease in the thickness of sludge cake; effective moisture diffusivity notably increased as drying temperature increased; activation energy for drying process was 34.9211 kJ/mol. [ABSTRACT FROM AUTHOR]

In this paper, we develop a new innovation diffusion model for two competing products, which allows us to evaluate the effect of competition both on the dynamics of within-product and cross-product word-of-mouth and on the definition of the residual market potential of each product. This model, which we call Lotka–Volterra model with churn, LVch, generalizes another model for competition, the unbalanced competition and regime change diachronic model (UCRCD), which assumes a common residual market and a delayed entrance for the second product. We compare the performance of these models in describing the competition between two blockbuster formats in the music industry, the compact cassette and the compact disc. In particular, we analyze the evolution of these technologies in the U.S. market for pre-recorded music, for which annual sales data are available from 1973 to 2012, and find that the LVch model outperforms the UCRCD. An interesting aspect of this application relies on the fact that there is a single product, the music album , which is commercialized in two different formats, so that competition arises between formats and not between two products in the same commercial category. [ABSTRACT FROM AUTHOR]

Accurate prediction of required energy is essential for efficient deployment of Unmanned Ground Vehicles (UGV). Typical UGV missions do not allow for the replenishment of power resources so mission survivability and overall mission success rate are reliant on accurate power prediction. The live prediction of resources required for a particular mission is therefore beneficial as it could allow for increased mission time and lessen the requirement of over provisioning power resources. Accurate power prediction has particular importance when considering critical missions where failure to complete atomic mission operations may lead to an unsafe situation. This paper presents a new approach to live energy prediction that considers the effects of weather conditions on off-road terrains based upon non-linear polynomial regression for prediction of mission energy consumption using live sensor data. The method is demonstrated and compared to existing methods using a simulation of a typical UGV mission. The mission simulation considers a UGV traversing a variety of terrain types in various weather conditions. The experimental results show a significant improvement in energy prediction compared to existing approaches and demonstrates the success of forward prediction using non-linear terrain/weather/energy consumption models. [ABSTRACT FROM AUTHOR]

The 3TP (three time points) method has been proposed for producing high-resolution pseudo-coloured maps related to the angiogenic activity in breast DCE-MRI. In the original formulation of the method, the three time-points have been chosen on an empirical basis: the algorithm for benign/malignant/uncertain classification of a voxel was to be as simple as possible and the corresponding regions of the parameters space (according to the Tofts’ model) were to occupy approximately the same area. Since its inception, the method has been largely used in clinical environment, due to its simplicity and soundness. However, as only three time-points are used to evaluate the characteristics of the complex time-course of the contrast medium within capillaries, noise can result in voxel misclassification, as we show in this study. In this paper we analysed the performances of the method from a geometrical perspective, based on the concept of confidence region, and we proposed an ‘optimal’ choice of the three time-points, in order to reduce the misclassification to a minimum. Comparing the original 3TP method with our proposal on the basis of misclassification rate, our results show that the modified 3TP method can lead to better performance. Preliminary results on real data have been also reported. Moreover, our proposal has a sounding mathematical basis and is easily generalisable to the case of more than two parameters and to other modalities such as DCE-CT. [ABSTRACT FROM AUTHOR]

Since nonwoven fabric is widely used in the application of high performance geotextiles, its creep behaviour is essential to be evaluated. In this paper, the creep response of nonwoven fabric was studied by using four mechanical models including the one-term generalised Kelvin model, Burger's model, two-term generalised Kelvin model and Zurek's model. To verify the feasibility of the models, creep experiments for nonwoven fabric were conducted, and the data were fitted by the four models, respectively, to obtain their parameter values using the Marquardt algorithm for nonlinear regression. When comparing the experimental creep curves with those fitted from the mechanical models, it is obvious that the experimental data was best fitted by Burger's model. Also, since the residual sum of squares is far less than that of the GK (n = 1) and GK (n = 2) model and the squares of the correlation coefficient are near to unity, it can be concluded that Burger's model is suitable to describe the creep behaviour of nonwoven fabric. Therefore the viscoelastic model verified can be adopted to predict the creep elongation of nonwoven fabrics. [ABSTRACT FROM AUTHOR]

