Improved cat swarm optimization for parameter estimation of mixed additive and multiplicative random error model

To estimate the parameters of the mixed additive and multiplicative(MAM)random error model using the weighted least squares iterative algorithm that requires derivation of the complex weight array,we introduce a derivative-free cat swarm optimization for parameter estimation.We embed the Powell method,which uses conjugate direction acceleration and does not need to derive the objective function,into the original cat swarm optimization to accelerate its convergence speed and search accuracy.We use the ordinary least squares,weighted least squares,original cat swarm optimization,particle swarm algorithm and improved cat swarm optimization to estimate the parameters of the straight-line fitting MAM model with lower nonlinearity and the DEM MAM model with higher nonlinearity,respectively.The experimental results show that the improved cat swarm optimization has faster convergence speed,higher search accuracy,and better stability than the original cat swarm optimization and the particle swarm algorithm.At the same time,the improved cat swarm optimization can obtain results consistent with the weighted least squares method based on the objective function only while avoiding multiple complex weight array derivations.The method in this paper provides a new idea for theoretical research on parameter estimation of MAM error models.

A novel Ti-5.55Al-6.70Zr-1.50V-0.70Mo-3.41Nb-0.21Si alloy designed using cluster-plus-glue-atom model for laser additive manufacturing

A novel Ti-5.55Al-6.70Zr-1.50V-0.70Mo-3.41Nb-0.21Si alloy was designed using the cluster formula approach(cluster-plus-glue-atom model)and prepared by laser melting deposition(LMD).Its composition formula 12[Al-Ti_(12)](AlTi_(2))+5[Al_(0.8)Si_(0.2)-Ti_(12)Zr_(2)](V_(0.8)Mo_(0.2)Nb_(1)Ti)features an enhancedβ-Ti via co-alloying of Zr,V,Mo,Nb and Si.The experimental results show that the cluster formula ofαandβphases in the novel alloy are respectivelyα-[Al-Ti_(11.5)Zr_(0.5)](Al_(1)Ti_(2))andβ-[Al_(0.8)Si_(0.2)-Ti_(13.2)Zr_(0.8)](V_(1)Mo_(0.4)Nb_(1.6)),both containing Zr elements.The fitted composition via the α andβphase cluster formulas has little difference with the actual alloy composition,suggesting that the validity of cluster-plus-glue-atom model in the alloy composition design.After hot isostatic pressing(HIP),both the Ti-6Al-4V and the novel alloy by LMD are characterized by prior-βcolumnar grains,while the typical<100>texture disappears.Compared with Ti-6Al-4V,Ti-5.55Al-6.70Zr-1.50V-0.70Mo-3.41Nb-0.21Si alloy exhibits a combination of higher strength(1,056 MPa)and higher ductility(14%)at room temperature and higher strength(580 MPa)at 550℃ after HIP,and can potentially serves as LMD materials.

A review on stress determination and control in metal-based additive manufacturing

Metal additive manufacturing(MAM)is an emerging and disruptive technology that builds three-dimensional(3D)components by adding layer-upon-layer of metallic materials.The complex cyclic thermal history and highly localized energy can produce large temperature gradients,which will,in turn,lead to compressive and tensile stress during the MAM process and eventually result in residual stress.Being an issue of great concern,residual stress,which can cause distortion,delamination,cracking,etc.,is considered a key mechanical quantity that affects the manufacturing quality and service performance of MAM parts.In this review paper,the ongoing work in the field of residual stress determination and control for MAM is described with a particular emphasis on the experimental measurement/control methods and numerical models.We also provide insight on what still requires to be achieved and the research opportunities and challenges.

Experimental and Modeling Study of the Effects of Gas Additives on the Thermal DeNO_x Process

An experimental study of thermal DeNOx process with different additives was performed in an electricityheated tubular flow reactor,showing that CO is less effective to lower the optimum temperature than H2 and CH4. The maximum NO reduction is lowered with H2 added,while it is hardly affected by CO or CH4.The temperature window is widened appreciably with CH4 added,while it is narrowed slightly by H2 or CO.The disadvantage of CH4 is that it causes CO emission due to its incomplete oxidation,and the maximum conversion of CH4 to CO is more than 50%.In general,the calculation using a detailed chemical kinetic model predicts most of the process features reasonably well.The analysis on reaction mechanism shows that the effects of these additives on NO reduction are achieved principally by promoting the production of·OH radical.

