Robust Parameter Set Research Articles

Macroscopic traffic flow model calibration using different optimization algorithms

This study tests and compares different optimization algorithms employed for the calibration of a macroscopic traffic flow model. In particular, the deterministic Nelder–Mead algorithm, a stochastic genetic algorithm and the stochastic cross-entropy method are utilized to estimate the parameter values of the METANET model for a particular freeway site, using real traffic data. The resulting models are validated using various traffic data sets and the optimization algorithms are evaluated and compared with respect to the accuracy of the produced validated models as well as the convergence speed and the required computation time. The validation results showed that all utilized optimization algorithms were able to converge to robust model parameter sets, albeit achieving different performances considering the convergence speed and the required computation time.

Robust Parameter Estimation Framework of a Rainfall-Runoff Model Using Pareto Optimum and Minimax Regret Approach

This study developed a robust parameter set (ROPS) selection framework for a rainfall-runoff model that considers multi-events using the Pareto optimum and minimax regret approach (MRA). The calibrated parameter sets based on the Nash-Sutcliffe coefficient (NSE) for two events were derived using a genetic algorithm. We generated 41 combinations for weighting values between two events for the multi-event objective function and derived 41 Pareto optimum points that were considered as the ROPS candidates. Then, two different approaches for parameter selection were proposed to determine the ROPS among the candidates: one uses NSE only and the other uses four performance measures (NSE, peak flow error, root mean square error and percentage of bias). In the NSE-only method, five events, including two events from the calibration set and three events from the evaluation set, were used, and the ROPS was selected based on the regrets of both the calibration and the evaluation sets. In the multiple (i.e., four) performance measure method, only three events from the evaluation set were used and the ROPS was determined based on the regrets of twelve different cases, including three events with four measures. As a result, while single- and multi-event optimizations produced satisfying results for the calibration events, the optimized parameters from the single-event calibration do not perform well for another event, even one with the same criteria, such as NSE. The results of this study suggest that the optimized parameter set from the well-weighted objective function can successfully simulate not only hydrographs in general but also others, such as peak flow. In addition, the ROPS can be selected by considering the multiple performance measures of multiple validation events, as well as the NSE only of multiple calibration and validation events. Note that the study provides a framework that could be performed reasonably well with a limited number of events. While the computational resources might not be a limiting factor these days, it is still valuable to have such a tool for several reasons: one could utilize it for an operational decision making support tool, as the full searches for an optimal set of parameters might not be performed in the operational facility. It could also be used in a situation where one has a limited number of good-quality observational data for some reason.

A catalogue of 2D photometric decompositions in the SDSS-DR7 spectroscopic main galaxy sample: preferred models and systematics

We present a catalogue of two-dimensional, point spread function-corrected de Vacouleurs, S\'{e}rsic, de Vacouleurs+Exponential, and S\'{e}rsic+Exponential fits of $\sim7\times10^5$ spectroscopically selected galaxies drawn from the Sloan Digital Sky Survey (SDSS) Data Release 7. Fits are performed for the SDSS r band utilizing the fitting routine GALFIT and analysis pipeline PYMORPH. We compare these fits to prior catalogues. Fits are analysed using a physically motivated flagging system. The flags suggest that more than 90 per cent of two-component fits can be used for analysis. We show that the fits follow the expected behaviour for early and late galaxy types. The catalogues provide a robust set of structural and photometric parameters for future galaxy studies. We show that some biases remain in the measurements, e.g. the presence of bars significantly affect the bulge measurements although the bulge ellipticity may be used to separate barred and non-barred galaxies, and about 15 percent of bulges of two-component fits are also affected by resolution. The catalogues are available in electronic format. We also provide an interface for generating postage stamp images of the 2D model and residual as well as the 1D profile. These images can be generated for a user-uploaded list of galaxies on demand.