This paper introduces a multiobjective optimization (MOP) method for nonlinear regression analysis which is capable of simultaneously minimizing the model order and estimating parameter values without the need of exogenous regularization constraints. The method is introduced through a case study in polymer rheology modeling. Prevailing approaches in this field tackle conflicting optimization goals as a monobjective problem by aggregating individual regression errors on each dependent variable into a single weighted scalarization function. In addition, their supporting deterministic numerical methods often rely on assumptions which are extrinsic to the problem, such as regularization constants and restrictions on parameter distribution, thereby introducing methodology inherent biases into the model. Our proposed non-deterministic MOP strategy, on the other hand, aims at finding the Pareto-front of all optimal solutions with respect not only to individual regression errors, but also to the number of parameters needed to fit the data, automatically reducing the model order. The evolutionary computation approach does not require arbitrary constraints on objective weights, regularization parameters or other exogenous assumptions to handle the ill-posed inverse problem. The article discusses the method rationales, implementation, simulation experiments, and comparison with other methods, with experimental evidences that it can outperform state-of-art techniques. While the discussion focuses on the study case, the introduced method is general and immediately applicable to other problem domains. • Multiobjective optimization method for nonlinear regression. • Includes the model order as an optimization criteria. • Regularization-free method unbiased by exogenous constraints. • Pioneer use of evolutionary multiobjective approach in the application domain. • Experimental evidence that the method can outperform state-of-art techniques. [ABSTRACT FROM AUTHOR]

In the paper the nonlinear regression model with continuous time and random noise, which is a local functional of strongly dependent stationary Gaussian random process, is considered. Sufficient conditions of asymptotic uniqueness of the least squares estimator of regression function parameters are obtained. This result is applied to the least squares estimator of amplitude and angular frequencies of harmonic oscillations sum observed on the background of given random noise. To obtain the main result limit theorems of random processes, weak convergence of a family of measures to the spectral measure of a regression function, etc were used. The novelty, compared with the known results in the theory of periodogram estimator in observation models on weakly dependent noise, is assuming that the random noise is a local functional of Gaussian strongly dependent stationary process. The result can be used in the proof of the asymptotic normality of the least squares estimator of nonlinear regression model parameters with the help of Brower fixed point theorem. [ABSTRACT FROM AUTHOR]

Maximum likelihood estimators provide a powerful statistical tool. In this paper we directly deal with non-linear reserving models, without the need to transform those models to make them tractable for linear or generalized linear methods. We also show how the same general approach can be easily adapted to provide estimates for a very wide range of reserving methods and models, making use of the same framework, and even much of the same computer code. We focus on the triangle of incremental average costs, and show how five common methods can be set in a stochastic framework. [ABSTRACT FROM AUTHOR]

In many situations, the quality of a process can be characterized better by a relationship, known as a profile, between a response variable and one or more predictors. Almost all research eforts assume that response variable is continuous and follows a normal distribution, while there are instances in which the response is a binary variable and methods such as logistic regression are commonly used. In this paper, four control schemes namely Hotelling T2, MEWMA, Likelihood Ratio Test (LRT) and LRT/EWMA are proposed to monitor binary response profiles in phase II. The performance of the proposed control charts is evaluated and compared by simulation experiments for different shift values in the parameters of the profile in terms of the Average Run Length (ARL) criterion. The results show that all methods work well in the sense that they can effiectively detect shifts in the process parameters. Based on the results, MEWMA and LRT/EWMA methods display a better performance for small to moderate and large shift values, respectively. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we propose a confidence map and a pixel-based weighted correlation method for digital camera identification. In traditional camera identification method, a simple denoising technique is used to extract the photo-response non-uniformity (PRNU) as the difference between the original image and the denoised image. One of the major problems is that the image content is left behind in the noise residue which affects the correlation calculation for identifying the source camera. In order to solve the problem, we first study the image content effect by examining the effect of different image features on correlation. We then formulate the image content effect by nonlinear regression model and apply this relationship to obtain a confidence map for the testing images. In such way, the confidence map shows the reliability of each pixel in correlation calculation. It can be used as a weighting function so as to give a higher weighting to pixel which is more reliable and a lower weighting for less reliable pixel. By using the weighted correlation, our proposed method is able to improve the identification accuracies especially for the low quality image such as heavy JPEG compression. In particular, we found 5–20% improvement in identification accuracy at JPEG quality factor of 70. [Copyright &y& Elsevier]

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Abstract: We consider in this paper the fault diagnosis problem of a three tank system DTS-200 pilot plant. The presented approach is based on the analysis of the variogram, which is a graphical variance representation that characterizes the distribution of a measured dataset, and is used to extract the sensor fault parameters. These parameters are obtained by determining the best mathematical model that fits the empirical data. Nonlinear regression techniques are used to estimate the model coefficients. Experimental study is provided to illustrate the potential applicability of this method in process monitoring. [Copyright &y& Elsevier]

The aim of this paper is to propose a procedure for interval estimate of nitrogen dose x[sub r], which allows to achieve a chosen value of wheat production function derivative r. It is shown that the point estimator of x[sub r] follows asymptotically normal distribution. That fact is used for construction of an asymptotic confidence interval for x[sub r]. It is also shown that substituting r with proportion of input and product unit prices allows to use the proposed formula for interval estimation of economically optimal nitrogen dose. An example of such application based on winter wheat fertilizing experiment is given. [ABSTRACT FROM AUTHOR]