Incorporating shape constraints in generalized additive modelling of the height-diameter relationship for Norway spruce

Background： Measurements of tree heights and diameters are essential in forest assessment and modelling. Tree heights are used for estimating timber volume, site index and other important variables related to forest growth and yield, succession and carbon budget models. However, the diameter at breast height （dbh） can be more accurately obtained and at lower cost, than total tree height. Hence, generalized height-diameter （h-d） models that predict tree height from dbh, age and other covariates are needed. For a more flexible but biologically plausible estimation of covariate effects we use shape constrained generalized additive models as an extension of existing h-d model approaches. We use causal site parameters such as index of aridity to enhance the generality and causality of the models and to enable predictions under projected changeable climatic conditions. Methods： We develop unconstrained generalized additive models （GAM） and shape constrained generalized additive models （SCAM） for investigating the possible effects of tree-specific parameters such as tree age, relative diameter at breast height, and site-specific parameters such as index of aridity and sum of daily mean temperature during vegetation period, on the h-d relationship of forests in Lower Saxony, Germany. Results： Some of the derived effects, e.g. effects of age, index of aridity and sum of daily mean temperature have significantly non-linear pattern. The need for using SCAM results from the fact that some of the model effects show partially implausible patterns especially at the boundaries of data ranges. The derived model predicts monotonically increasing levels of tree height with increasing age and temperature sum and decreasing aridity and social rank of a tree within a stand, The definition of constraints leads only to marginal or minor decline in the model statistics like AIC An observed structured spatial trend in tree height is modelled via 2-dimensional surface fitting. Conclusions： We demonstrate that the SCAM approach allows optimal regression modelling flexibility similar to the standard GAM but with the additional possibility of defining specific constraints for the model effects. The longitudinal character of the model allows for tree height imputation for the current status of forests but also for future tree height prediction.

Design for Ti-Al-V-Mo-Nb alloys for laser additive manufacturing based on a cluster model and on their microstructure and properties

In this study,α+βTi-Al-V-Mo-Nb alloys with the addition of multiple elements that are suitable for laser additive manufacturing(LAM)were designed according to a Ti-6Al-4V cluster formula.This formula can be expressed as 12[Al-Ti12](AlTi2)+5[Al-Ti14]((Mo,V,Nb)2Ti),in which Mo and Nb were added into the alloys partially instead of V to give alloys with nominal compositions of Ti-6.01Al-3.13V-1.43Nb,Ti-5.97Al-2.33V-2.93Mo,and Ti-5.97Al-2.33V-2.20Mo-0.71Nb(wt.%).The microstructures and mechanical properties of the as-deposited and heat-treated samples prepared via LAM were examined.The sizes of theβcolumnar grains andαlaths in the Nb-containing samples are found to be larger than those of the Ti-6Al-4V alloy,whereas Mo-or Mo/Nb-added alloys contain finer grains.It indicates that Nb gives rise to coarsenedβcolumnar grains andαlaths,while Mo significantly refines them.Furthermore,the single addition of Nb improves the elongation,whereas the single addition of Mo enhances the strength of the alloys.The simultaneous addition of Mo/Nb significantly improves the comprehensive mechanical properties of the alloys,leading to the best properties with an ultimate tensile strength of 1,070 MPa,a yield strength of 1,004 MPa,an elongation of 9%,and micro-hardness of 355 HV.The fracture modes of all the alloys are ductile-brittle mixed fracture.

Design and Manufacturing Strategies for Fused Deposition Modelling in Additive Manufacturing:A Review

Although several research works in the literature have focused on studying the capabilities of additive manufacturing(AM) systems, few works have addressed the development of Design for Additive Manufacturing(DfAM) knowledge,tools, rules, and methodologies, which has limited the penetration and impact of AM in industry. In this paper a comprehensive review of design and manufacturing strategies for Fused Deposition Modelling(FDM) is presented.Consequently, several DfAM strategies are proposed and analysed based on existing research works and the operation principles, materials, capabilities and limitations of the FDM process. These strategies have been divided into four main groups: geometry, quality, materials and sustainability. The implementation and practicality of the proposed DfAM is illustrated by three case studies. The new proposed DfAM strategies are intended to assist designers and manufacturers when making decisions to satisfy functional needs, while ensuring manufacturability in FDM systems.Moreover, many of these strategies can be applied or extended to other AM processes besides FDM.