Stacking Interactions between Adenines in Oxidized Oligonucleotides

The effects of stacking interactions on the oxidation potentials of single strand oligonucleotides containing up to four consecutive adenines, alternated with thymines and cytosines in different sequences and ratios, have been determined by means of differential pulse voltammetry. Voltammetric measurements point toward the establishment in solution of structured oligonucleotide conformations, in which the nucleobases are well stacked altogether. Molecular dynamics simulations confirm that finding, indicating that single strands assume geometrical parameters characteristic of the B-DNA form. The analysis of the voltammetric signals in terms of a simple effective tight binding quantum model leads one to infer a robust set of parameters for treating hole transfer in one-electron-oxidized DNA containing adenines and thymines.

Evaluation of the multiobjective ant colony algorithm performances on biobjective quadratic assignment problems

The difficulty of resolving the multiobjective combinatorial optimization problems with traditional methods has directed researchers to investigate new approaches which perform better. In recent years some algorithms based on ant colony optimization (ACO) metaheuristic have been suggested to solve these multiobjective problems. In this study these algorithms have been reported and programmed both to solve the biobjective quadratic assignment problem (BiQAP) instances and to evaluate the performances of these algorithms. The robust parameter sets for each 12 multiobjective ant colony optimization (MOACO) algorithms have been calculated and BiQAP instances in the literature have been solved within these parameter sets. The performances of the algorithms have been evaluated by comparing the Pareto fronts obtained from these algorithms. In the evaluation step, a multi significance test is used in a non hierarchical structure, and a performance metric (P metric) essential for this test is introduced. Through this study, decision makers will be able to put in the biobjective algorithms in an order according to the priority values calculated from the algorithms’ Pareto fronts. Moreover, this is the first time that MOACO algorithms have been compared by solving BiQAPs.

Application of SVR, Taguchi loss function, and the artificial bee colony algorithm to resolve multiresponse parameter design problems: a case study on optimizing the design of a TIR lens

Multiresponse parameter design problems have become increasingly important and have received considerable attention from both researchers and practitioners since there are usually several quality characteristics that must be optimized simultaneously in most modern products/processes. This study applies support vector regression (SVR), Taguchi loss function, and the artificial bee colony (ABC) algorithm to develop a six-staged procedure that resolves these common and complicated parameter design problems. SVR is used to model the mathematical relationship between input control factors and output responses, and the ABC algorithm is used to find the optimal control factor settings by searching the well-constructed SVR models in which the Taguchi loss function is applied to evaluate the overall performance of a product/process. The feasibility and effectiveness of the proposed approach are demonstrated via a case study in which the design of a total internal reflection (TIR) lens is optimized while fabricating an MR16 light-emitting diode lamp. Experimental results indicate that the proposed solution procedure can provide highly robust design parameter settings for TIR lenses that can be directly applied in real manufacturing processes. Comparisons with the Taguchi method reveal that the Taguchi method is an undesirable and inappropriate method for resolving multiple-response parameter design problems, while the ABC algorithm can search the solution spaces in continuous domains modeled via SVR instead of in the limited discrete experiment levels, thus finding a more robust design than that obtained by the traditional analysis of variance. Consequently, the proposed integrated approach in this study can be considered feasible and effective and can be popularized as a useful tool for resolving general multiresponse parameter design problems in the real world.

A hybrid intelligent approach for optimizing the fine-pitch copper wire bonding process with multiple quality characteristics in IC assembly

Gold is the primary material used for wire bonding in integrated circuit (IC) assembly. Owing to the high appreciation in the price of gold, copper (Cu) wire has become an important substitute material in order to save on manufacturing costs. However, an average of 40% in yield loss during IC assembly can be attributed to improper control of the Cu wire bonding process. To assure cost savings without losing yield, and ensure cost-effective IC assembly, optimization of the parameters for the Cu wire process is critical. This work proposes a hybrid intelligent approach to derive robust parameter settings for a fine-pitch Cu wire bonding process with multiple quality characteristics. The proposed methodology utilizes grey relational analysis and an entropy measurement method to convert the multiple responses into a single synthetic performance index without involving the subjective judgment of an engineer and causing unbalanced improvements of the responses. An integrated neural network model and genetic algorithm method is then applied to acquire the optimal parameter settings. The performance of this method is evaluated experimentally and the results compared with that of the response surface methodology and original parameter settings. The results confirm the feasibility and practicality of this strategy to improve production yield and process capability during Cu wire bonding.

