Distributed asynchronous multiple sensor fusion with nonlinear multiple models

Accurate prediction of phenological development is important in the winter wheat Triticum aestivum agroecosystem. From a practical perspective, applications of pesticides and fertil- izers are carried out at specific phenological stages. In crop-simulation modeling, the prediction of yield components (kernel number and kernel weight) and wheat-grain yield relies on accurate pre- diction of phenology. In this study, a nonlinear multiplicative model by Wang & Engel (WE) for pre- dicting phenological development in differing winter wheat cultivars was evaluated using data from a 3 yr field experiment. In the vegetative phase (emergence to anthesis) the daily development rate (r) was simulated based on the product of a maximum development rate (Rmax) in the vegetative phase, a temperature response function (ƒ(T)), a photoperiod response function (ƒ(P)), and a vernal- ization response function (ƒ(V)). ƒ(T) was a nonlinear function of the 3 cardinal temperatures for phe- nological development (minimum, Tmin, optimum, Topt, and maximum, Tmax). ƒ(P) was an exponential function of the actual and critical photoperiods and a sensitivity parameter unique to each cultivar. ƒ(V) was calculated using ƒ(T) based on the cardinal temperatures for vernalization (Tmin,vn, Topt,vn, and Tmax,vn). In the reproductive phase, r was simulated based on the product of Rmax for the repro- ductive phase and ƒ(T). Predictions from this nonlinear model were compared to predictions from the phenology submodel of CERES-Wheat V3.0 (CW3). The nonlinear model performed very well for predicting phenological development in the 3 winter wheat cultivars, the mean root mean square error (RMSE) ranged from 2.9 to 4.1 d from booting to maturity. For the CW3 model, the mean RMSE ranged from 4.8 to 5.9 d for the same phenological stages. The WE model predicted double ridge with a mean RMSE of 7.3 d. Both models predicted terminal spikelet with a mean RMSE ranging from 6.2 to 7.1 d. The WE model was generally a better predictor of phenology between booting and maturity than the CW3 model.

Purpose– The purpose of this article, starting from linear regression, was to estimate a switching regression model, nonparametric model and generalized additive model as a semi-parametric model, perform function estimation with multiple nonlinear estimation methods and conduct comparative analysis of their predictive accuracy. The theoretical importance of estimating hedonic functions using a nonlinear function form has been pointed out in ample previous research (e.g. Heckmanet al. (2010).Design/methodology/approach– The distinctive features of this study include not only our estimation of multiple nonlinear model function forms but also the method of verifying predictive accuracy. Using out-of-sample testing, we predicted and verified predictive accuracy by performing random sampling 500 times without replacement for 9,682 data items (the same number used in model estimation), based on data for the years before and after the year used for model estimation.Findings– As a result of estimating multiple models, we believe that when it comes to hedonic function estimation, nonlinear models are superior based on the strength of predictive accuracy viewed in statistical terms and on graphic comparisons. However, when we examined predictive accuracy using out-of-sample testing, we found that the predictive accuracy was inferior to linear models for all nonlinear models.Research limitations/implications– In terms of the reason why the predictive accuracy was inferior, it is possible that there was an overfitting in the function estimation. Because this research was conducted for a specific period of time, it needs to be developed by expanding it to multiple periods over which the market fluctuates dynamically and conducting further analysis.Practical implications– Many studies compare predictive accuracy by separating the estimation model and verification model using data at the same point in time. However, when attempting practical application for auto-appraisal systems and the like, it is necessary to estimate a model using past data and make predictions with respect to current transactions. It is possible to apply this study to auto-appraisal systems.Social implications– It is recognized that housing price fluctuations caused by the subprime crisis had a massive impact on the financial system. The findings of this study are expected to serve as a tool for measuring housing price fluctuation risks in the financial system.Originality/value– While the importance of nonlinear estimation when estimating hedonic functions has been pointed out in theoretical terms, there is a noticeable lag when it comes to testing based on actual data. Given this, we believe that our verification of nonlinear estimation’s validity using multiple nonlinear models is significant not just from an academic perspective – it may also have practical applications.

This paper presents a novel manifold learning algorithm for high-dimensional data sets. The scope of the application focuses on the problem of motion tracking in video sequences. The framework presented is twofold. First, it is assumed that the samples are time ordered, providing valuable information that is not presented in the current methodologies. Second, the manifold topology comprises multiple charts, which contrasts to the most current methods that assume one single chart, being overly restrictive. The proposed algorithm, Gaussian process multiple local models (GP-MLM), can deal with arbitrary manifold topology by decomposing the manifold into multiple local models that are probabilistic combined using Gaussian process regression. In addition, the paper presents a multiple filter architecture where standard filtering techniques are integrated within the GP-MLM. The proposed approach exhibits comparable performance of state-of-the-art trackers, namely multiple model data association and deep belief networks, and compares favorably with Gaussian process latent variable models. Extensive experiments are presented using real video data, including a publicly available database of lip sequences and left ventricle ultrasound images, in which the GP-MLM achieves state of the art results.