Design Consideration for Additive Manufacturing: Fused Deposition Modelling

Additive Manufacturing (AM) technologies have progressed in the past few years and many of them are now capable of producing functional parts instead of mere prototypes. AM provides a multitude of benefits, especially in design freedom. However, it still lacks industrial relevance because of the absence of comprehensive design rules for AM. Although AM is usually advertised as being the solution for all traditional manufacturing design limitations, the fact is that AM only replaces these limitations with a different set of restrictions. To fully exploit the advantages of AM, it is necessary to understand these limitations and consider them early during the design process. The establishment of design considerations in AM enables parts and process optimization. This paper discusses the design considerations that will lead to optimize part quality. Specifically, the work discusses the Fused Deposition Modeling (FDM) due to its common use and availability. These considerations are drawn from literature and from experiments done by the authors. The experiments done by the authors include an investigation for the influence of elevated service temperature on the performance of FDM PLA parts, benchmarking the capability of FDM to print overhangs and bridges without supports, studying the influence of processing parameters over dimensional accuracy, and the effect of processing parameters on the final FDM samples modulus of elasticity. The work presents a case study investigating the correct clearances for FDM parts and finally a redesign for AM case study of a support bracket originally manufactured using traditional manufacturing methods taking into consideration the design considerations discussed in this paper.

Predictive Vegetation Mapping Approach Based on Spectral Data, DEM and Generalized Additive Models

This study aims to provide a predictive vegetation mapping approach based on the spectral data, DEM and Generalized Additive Models (GAMs). GAMs were used as a prediction tool to describe the relationship between vegetation and environmental variables, as well as spectral variables. Based on the fitted GAMs model, probability map of species occurrence was generated and then vegetation type of each grid was defined according to the probability of species occurrence. Deviance analysis was employed to test the goodness of curve fitting and drop contribution calculation was used to evaluate the contribution of each predictor in the fitted GAMs models. Area under curve (AUC) of Receiver Operating Characteristic (ROC) curve was employed to assess the results maps of probability. The results showed that: 1) AUC values of the fitted GAMs models are very high which proves that integrating spectral data and environmental variables based on the GAMs is a feasible way to map the vegetation. 2) Prediction accuracy varies with plant community, and community with dense cover is better predicted than sparse plant community. 3) Both spectral variables and environmental variables play an important role in mapping the vegetation. However, the contribution of the same predictor in the GAMs models for different plant communities is different. 4) Insufficient resolution of spectral data, environmental data and confounding effects of land use and other variables which are not closely related to the environmental conditions are the major causes of imprecision.

Modeling hot strip rolling process under framework of generalized additive model

This research develops a new mathematical modeling method by combining industrial big data and process mechanism analysis under the framework of generalized additive models(GAM)to generate a practical model with generalization and precision.Specifically,the proposed modeling method includes the following steps.Firstly,the influence factors are screened using mechanism knowledge and data-mining methods.Secondly,the unary GAM without interactions including cleaning the data,building the sub-models,and verifying the sub-models.Subsequently,the interactions between the various factors are explored,and the binary GAM with interactions is constructed.The relationships among the sub-models are analyzed,and the integrated model is built.Finally,based on the proposed modeling method,two prediction models of mechanical property and deformation resistance for hot-rolled strips are established.Industrial actual data verification demonstrates that the new models have good prediction precision,and the mean absolute percentage errors of tensile strength,yield strength and deformation resistance are 2.54%,3.34%and 6.53%,respectively.And experimental results suggest that the proposed method offers a new approach to industrial process modeling.

Ridge estimation iterative solution of ill-posed mixed additive and multiplicative random error model with equality constraints

The reasonable prior information between the parameters in the adjustment processing can significantly improve the precision of the parameter solution. Based on the principle of equality constraints, we establish the mixed additive and multiplicative random error model with equality constraints and derive the weighted least squares iterative solution of the model. In addition, aiming at the ill-posed problem of the coefficient matrix, we also propose the ridge estimation iterative solution of ill-posed mixed additive and multiplicative random error model with equality constraints based on the principle of ridge estimation method and derive the U-curve method to determine the ridge parameter. The experimental results show that the weighted least squares iterative solution can obtain more reasonable parameter estimation and precision information than existing solutions, verifying the feasibility of applying the equality constraints to the mixed additive and multiplicative random error model. Furthermore, the ridge estimation iterative solution can obtain more accurate parameter estimation and precision information than the weighted least squares iterative solution.