Nondimensional characterization of flow-mode magnetorheological/electrorheological fluid dampers

Magnetorheological/electrorheological dampers are complex devices and involve a large set of important material and geometric variable with mutual interactions between them. As such, reliable predictions of the damping force level in these devices are difficult to achieve. However, meaningful results can be obtained with significantly less effort through nondimensional parameters involving all key variables. Therefore, the goal of this study was to propose a robust set of nondimensional parameters for the purpose of modeling of magnetorheological/electrorheological dampers (and other flow-mode devices) as well as the characterization of data from experiments with magnetorheological/electrorheological devices. The proposed scheme employs five parameters characterizing the contribution of flow inertia, viscosity, and yield stress, as well as shear thinning/thickening effects to the damping force output of magnetorheological/electrorheological valves. It is the result of analysis of several constitutive models of non-Newtonian fluid models (Bingham plastic model, biviscous model, biplastic Bingham model, and Herschel–Bulkley model). Specifically, the goal was to derive analytical (exact) formulae for pressure gradient of all examined models excluding the Herschel–Bulkley model. In the Herschel–Bulkley model, the nondimensional relationship between pressure gradient and flow rate is given in a power-law form, and the analytical (exact) solution cannot be obtained. Prior art included analytical (exact) solutions for the Bingham plastic model only. In the most generic form, the expressions can be useful for designing magnetorheological/electrorheological flow-mode devices. Exemplary calculations of the damping force output are presented in this article for a custom single-gap magnetorheological piston. The piston contains a semi-bypass feature in the annulus to allow for low-breakaway forces at near-zero piston velocity inputs. The steady-state calculations are presented for two exemplary damper units, and the model is validated against experimental data. Finally, the expressions allow one to easily characterize flow data into separate regimes of damper operation by means of the proposed scheme.

Constrained probabilistic multi-objective optimization of shot peening process

Shot peening is a cold working process used for improving the fatigue strength of metallic components. An optimum set of peening parameters must increase the residual compressive stress (RCS), but reduce the surface roughness and cold work for improving the fatigue strength. The optimization is made robust to avoid any unfeasible solution that may arise out of random variations of input variables. The current study uses the well-known Design and Analysis of Computer Experiments (DACE) methodology for optimization, which is better than the conventional Design of Experiments (DoE) approach. It employs a finite element method based unit cell approach to determine the RCS, surface roughness and cold work of a given material. Radial basis functions are used to develop the meta-models. A genetic algorithm (GA) is employed for finding a robust and optimum set of shot peening parameters for a given material. With this approach, the operator will achieve the optimum solution specified by the designer.

A hybrid machining center for enabling new die manufacturing and repair concepts

This paper presents an analysis of a scanner-based laser cladding process and its integration into a hybrid machining center. After deducing a robust set of process parameters from experiments for scanner-based cladding which reliably yield defined geometries, we present an automation architecture to encapsulate the hardware that is specific to this process. We then describe a planning approach in CAM software to facilitate using the scanner-based cladding process within a planning framework for milling processes. After introducing a hybrid laser tool which enables new stabilizing process possibilities we conclude with an approach to combine all these findings into a seamless machine concept of which we present a prototype.

A Flexible, Corpus-Driven Model of Regular and Inverse Selectional Preferences

We present a vector space–based model for selectional preferences that predicts plausibility scores for argument headwords. It does not require any lexical resources (such as WordNet). It can be trained either on one corpus with syntactic annotation, or on a combination of a small semantically annotated primary corpus and a large, syntactically analyzed generalization corpus. Our model is able to predict inverse selectional preferences, that is, plausibility scores for predicates given argument heads. We evaluate our model on one NLP task (pseudo-disambiguation) and one cognitive task (prediction of human plausibility judgments), gauging the influence of different parameters and comparing our model against other model classes. We obtain consistent benefits from using the disambiguation and semantic role information provided by a semantically tagged primary corpus. As for parameters, we identify settings that yield good performance across a range of experimental conditions. However, frequency remains a major influence of prediction quality, and we also identify more robust parameter settings suitable for applications with many infrequent items.