An adaptive channel estimation scheme, exploiting the over-sampled complex exponential basis expansion model (CE-BEM), is presented for doubly-selective channels where we track the BEM coefficients via a multiple model approach. In the past work the number of BEM coefficients used to model the doubly-selective channels for channel estimation has been based on an upperbound on the channel Doppler spread. Higher the Doppler spread, more the number of BEM coefficients leading to higher channel estimation variance. In this paper we propose to use a multiple model framework where several candidate Doppler spread values are used to cover the range from zero to an upperbound, leading to multiple CE-BEM channel models, each corresponding to an assumed value of the Doppler spread. Subsequently the well- known interacting multiple model (IMM) algorithm is used for symbol detection based on multiple state-space models corresponding to the multiple estimated channels. A simulation example is presented to illustrate the proposed approach.

A nonlinear multiple criteria model base for the problem of balancing caseload risk in probation departments

ADS-B and radar will coexist for a long time in the future. For the ADS-B and multi-radar asynchronous track fusion problem in the air traffic control system, an ADS-B and radar fusion architecture and an asynchronous multi-sensor track fusion algorithm based on augmented state interacting multiple model(IMM) filter with probability data association(PDA) are proposed in this paper. The fusion algorithm is developed by applying the standard IMM approach, the PDA technique, and asynchronous measurement updating to a state-augmented system for the target motion. The state vector is augmented to include the past states which correspond to those asynchronous measurements. A PDA filter for parallel sensor processing is developed for target tracking under clutter. Real data from the ADS-B equipped aircraft and radar are used to test the performance of the algorithm. The simulation result shows that the track accuracy can be improved by this algorithm and the performance is comparable with the distributed multi-rate track fusion algorithm.

In this article, we describe the use of a new dynamic cubic nonlinear model, a new nonlinear subgrid-scale model, for simulating the cavitating flow around an NACA66 series hydrofoil. For comparison, the dynamic Smagorinsky model is also used. It is found that the dynamic cubic nonlinear model can capture the turbulence spectrum, while the dynamic Smagorinsky model fails. Both models reproduce the cavity growth/destabilization cycle, but the results of the dynamic cubic nonlinear model are much smoother. The re-entrant jet is clearly captured by the models, and it is shown that the re-entrant jet cuts the cavity into two parts. In general, the dynamic cubic nonlinear model provides improvement over the dynamic Smagorinsky model for the calculation of cavitating flow.

In this paper is shown that the probability-statistic methods application, especially at the early stage of the aviation gas turbine engine (GTE) technical condition diagnosing, when the flight information has property of the fuzzy, limitation and uncertainty is unfounded. Hence is considered the efficiency of application of new technology Soft Computing at these diagnosing stages with the using of the Fuzzy Logic and Neural Networks methods. Training with high accuracy of fuzzy multiple linear and non-linear models (fuzzy regression equations) which received on the statistical fuzzy data basis is made. Thus for GTE technical condition more adequate model making are analysed dynamics of skewness and kurtosis coefficients' changes. Researches of skewness and kurtosis coefficients values' changes show that, distributions of GTE work parameters have fuzzy character. Hence consideration of fuzzy skewness and kurtosis coefficients is expedient. Investigation of the basic characteristics changes' dynamics of GTE work parameters allows to draw conclusion on necessity of the Fuzzy Statistical Analysis at preliminary identification of the engines' technical condition. Researches of correlation coefficients values' changes shows also on their fuzzy character. Therefore for models choice the application of the Fuzzy Correlation Analysis results is offered. For checking of models adequacy is considered the Fuzzy Multiple Correlation Coefficient of Fuzzy Multiple Regression. At the information sufficiency is offered to use recurrent algorithm of aviation GTE technical condition identification (Hard Computing technology is used) on measurements of input and output parameters of the multiple linear and non-linear generalised models at presence of noise measured (the new recursive Least Squares Method (LSM)). The developed GTE condition monitoring system provides stage-by-stage estimation of engine technical conditions. As application of the given technique the estimation of the new operating aviation engine temperature condition was made.

In this paper, a dynamic subgrid scale (SGS) stress model is proposed by modifying the existing quadratic nonlinear model. Unlike the conventional eddy viscosity models, the nonlinear model suggests an anisotropic tensorial polynomial relation between the SGS stress and the resolved strain rate tensor. Examined in the rotating turbulent channel flow, the previous nonlinear model is found unable to successfully predict the turbulent kinetic energy and the Reynolds shear stress. In the previous nonlinear model, an excessive backward energy transfer from the SGS to the resolved scale is found, so it is removed in the new model to provide sufficient SGS dissipation. The dynamic method is reconsidered, based on the analysis of the transport equation of the resolved Reynolds shear stress. The new dynamic nonlinear model is examined in the rotating turbulent channel flow at Re = 7000 and various rotation numbers (Ro), ranging from 0.0 to 0.6. The new dynamic procedure determines a more proper model coefficient for the nonlinear term, so the modified nonlinear model predicts superior results to the previous nonlinear models. The Vreman's approach of the eddy viscosity is implemented into the nonlinear model, to compare with the conventional dynamic Smagorinsky type nonlinear model in the rotating turbulent channel flow.