Longitudinal height-diameter curves for Norway spruce, Scots pine and silver birch in Norway based on shape constraint additive regression models

Background： Generalized height-diameter curves based on a re-parameterized version of the Korf function for Norway spruce （Piceo abies （L.） Karst.）, Scots pine （Pinus sylvestris L.） and silver birch （Betula pendula Roth） in Norwa are presented. The Norwegian National Forest Inventory （NFI） is used as data base for estimating the model parameters. The derived models are developed to enable spatially explicit and site sensitive tree height imputatio in forest inventories as well as future tree height predictions in growth and yield scenario simulations. Methods： Generalized additive mixed models （gamm） are employed to detect and quantify potentially non-linear effects of predictor variables. In doing so the quadratic mean diameter serves as longitudinal covariate since stand ag as measured in the NFI, shows only a weak correlation with a stands developmental status in Norwegian forests. Additionally the models can be locally calibrated by predicting random effects if measured height-diameter pairs are available. Based on the model selection of non-constraint models, shape constraint additive models （scare） were fit tc incorporate expert knowledge and intrinsic relationships by enforcing certain effect patterns like monotonicity. Results： Model comparisons demonstrate that the shape constraints lead to only marginal differences in statistical characteristics but ensure reasonable model predictions. Under constant constraints the developed models predict increasing tree heights with decreasing altitude, increasing soil depth and increasing competition pressure of a tree. / two-dimensional spatially structured effect of UTM-coordinates accounts for the potential effects of large scale spatial correlated covariates, which were not at our disposal. The main result of modelling the spatially structured effect is lower tree height prediction for coastal sites and with increasing latitude. The quadratic mean diameter affects both the level and the slope of the height-diameter curve and both effects are positive. Conclusions： In this investigation it is assumed that model effects in additive modelling of height-diameter curves which are unfeasible and too wiggly from an expert point of view are a result of quantitatively or qualitatively limited data bases. However, this problem can be regarded not to be specific to our investigation but more general since growth and yield data that are balanced over the whole data range with respect to all combinations of predictor variables are exceptional cases. Hence, scare may provide methodological improvements in several applications by combining the flexibility of additive models with expert knowledge.

Model Detection for Additive Models with Longitudinal Data

In this paper, we consider the problem of variable selection and model detection in additive models with longitudinal data. Our approach is based on spline approximation for the components aided by two Smoothly Clipped Absolute Deviation (SCAD) penalty terms. It can perform model selection (finding both zero and linear components) and estimation simultaneously. With appropriate selection of the tuning parameters, we show that the proposed procedure is consistent in both variable selection and linear components selection. Besides, being theoretically justified, the proposed method is easy to understand and straightforward to implement. Extensive simulation studies as well as a real dataset are used to illustrate the performances.

Additive mixed models to study the effect of tree age and climatic factors on stem radial growth of Eucalyptus trees

The effect of tree age and climatic variables on stem radial growth of two hybrid clones of Eucalyptus was determined using longitudinal data from eastern South Africa.The stem radius of was measured weekly as the response variable.In addition to tree age,average weekly temperature,solar radiation,relative humidity and wind speed were simultaneously recorded with total rainfall at the site.An additive mixed effects model that incorporates a non-parametric smooth function was used.The results of the analysis indicate that the relationship between stem radius and each of the covariates can be explained by nonlinear functions.Models that account for the effect of clone and season together with their interaction in the parametric part of the additive mixed model were also fitted.The interaction between clone and season was not significant in all cases.For analyzing the joint effect all the covariates,additive mixed models that included two or more covariates were fitted.A significant effect of tree age was found in all cases.Although tree age was the key determinant of stem radial growth,weather variables also had a significant effect that was dependent on season.

Review of heterogeneous material objects modeling in additive manufacturing

This review investigates the recent developments of heterogeneous objects modeling in additive manufacturing(AM),as well as general problems and widespread solutions to the modeling methods of heterogeneous objects.Prevalent heterogeneous object representations are generally categorized based on the different expression or data structure employed therein,and the state-of-the-art of process planning procedures for AM is reviewed via different vigorous solutions for part orientation,slicing methods,and path planning strategies.Finally,some evident problems and possible future directions of investigation are discussed.

Modeling the effect of stand and site characteristics on the probability of mistletoe infestation in Scots pine stands using remote sensing data

