Parametric Modeling Techniques Research Articles

Using Airborne Lidar, Multispectral Imagery, and Field Inventory Data to Estimate Basal Area, Volume, and Aboveground Biomass in Heterogeneous Mixed Species Forests: A Case Study in Southern Alabama

Airborne light detection and ranging (lidar) has proven to be a useful data source for estimating forest inventory metrics such as basal area (BA), volume, and aboveground biomass (AGB) and for producing wall-to-wall maps for validation of satellite-derived estimates of forest measures. However, some studies have shown that in mixed forests, estimates of forest inventory derived from lidar can be less accurate due to the high variability of growth patterns in multispecies forests. The goal of this study is to produce more accurate wall-to-wall reference maps in mixed forest stands by introducing variables from multispectral imagery into lidar models. Both parametric (multiple linear regression) and non-parametric (Random Forests) modeling techniques were used to estimate BA, volume, and AGB in mixed-species forests in Southern Alabama. Models from Random Forests and linear regression were competitive with one another; neither approach produced substantially better models. Of the best models produced from linear regression, all included a variable for multispectral imagery, though models with only lidar variables were nearly as sufficient for estimating BA, volume, and AGB. In Random Forests modeling, the most important variables were those derived from lidar. The following accuracy was achieved for linear regression model estimates: BA R2 = 0.36, %RMSE = 31.26, volume R2 = 0.45, %RMSE = 35.30, and AGB R2 = 0.41, %RMSE = 31.31. The results of this study show that the addition of multispectral imagery is not substantially beneficial for improving estimates of BA, volume, and AGB in mixed forests and suggests that the investigation of other variables to explain forest variability is necessary.

Average-Value Modeling of Direct-Driven PMSG-Based Wind Energy Conversion Systems

Control design and stability analysis of direct-driven permanent magnet synchronous generator (PMSG)-based wind energy conversion systems (WECSs) typically involve extensive time-domain simulations and frequency-domain analyses. Those studies rely on accurate and computationally efficient models of WECSs. Detailed switching models can accurately describe the transients of power-electronic converters, but they suffer from low computational efficiency due to the repeated switching events. In this paper, a numerically efficient model is developed for a direct-driven PMSG-based WECS. Analytical and parametric average-value modeling techniques are applied to its switching subsystems, including a 12-pulse diode rectifier, a three-phase-interleaved boost converter, a dual three-phase voltage source inverter (VSI), and a crowbar circuit. The average-value models (AVMs) improve the simulation efficiency by representing the terminal characteristics of switching subsystems with a set of algebraic equations. In addition, numerical linearization techniques can be directly employed for them to obtain frequency-domain characteristics. The proposed model is verified against the detailed switching model and existing AVM in MATLAB/Simulink. It is shown to have excellent accuracy in both time-domain and frequency-domain studies while maintaining high computational efficiency.

Nonlinear Modeling of a Plate Heat Exchanger of a District Heating System

The integration of renewable and residual energy sources requires a detailed level of knowledge of district heating components such as thermal pipes, pipes, pumps, and heat exchanger of a substation. The thermal energy stored in these components can be utilized to match the heat demand and heat generation in time for next generation district heating networks. The goal of this paper is to provide accurate substation models that can be utilized for controlled energy exchange between primary and secondary networks. The proposed method introduces an efficient, user-friendly, combined parametric and nonparametric nonlinear data-driven modeling technique illustrated on a plate heat exchanger which is installed in the district heating network of the Vrije Universiteit Brussel. A fair comparison between a physics-based white-box and a data-driven black-box modeling techniques is provided. The white-box model is developed on the first principle modeling approach of the heat balance equations, which allows calculating the temperature evolution and heat transfer in the plate heat exchanger. As for the data-driven modeling approach, there are four interrelated consecutive steps. First, the measurement is processed to obtain a frequency response function model. Second, a linear parametric (state-space) model is estimated. Third, a polynomial nonlinear model is built. Last, a decoupled model structure is obtained based on the previously obtained PNLSS model. The proposed black-box method outperforms the white-box techniques in terms of prediction capabilities and computational needs.