Characterisation of Electrospun Poly(lactic acid) Nanofibre Networks

Electrospinning has been regarded as a convenient method of manufacturing polymer-based multi-functional and high performance nanofibres that can significantly contribute to the world of advanced materials. The primary requirement of the process is to obtain nanofibres in continuous form with fine diameters and minimum variations. Secondly, the fibre network has to have minimum area occupied by beads to enhance the network's porosity. These two important characteristics, when achieved, render the nanofibre mats acceptable for many membrane type applications. The relationship between processing parameters and microstructures of nanofibrils is still not well understood. The goals of this study are to obtain a set of robust manufacturing parameters that would reduce the variation in quality while electrospinning non-woven mats of nanofibres from poly(L-lactic) acid (PLLA). The study involves sixteen sets of parametric combinations and the scanning electron microscopy of the produced mats. The desirable combination for producing acceptable networks appears to be low concentration of polymer solution, low feed rate, comparatively high applied voltage and a large distance (within the studied range) between the collector and the needle. However, a low concentration of polymer solution may result in some bead formation if other factors are not changed accordingly.

Robust parameter design of micro-injection molded gears using a LIGA-like fabricated mold insert

Micro-injection molding, with advantages of easy mass production and low cost, is a key technology for producing micro components. Nevertheless, a low yield rate of high-quality molded parts is common due to problems associated with geometric precision, molecular orientation, and optical properties. Solutions to such problems must consider the machine, mold design, and process parameter settings. However, optimal performance becomes relatively less attainable when process parameters deviate due to inevitable process tolerances and change in an operation environment. This study has two goals: (1) fabrication of high-precision mold inserts using UV-LIGA; and, (2) identification of robust parameters that ensure production quality. Two gear mold-inserts with outside diameters of approximate 6 mm and 4 mm, named as the 6 mm and 4 mm gears, are introduced. First, lithography of thick photoresist SU8-50 is utilized as the initial structure seed layer to electroform the Ni gear mold insert. Second, this study investigates two key geometrical dimensions-the outside diameter and tooth thickness of the molded gears. The robust optimization of multiple objectives is introduced to identify robust parameter settings with high dimensional accuracy and high yield rates for molded gears despite process parameter deviations. Experimental results indicate that electroformed Ni mold inserts have good quality and are successfully utilized in the subsequent micro-injection molding process, demonstrating the feasibility of the mold inserts fabricated using UV-LIGA. The micro-injection molding experiments suggest that mold temperature, holding pressure, and injection speed have significant effects on dimensional quality characteristics of molded gears. The robust parameters derived from the proposed method increase yield rates of the 6 mm gear from 10% to 91%, and those of the 4 mm gear from 38% to 93% in comparison with the initial point, thereby demonstrating application effectiveness.

Computer-Assisted Venous Thrombosis Volume Quantification

Venous thrombosis (VT) volume assessment, by verifying its risk of progression when anticoagulant or thrombolytic therapies are prescribed, is often necessary to screen life-threatening complications. Commonly, VT volume estimation is done by manual delineation of few contours in the ultrasound (US) image sequence, assuming that the VT has a regular shape and constant radius, thus producing significant errors. This paper presents and evaluates a comprehensive functional approach based on the combination of robust anisotropic diffusion and deformable contours to calculate VT volume in a more accurate manner when applied to freehand 2-D US image sequences. Robust anisotropic filtering reduces image speckle noise without generating incoherent edge discontinuities. Prior knowledge of the VT shape allows initializing the deformable contour, which is then guided by the noise-filtering outcome. Segmented contours are subsequently used to calculate VT volume. The proposed approach is integrated into a system prototype compatible with existing clinical US machines that additionally tracks the acquired images 3-D position and provides a dense Delaunay triangulation required for volume calculation. A predefined robust anisotropic diffusion and deformable contour parameter set enhances the system usability. Experimental results pertinence is assessed by comparison with manual and tetrahedron-based volume computations, using images acquired by two medical experts of eight plastic phantoms and eight in vitro VTs, whose independently measured volume is the reference ground truth. Results show a mean difference between 16 and 35 mm(3) for volumes that vary from 655 to 2826 mm(3). Two in vivo VT volumes are also calculated to illustrate how this approach could be applied in clinical conditions when the real value is unknown. Comparative results for the two experts differ from 1.2% to 10.08% of the smallest estimated value when the image acquisition cadences are similar.