Over the past decade,the presence of mistletoe(Viscum album ssp.austriacum)in Scots pine stands has increased in many European countries.Understanding the factors that influence the occurrence of mistletoe in stands is key to making appropriate forest management decisions to limit damage and prevent the spread of mistletoe in the future.Therefore,the main objective of this study was to determine the probability of mistletoe occurrence in Scots pine stands in relation to stand-related endogenous factors such as age,top height,and stand density,as well as topographic and edaphic factors.We used unmanned aerial vehicle(UAV)imagery from 2,247 stands to detect mistletoe in Scots pine stands,while majority stand and site characteristics were calculated from airborne laser scanning(ALS)data.Information on stand age and site type from the State Forest database were also used.We found that mistletoe infestation in Scots pine stands is influenced by stand and site characteristics.We documented that the densest,tallest,and oldest stands were more susceptible to mistletoe infestation.Site type and specific microsite conditions associated with topography were also important factors driving mistletoe occurrence.In addition,climatic water balance was a significant factor in increasing the probability of mistletoe occurrence,which is important in the context of predicted temperature increases associated with climate change.Our results are important for better understanding patterns of mistletoe infestation and ecosystem functioning under climate change.In an era of climate change and technological development,the use of remote sensing methods to determine the risk of mistletoe infestation can be a very useful tool for managing forest ecosystems to maintain forest sustainability and prevent forest disturbance.

Study of monthly variations in primary production and their relationships with environmental factors in the Daya Bay based on a general additive model

In this study, the horizontal and vertical distribution of primary production(PP) and its monthly variations were described based on field data collected from the Daya Bay in January–December of 2016. The relationships between PP and environmental factors were analyzed using a general additive model(GAM). Significant seasonal differences were observed in the horizontal distribution of PP, while vertical distribution showed a relatively consistent unimodal pattern. The monthly average PP(calculated by carbon) ranged from 48.03 to 390.56 mg/(m~2·h),with an annual average of 182.77 mg/(m~2·h). The highest PP was observed in May and the lowest in November.Additionally, the overall trend in PP was spring>summer>winter>autumn, and spring PP was approximately three times that of autumn PP. GAM analysis revealed that temperature, bottom salinity, phytoplankton, and photosynthetically active radiation(PAR) had no significant relationships with PP, while longitude, depth, surface salinity, chlorophyll a(Chl a) and transparency were significantly correlated with PP. Overall, the results presented herein indicate that monsoonal changes and terrestrial and offshore water systems have crucial effects on environmental factors that are associated with PP changes.

ADDITIVE HAZARDS MODEL WITH TIME-VARYING REGRESSION COEFFICIENTS

This article discusses regression analysis of failure time under the additive hazards model, when the regression coefficients are time-varying. The regression coefficients are estimated locally based on the pseudo-score function [12] in a window around each time point. The proposed method can be easily implemented, and the resulting estimators are shown to be consistent and asymptotically normal with easily estimated variances. The simulation studies show that our estimation procedure is reliable and useful.

Simulating Potential Distribution of Tamarix chinensis in Yellow River Delta by Generalized Additive Models

There are typical ecosystems of littoral wetlands in the Yellow River Delta.In order to study the relationships between Tamarix chinensis and environmental variables and to predict T.chinensis potential distribution in the Yellow River Delta,641 vegetation samples and 964 soil samples were collected in the area in October of 2004,2005,2006 and 2007.The contents of soil organic matter,total phosphorus,salt,and soluble potassium were determined.Then,the analyzed data were interpolated into spatial raster data by Kriging interpolation method.Meanwhile,the digital elevation model,soil type map and landform unit map of the Yellow River Delta were also collected.Generalized Additive Models(GAMs) were employed to build species-environment model and then simulate the potential distribution of T.chinensis.The results indicated that the distribution of T.chinensis was mainly limited by soil salt content,total soil phosphorus content,soluble potassium content,soil type,landform unit,and elevation.The distribution probability of T.chinensis was produced with a lookup table generated by Grasp Module(based on GAMs) in software ArcView GIS 3.2.The AUC(Area Under Curve) value of validation and cross-validation of ROC(Receive Operating Characteristic) were both higher than 0.8,which suggested that the established model had a high precision for predicting species distribution.

Modeling and Fault Monitoring of Bioprocess Using Generalized Additive Models （GAMs） and Bootstrap

Fault monitoring of bioprocess is important to ensure safety of a reactor and maintain high quality of products. It is difficult to build an accurate mechanistic model for a bioprocess, so fault monitoring based on rich historical or online database is an effective way. A group of data based on bootstrap method could be resampling stochastically, improving generalization capability of model. In this paper, online fault monitoring of generalized additive models （GAMs） combining with bootstrap is proposed for glutamate fermentation process. GAMs and bootstrap are first used to decide confidence interval based on the online and off-line normal sampled data from glutamate fermentation experiments. Then GAMs are used to online fault monitoring for time, dissolved oxygen, oxygen uptake rate, and carbon dioxide evolution rate. The method can provide accurate fault alarm online and is helpful to provide useful information for removing fault and abnormal phenomena in the fermentation.