Exploratory Data Analysis Based Short-Term Electrical Load Forecasting: A Comprehensive Analysis

Power system planning in numerous electric utilities merely relies on the conventional statistical methodologies, such as ARIMA for short-term electrical load forecasting, which is incapable of determining the non-linearities induced by the non-linear seasonal data, which affect the electrical load. This research work presents a comprehensive overview of modern linear and non-linear parametric modeling techniques for short-term electrical load forecasting to ensure stable and reliable power system operations by mitigating non-linearities in electrical load data. Based on the findings of exploratory data analysis, the temporal and climatic factors are identified as the potential input features in these modeling techniques. The real-time electrical load and meteorological data of the city of Lahore in Pakistan are considered to analyze the reliability of different state-of-the-art linear and non-linear parametric methodologies. Based on performance indices, such as Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE), the qualitative and quantitative comparisons have been conferred among these scientific rationales. The experimental results reveal that the ANN–LM with a single hidden layer performs relatively better in terms of performance indices compared to OE, ARX, ARMAX, SVM, ANN–PSO, KNN, ANN–LM with two hidden layers and bootstrap aggregation models.

Field Electromagnetic Strength Variability Measurement and Adaptive Prognostic Approximation with Weighed Least Regression Approach in the Ultra-high Radio Frequency Band

Propagated electromagnetic signal over the cellular radio communication channels and interfaces are usually highly stochastic and complex with unequal noise variation pattern. This is due to multipath nature of the propagation channels and diverse radio propagation mechanisms that impact the signal strength at the receiver en-route the transmitter, and verse versa. This also makes measurement, predictive modeling and estimation based analysis of such signal very challenging and complex as well. One important and popular parametric modelling and estimation technique in mathematics and engineering science, especially for signal processing applications is the least square regression (LSR). The dominance use and popularity of the LSR approach may be attributed to its simplified supporting theory, relatively fast application procedure and ubiquitous application packages. However, LSR is known to be very sensitive to outliers and unusual stochastic signal data. In this work, we propose the application of weighted least square regression method for enhanced propagation practical field strength estimation modelling over cellular radio communication networks interface. The signal data was collected from a commercial LTE networks service provider. Also, we provide statistical computational analyses to compare the resultant estimation outcome of the weighted least square and the standard least approach. From the result, it is found that the WLSR approach is reliably better the most popular standard least square method. The significance and academic of value of this paper is that our proposed and implemented WLSR method can used as replacement of the standard LSR approach for robust mobile signal processing of future communication system networks.

Simplified parametric modeling to predict vibration attenuation provided by on-grade slabs

When measured vibration amplitudes at the proposed site for a highly sensitive facility exceed the maximum allowable for the sensitive equipment, mitigation measures must be integrated into the design that will reduce the vibration amplitudes to meet the requirements. Past studies have shown that thick concrete slabs supported on a well-engineered subgrade can effectively reduce ground borne vibrations at certain frequencies. Predicting the vibration performance of a new slab-on-grade can be a significant challenge, however, as the performance is highly dependent upon the site soil conditions and nature of the vibration sources impacting the location, as well as structural characteristics. Large and detailed three-dimensional finite element analysis models of the site conditions and proposed structure are often used for this type of assessment, however development of such a model requires significant time and cost to develop accurately. This presentation discusses a proposed an alternative simplified parametric modeling technique using two-dimensional plane strain modeling. This technique can be utilized in combination with real-world data to predict the relative benefit of various slab thicknesses and design features such as structural breaks. The paper will include multiple comparisons between predicted results using this methodology and field measurement results.

PARAMETRIC COST MODELLING OF COMPONENTS FOR TURBOMACHINES: PRELIMINARY STUDY

AbstractThe ever-increasing competitiveness, due to the market globalisation, has forced the industries to modify their design and production strategies. Hence, it is crucial to estimate and optimise costs as early as possible since any following changes will negatively impact the redesign effort and lead time.This paper aims to compare different parametric cost estimation methods that can be used for analysing mechanical components. The current work presents a cost estimation methodology which uses non-historical data for the database population. The database is settled using should cost data obtained from analytical cost models implemented in a cost estimation software. Then, the paper compares different parametric cost modelling techniques (artificial neural networks, deep learning, random forest and linear regression) to define the best one for industrial components.Such methods have been tested on 9 axial compressor discs, different in dimensions. Then, by considering other materials and batch sizes, it was possible to reach a training dataset of 90 records. From the analysis carried out in this work, it is possible to conclude that the machine learning techniques are a valid alternative to the traditional linear regression ones.