Parametric study of manufacturing poly(lactic) acid nanofibrous mat by electrospinning

Electrospinning is a versatile method for manufacturing polymer-based multi-functional and high-performance nanofibrillar network. Two important characteristics, namely minimum diameter variation and bead area, render the nanofibre mats acceptable for many membrane type applications, but the relationship between processing parameters and microstructures is still not well understood. This article outlines a systematic study via the design of experiments in the context of selecting process control parameters while electrospinning nonwoven mats of nanofibres from poly(l-lactic acid). The goals are to obtain a robust set of parameters to reduce the variation in product quality by performing the minimum number of experiments. A desirable combination has been found to be low concentration of polymer solution, low feed rate, comparatively high applied voltage and a large distance between the collector and the needle. However, a low concentration of polymer solution may result in some bead formation if other factors are not changed accordingly.

Near‐surface soil moisture assimilation for quantifying effective soil hydraulic properties using genetic algorithms: 2. Using airborne remote sensing during SGP97 and SMEX02

Pixel‐based effective soil hydraulic parameters are crucial inputs for large‐scale hydroclimatic modeling. In this paper, we extend/apply a genetic algorithm (GA) approach for estimating these parameters at the scale of an airborne remote sensing (RS) footprint. To estimate these parameters, we used a time series of near‐surface RS soil moisture data to invert a physically based soil‐water‐atmosphere‐plant (SWAP) model with a (multipopulated) modified‐microGA. Uncertainties in the solutions were examined in two ways: (1) by solving the inverse problem under various combinations of modeling conditions in a respective way; and (2) the same as the first method but the inverse solutions were determined in a collective way aimed at finding the robust solutions for all the modeling conditions (ensembles). A cross validation of the derived soil hydraulic parameters was done to check their effectiveness for all the modeling conditions used. For our case studies, we considered three electronically scanned thinned array radiometer (ESTAR) footprints in Oklahoma and four polarimetric scanning radiometer (PSR) footprints in Iowa during the Southern Great Plains 1997 (SGP97) Hydrology Experiment and Soil Moisture Experiment 2002 (SMEX02) campaigns, respectively. The results clearly showed the promising potentials of near‐surface RS soil moisture data combined with inverse modeling for determining average soil hydrologic properties at the footprint scale. Our cross validation showed that parameters derived by method 1 under water table (bottom boundary) conditions are applicable also for free‐draining conditions. However, parameters derived under free‐draining conditions generally produced too wet near‐surface soil moisture when applied under water table conditions. Method 2, on the other hand, produced robust parameter sets applicable for all modeling conditions used. These results were validated using distributed in situ soil moisture and soil hydraulic properties measurements, and texture‐based data from the UNSODA database. In this study, we conclude that inverse modeling of RS soil moisture data is a promising approach for parameter estimation at large measurement support scale. Nevertheless, the derived effective soil hydraulic parameters are subject to the uncertainties of remotely sensed soil moisture data and from the assumptions used in the soil‐water‐atmosphere‐plant modeling. Method 2 provides a flexible framework for accounting these sources of uncertainties in the inverse estimation of large‐scale soil hydraulic properties. We have illustrated this flexibility by combining multiple data sources and various modeling conditions in our large‐scale inverse modeling.