The existence of many different dynamic large eddy simulation (LES) methods leads to questions about the theoretical foundation of dynamic LES methods. It was shown recently that the use of stochastic analysis enables a theoretically well based systematic derivation of a realizable linear dynamic model (LDM) and a realizable nonlinear dynamic model (NDM). A priori and a posteriori analyses of turbulent channel flow are used here to study the characteristic properties of these dynamic models. The LDM and NDM are compared with other dynamic models: the non-stabilized and stabilized dynamic Smagorinsky model (DSM), which is used in many applications of LES, and Wang-Bergstrom's dynamic model (WBDM), which represents an extension of the DSM. The DSM and WBDM do not represent realizable models because they are not derived as consequences of a realizable stochastic process. The comparisons reported here show that the LDM and NDM are based on a dynamic model formulation that avoids shortcomings of existing concepts. The LDM and NDM account for backscatter, and they are computationally stable without any modification. The LDM and NDM represent the instantaneous small scale structure of turbulence very well. Compared to the DSM and WBDM, respectively, the LDM and NDM are computationally more efficient.

Superconducting cable is the key function part of the ITER (International Thermonuclear Experimental Reactor), and exhibits a typical geometrical multi-scale feature. Based on a modified discrete element method , the multi-layer discrete dynamic models that span in 104–105 orders of magnitude that from a spiral superconducting filament to the multi-stage twist cable are established by stepwise twisting process firstly, and their mechanics response under the external load can be successfully predicted by introducing the flexible authorization nonlinear multiple contact models and considering the friction among multi-continuum reasonably. This bottom-up modeling process also presents a useful approach to predict the mechanical properties of continuous complex twist structures with strong nonlinear and partial discrete features.

The calculation of leaf appearance rate (LAR) and number of accumulated or emerged leaves (NL) on the main stem are part of many crop simulation models. The purpose of this study was to adapt and evaluate a model (WE model) for simulating the main stem LAR and NL in potato (Solanum tuberosum L.). The WE model is a non-linear multiplicative model that takes into account the effect of genotype and environmental factors on LAR. A linear model (Phyllochron model) was also used as a comparison with the WE model. A series of field experiments with 14 planting dates over two years (2003 and 2004) was carried out in Santa Maria, RS, Brazil, using the cultivar Asterix. Coefficients of the WE model and the phyllochron model were estimated with data from four planting dates in 2003, and the models were validated with data from the other ten plantings, which are independent data. The statistics used to quantify model performance was the root mean square error (RMSE). The WE model was a better predictor of NL (RMSE=2.0 leaves) than the phyllochron model (RMSE=3.7 leaves). The WE model has coefficients with biological meaning and a non-linear temperature response function, which renders generality and robustness to this LAR model.

This paper proposes a new multiple model estimation method which employs the square root version of unscented information filter (SRUIF) in the framework of interacting multiple model (IMM). Comparing with the general UIF, the square root version has better numerical characteristics, such as improved numerical accuracy, double order precision and preservation of the symmetry for the covariance matrix. However, the SRUIF has to been further improved in order to achieve better performance in case the system has multiple models, e.g., maneuvering object tracking. Therefore, we here propose to use the IMM with the SRUIF (IMM-SRUIF) for solving multiple model estimation problem. A classical simulation of the maneuvering object tracking problem is demonstrated, which shows the advantages of the proposed IMM-SRUIF method.

This paper introduces for the first time in literature the complete picture of depth of anesthesia regulatory paradigm. The simulation model proposes to include not only the three anesthesia features: hypnosis, analgesia and neuromuscular blockade, but also to introduce the interaction with the hemodynamic system (cardio-regulatory loops). Disturbances occurring in the operation theatre origin from surgical stimulation, for which profiles are available. A novel addition is the model of nociceptor pathway, recently developed and thus available for analysis. The nociceptor pathway model makes the link between the surgical stimulation and the perception to the patient of its effect. In conclusion, this paper presents a nonlinear multiple input multiple output model for further development of control algorithms in this application field.

ABSTRACTDistillers dried grains with solubles (DDGS) is a widely used animal feed. But transportation of DDGS is often troublesome because of its stickiness. DDGS is formed by combining condensed distillers solubles (CDS) with distillers wet grains (DWG) and then drying. As a first step toward understanding drying behavior, this study's objective was to investigate batch‐drying kinetic behavior of DWG with three CDS addition levels (10, 15, and 20% wb) and three drying‐temperature levels (100, 200, and 300°C). Multiple nonlinear mathematical models were used to fit experimental drying data for moisture content versus drying rate. A new comprehensive model was developed (R2 = 0.89, SEM = 18.60) from a modified Chen and Douglas model to incorporate CDS and drying‐temperature terms. Drying temperature affected drying rate more significantly than did changes in CDS level; thus, drying temperature was the main effect and CDS was a subeffect. Increasing the drying temperature increased the drying rate significantly for all levels of CDS addition. This model can be used for predicting DWG drying behavior under broad operating conditions; it can be used to help the industry produce better DDGS, which may thus result in better DDGS handling and transport characteristics.