Hierarchical model updating strategy of complex assembled structures with uncorrelated dynamic modes

In structural simulation and design, an accurate computational model directly determines the effectiveness of performance evaluation. To establish a high-fidelity dynamic model of a complex assembled structure, a Hierarchical Model Updating Strategy (HMUS) is developed for Finite Element (FE) model updating with regard to uncorrelated modes. The principle of HMUS is first elaborated by integrating hierarchical modeling concept, model updating technology with proper uncorrelated mode treatment, and parametric modeling. In the developed strategy, the correct correlated mode pairs amongst the uncorrelated modes are identified by an error minimization procedure. The proposed updating technique is validated by the dynamic FE model updating of a simple fixed–fixed beam. The proposed HMUS is then applied to the FE model updating of an aeroengine stator system (casings) to demonstrate its effectiveness. Our studies reveal that (A) parametric modeling technique is able to build an efficient equivalent model by simplifying complex structure in geometry while ensuring the consistency of mechanical characteristics; (B) the developed model updating technique efficiently processes the uncorrelated modes and precisely identifies correct Correlated Mode Pairs (CMPs) between FE model and experiment; (C) the proposed HMUS is accurate and efficient in the FE model updating of complex assembled structures such as aeroengine casings with large-scale model, complex geometry, high-nonlinearity and numerous parameters; (D) it is appropriate to update a complex structural FE model parameterized. The efforts of this study provide an efficient updating strategy for the dynamic model updating of complex assembled structures with experimental test data, which is promising to promote the precision and feasibility of simulation-based design optimization and performance evaluation of complex structures.

Incorporating Prior Knowledge in Local Parametric Modeling for Frequency Response Measurements: Applied to Thermal/Mechanical Systems

Frequency response function (FRF) identification is a key step in experimental modeling of many applications, including mechatronic systems. Applying these techniques to systems where measurement time is limited leads to a situation where the accuracy of the identified model is deteriorated by transient dynamics. This article aims to develop an identification procedure that mitigates these transient dynamics by employing local parametric modeling techniques. To improve the modeling accuracy, prior knowledge is suitably incorporated in the procedure while at the same time allowing for rational parameterizations that maintain a closed-form solution. The prior knowledge is exploited in a relevant local frequency range using a specific Möbius transformation. Preexisting methods, including the commonly used local polynomial method, are recovered as a special case. The presented framework leads to accurate identification results in a simulation study as well as on experimental measurement data.

The Conception of a 3d Cad Model of Custom Designed Fasteners Using Freecad Software and Parametric Modeling Techniques

The Conception of a 3d Cad Model of Custom Designed Fasteners Using Freecad Software and Parametric Modeling Techniques

Estimating Production and Warranty Cost at the Early Stage of a New Product Development Project

Abstract The paper is concerned with estimating the cost of production and warranty in the context a new product development project. All possible variants of the trade-off between costs are sought within the company’s resources and requirements for a product development project. A company and its projects can be specified in terms of variables and constraints that constitute the systems approach for a problem related to cost optimization. This problem is described in the form of a constraint satisfaction problem and implemented with the use of parametric modelling and constraint programming techniques. The paper also presents a method for estimating the cost of prototyping, faulty products in manufacturing and the after-sales stage, and simulating variants that ensure the desirable level of costs. An example shows the applicability of the proposed approach in the context of a product development project.