Abstract: Network reliability prediction is significant to make up the strategy for network management. In order to solve the local optimum of BP neural network, support vector machine(SVM) is proposed to predict network reliability in the paper. In the study, Grid method is applied to select optimal parameters of support vector machine. Thus, network reliability prediction model by SVM has four input nodes and one output node. The experimental network reliability data are applied to study the superiority of support vector machine. The comparison of network reliability prediction error between support vector machine and BP neural network demonstrates that network reliability prediction ability of support vector machine is better than that of BP neural network. [Copyright &y& Elsevier]

This paper gives a concise overview of concrete properties prediction using advanced nonlinear regression approach and Bayesian inference. Feed-forward layered neural network (FLNN) with Markov chain Monte Carlo stochastic sampling and Gaussian process (GP) with maximum likelihood hyperparameters estimation are introduced and compared. An empirical assessment of these two models using two benchmark problems are presented. Results on these benchmark datasets show that Bayesian neural networks and Gaussian processes have rather similar prediction accuracy and are superior in comparison to linear regression model. [ABSTRACT FROM AUTHOR]

Abstract: When the underlying asset price process follows a Lévy process, the market becomes incomplete, in which the option pricing can be a complicated problem. This paper proposes a method of asymptotic option pricing when the underlying asset price process follows a pure-jump Lévy process. We express the option price as the expected value of the discounted payoff and expand it at the Black–Scholes price assuming that the price process converges weakly to the Black–Scholes model. The price can be approximated by a formula with 4 parameters, which can easily be estimated using option prices observed in the market. The proposed price explains the market option data better than the Black–Scholes price in real data application with KOSPI 200. [Copyright &y& Elsevier]

The aim of this paper was to compare, analyze and explain thechange in linear, parabolic and cubic models of the relation between two chosen anthropometric soft tissue variables through a learning process implemented as a kinesiological treatment. A homogenous sample which homogenised further as the learning process advanced was used for the purpose of this research. In accordance with this, the linear model (y = β1x), the parabolic model ( y=a2x+bx+c) and the cubic model which has never been used before (y=a3x+b√x+c) depicting the relation between the thigh skinfold criterion variable andthe upper arm circumference predictor variable were observed at 4 points in time. The convergence of statistically insignificant nonlinear models to statistically significant nonlinear model was observed and explained, providing a new methodological tool for quantitative analysis of the success of the learning process. The research has shown that the cubic model is the most appropriate one at the highest point of sample homogenization. It also suggests the introduction of a mathematical expression for an interval of numbers which encompasses those predictor variable values for which it is reasonable to calculate the criterion variable value - the domain of the (non) linear regression model. Furthermore, the process of calculating the extreme points of the general nonlinear model was mathematically described and applied. [ABSTRACT FROM AUTHOR]

The paper presents results of modelling of quality changes in a muliticomponent non-homogenous granular mixture during mixing in an industrial worm mixer. The analysis was carried out for three granular mixtures: nine-component, ten-component and twelve-component. Modelling was performed with the use of nonlinear regression. Dependent variable was a residual sum of squares (mixture quality parameter), whereas independent variables were: number of components and mixing time. Two-dimensional dependence was constituted by square function formula. The aim of the study was to describe quality changes of muliticomponent, non-homogenous granular mixtures (nine, ten and twelve — component) during mixing in a vertical mixer with worm agitator, with the use of nonlinear regression model. Atypical aspect of the mixing process was a specific recirculation of the mixed components with the use of bucket conveyor during the process. [ABSTRACT FROM AUTHOR]

Abstract: Paper considers adaptation of control parameters in differential evolution. Adaptation by competitive setting is described and two novel variants of competitive differential evolution are proposed. Five adaptive variants of differential evolution are compared with other search algorithms on three benchmarks. One of them is the novel composition test functions, where the variants of differential evolution outperform other algorithms in 5 of 6 test functions. The NIST nonlinear regression datasets are used as the second benchmark and a subset of CEC’05 benchmark functions as the third one. The performance of adaptive differential evolution is compared with the adaptive controlled random search algorithm, tailored especially for the nonlinear-regression problems. Two of five tested variants of adaptive differential evolution are almost as reliable as the adaptive controlled random search algorithm and one of these variants converges only slightly slower than the adaptive controlled random search in nonlinear-regression problems. The results achieved in CEC’05 benchmark functions are close to the best performing algorithm. Therefore, the adaptive differential evolution is a promising tool of heuristic search for the global minimum in boundary-constrained problems. [Copyright &y& Elsevier]