Nonlinear Static Analysis and Structural Optimization of Rigid Clamp Band Connection Device between Launch Vehicle and Satellite

The Rigid Clamp Band Connection Device (RCBCD) is a novel satellite-rocket connection method adapted to the heavy lift trend of the launch vehicle. The static analysis model for the Rigid Clamp Band (RCB) and the Docking Ring (DR) under preloading state is established, and the structural strength and connection stiffness characteristics under the axial loading are analyzed, and the axisymmetric equivalent and parametric modeling techniques are combined to optimize the section shape parameter, which improves the overall connection performance. The results show that the structural stress concentrated on the "line-to-face" contact position between RCB and DR. Increasing the axial dimension of RCB, reducing the V-type angle and deepening the occlusion depth of RCB and DR can effectively improve the connection rigidity of RCB. By optimizing the section shape, the connection performance of RCBCD can be improved by above 70% under the structural strength and mass constraints.

Assessing the effect of snowfall and cold temperature on a commuter highway traffic volume using several layers of statistical methods

Presented in this paper is an application of the parametric and non-parametric statistical modelling technique to assess and diagnosis the impact of snowfall and cold temperature on classified vehicle volume variations with five years of Weigh-in-Motion (WIM) data recorded at the commuter type highway nearby the City of Leduc in Alberta, Canada. The influence of the winter driving conditions on classified vehicle volumes travelling in mixed traffic stream is examined. The first phase involves developing parametric dummy-variable regression models to relate and then quantify the variations of daily traffic volumes associated with snowfall and temperature. The second phase involves utilizing a non-parametric Chi-square test combined with the Binomial and the Friedman test. Two levels of statistical tests commonly revealed that the total traffic and passenger car volumes are influenced by both snowfall and temperature, but the truck traffic volumes are not affected significantly. The results of this study may contribute to presenting a new statistical methodology for observing changes in highway traffic in winter. It can also provide important insights for highway agencies to develop efficient traffic monitoring programs throughout the year.

Development and Research of EMU Honeycomb Floor Structure Mechanic Simulation Modular

Basing on PYTHON coding, honeycomb floor static and dynamic strength simulation modular had been developed. Modular automatic analysing, database parallel saving and parametric modelling technique were introduced. By means of the modular, component-level actual loading test was simulated and results of test and simulation were compared. It was presented that result deviation between test and simulation was small and accuracy of the modular could be verified. References of related honeycomb floor design and simulation could be provided.

КОНСТРУКТИВНО-ТЕХНОЛОГІЧНІ ОСОБЛИВОСТІ МЕТАЛЕВИХ ЛОПАТЕЙ НЕСУЧОГО І РУЛЬОВОГО ГВИНТІВ ВЕРТОЛЬОТА

Analysis of the foreign and domestic work experience, manufacturing and operation of the helicopters with metal blades of the main (MR) and tail (TR) rotors is performed. General requirements for the helicopter MR and TR blades design are formulated. Structural arrangement of the helicopter MR metal blade is reviewed, features of the construction materials for the metal blades are noted. Features of the MR and TR metal blades design with the pressed spar and aluminum honeycomb core are given. Parametric modeling technique of the helicopter MR metal blade is presented. The manufacturing rout technology and the method of the surface strengthening of the metal blade tip are presented. The scheme and the manufacturing rout technology of the pressed aluminum spar are given; the geometrical twist features, the surface strengthening and the assembling of the spar with the blade tip are re- viewed. The features of the electric-heating patch bonding on the spar leading edge are shown. Following technological steps of the blade metal rear area manufacturing are reviewed: the rolled aluminum foil degreasing; the aluminum honeycomb structures manufacturing; the aluminum honeycomb core butt milling. The features of the blade rear area assembling and metal blade assembling by bonding in the jig are presented; the content of works outside of the jig for metal blade of the helicopter main rotor is given.

Multiphysics Parametric Modeling of Microwave Components Using Combined Neural Networks and Transfer Function

This paper proposes a new technique to develop an accurate multiphysics parametric model for microwave components to speed up the multiphysics modeling process. In the proposed technique, the artificial neural networks (ANNs) and pole/residue based transfer function are incorporated to represent the highly non-linear relationships between electromagnetic centric (EM-centric) multiphysics behaviors and multiphysics geometrical/non-geometrical design parameters. Vector fitting technique is utilized to obtain the poles/residues of the transfer function for each multiphysics sample. Since the relationship between multiphysics design parameters and the pole/residues of the transfer function is non-linear and unknown, two mapping functions are proposed to establish the mathematical links between the multiphysics design parameters and poles/residues. Parallel multiphysics data generation is proposed to generate the training and testing data for establishing the proposed multiphysics parametric model. A two stage training algorithm is proposed to guide the multiphysics training process. Once an accurate overall model is developed, it can be used to provide accurate and fast prediction of the multiphysics behavior of microwave components with geometrical and non-geometrical parameters as variables, and further can be used in the high level design. Compared with the existing multiphysics modeling methods, the proposed technique can achieve better model accuracy with high efficiency. The proposed technique provides an accurate and efficient methodology even when the coarse model or empirical model is unavailable. Two microwave examples are used to illustrate the validity of the proposed multiphysics parametric modeling technique.

Prediction of the sound insulation of panel structures based on the virtual testing room

The sound transmission through an elastically bounded panel between rooms is presented in this paper. A fully-coupled modal is used to investigate the effect of elastic boundary of the panel on the sound transmission by using Spectro-Geometric Method (SGM) which is a meshless and parametric modeling technique for general vibro-acoustic analysis. Unlike the traditional modal superposition methods, the continuity of the normal velocities is faithfully enforced on the interfaces between the flexible panel and the (interior and exterior) acoustic media. The sound insulation structures can represent a simple panel or a complex structure with various structural and/or acoustical design features. This model can be effectively used to virtually test the acoustic performance of sound insulation designs under different environments and application conditions.

Permutation and randomization tests for network analysis

Permutation tests have a long history in testing hypotheses of independence between nodal attributes and network structure, though they are often thought less informative than parametric modeling techniques. In this paper, we show that when the nodal attribute is random assignment to a treatment condition, permutation tests provide a valid test of the causal effect of treatment. We discuss existing test statistics used in network permutation tests and propose several new statistics. In simulations we find that these statistics perform well compared to parametric tests and that specific statistics can be selected to provide power against common network models. We illustrate the methods with gene-wide association study performed on randomized study participants and an observational study of gender membership on Scandinavian corporate boards.

Light field display with ellipsoidal mirror array and single projector.

We present a method to create light field display using a single projector and an array of plane mirrors. Mirrors can reproduce densely arranged virtual projectors regardless of the physical size of the real projector, thus producing a light field display of competitive ray density. We propose an ellipsoidal geometric framework and a design pipeline, and use parametric modelling technique to automatically generate the display configurations satisfying target design parameters. Three units of mirror array light field display systems have been implemented to evaluate the proposed methodologies. More importantly, we have experimentally verified that the high-density light field produced by our method can naturally evoke accommodation of the eyes, thereby reducing the vergence-accommodation conflict. The mirror array approach allows flexible trading between the spatial and angular resolutions for accommodating different applications, thus providing a practical solution to realize projection-based light field display.

An Adapted Loss Function for Censored Quantile Regression

ABSTRACTIn this article, we study a novel approach for the estimation of quantiles when facing potential right censoring of the responses. Contrary to the existing literature on the subject, the adopted strategy of this article is to tackle censoring at the very level of the loss function usually employed for the computation of quantiles, the so-called “check” function. For interpretation purposes, a simple comparison with the latter reveals how censoring is accounted for in the newly proposed loss function. Subsequently, when considering the inclusion of covariates for conditional quantile estimation, by defining a new general loss function the proposed methodology opens the gate to numerous parametric, semiparametric, and nonparametric modeling techniques. To illustrate this statement, we consider the well-studied linear regression under the usual assumption of conditional independence between the true response and the censoring variable. For practical minimization of the studied loss function, we also provide a simple algorithmic procedure shown to yield satisfactory results for the proposed estimator with respect to the existing literature in an extensive simulation study. From a more theoretical prospect, consistency and asymptotic normality of the estimator for linear regression are obtained using several recent results on nonsmooth semiparametric estimation equations with an infinite-dimensional nuisance parameter, while numerical examples illustrate the adequateness of a simple bootstrap procedure for inferential purposes. Lastly, an application to a real dataset is used to further illustrate the validity and finite sample performance of the proposed estimator. Supplementary materials for this article are available online.