Tuning a parametric Clarke–Wright heuristic via a genetic algorithm

Almost all heuristic optimization procedures require the presence of a well-tuned set of parameters. The tuning of these parameters is usually a critical issue and may entail intensive computational requirements. We propose a fast and effective approach composed of two distinct stages. In the first stage, a genetic algorithm is applied to a small subset of representative problems to determine a few robust parameter sets. In the second stage, these sets of parameters are the starting points for a fast local search procedure, able to more deeply investigate the space of parameter sets for each problem to be solved. This method is tested on a parametric version of the Clarke and Wright algorithm and the results are compared with an enumerative parameter-setting approach previously proposed in the literature. The results of our computational testing show that our new parameter-setting procedure produces results of the same quality as the enumerative approach, but requires much shorter computational time.

Discrete parameterization of hydrological models: Evaluating the use of parameter sets libraries over 900 catchments

This article describes an alternative to the optimization strategies classically adopted to calibrate the parameters of rainfall‐runoff models. This new method, called discrete parameterization, relies on the sole use of the prior information on parameters gained on other catchments. The optimum parameter set is simply searched within a collection (a library) of predefined optima. This library is composed of parameter sets representing a large number of actual catchments. The method was tested on a set of 900 catchments (from Australia, France, and the United States) using two daily lumped rainfall‐runoff models and was compared to more classical calibration approaches. Results are very similar for both models. Although the discrete parameterization method is not as efficient as a classical global search calibration approach when long time series are available for calibration, it provides more robust parameter sets when flow time series available for calibration becomes shorter than 2 years. This makes the method particularly interesting in the cases of poorly gauged catchments where available flow records are short. In case of limited data, the advantage of the proposed approach over the classical calibration approaches was more significant for the more complex model. On our set of 900 catchments, the optimum parameter set is generally selected among parameter sets from the library corresponding to catchments spatially close to the studied catchment. However other criteria of physical similarity may be relevant to select the donor catchment in the library.

Parametric modelling of the geometrical ice-ocean interaction in the Ekstroemisen grounding zone based on short time-series

SUMMARY Due to the impact of ocean tide the Ekstroemisen (Ekstroem Ice Shelf, Antarctica) still shows vertical displacements at its assumed grounding line. These can reach amplitudes of 5 cm. The low tide amplitudes are smaller than the corresponding high tide amplitudes. This indicates that the ice body touches the bedrock during the low tides. An elastic feedback of the ice body can be observed as little as 1 km behind the assumed grounding line. The maximum vertical displacements can still reach 1 cm. These results are based on data collected in the grounding zone of the Ekstroemisen during the austral summer of 1997. This region has been studied by using a combined sensor field that makes continuous records using Global Positioning System (GPS), gravimeter and tiltmeter instruments to derive the elastic response of the ice body to the ocean tides. The resulting time-series which has to be analysed is biased by outliers, incomplete data or complex trend functions. It is nearly impossible to analyse the very short time periods without assuming hypotheses. It turns out that standard algorithms like correlation functions, various Fourier transforms or functional regressions are not suitable to solve the parametrization of time-series in the region of the grounding zone, where several kinematic and gravimetric processes overlap. After a thorough analysis of these time-series with an average length of 8 d we managed to homogenize the results of the hybrid and non-simultaneous observations. We were able to create a set of robust parameters to describe the ice shelf motion in the grounding zone, based on non-linear programming.

A Model for Perceptual Averaging and Stochastic Bistable Behavior and the Role of Voluntary Control

We combine population coding, winner-take-all competition, and differentiated inhibitory feedback to model the process by which information from different, continuously variable signals is integrated for perceptual awareness. We focus on "slant rivalry," where binocular disparity is in conflict with monocular perspective in specifying surface slant. Using a robust single parameter set, our model successfully replicates three key experimental results: (1) transition from signal averaging to bistability with increasing signal conflict, (2) change in perceptual reversal rates as a function of signal conflict, and (3) a shift in the distribution of percept durations through voluntary control exertion. Voluntary control is implemented through the use of a single top-down bias input. The transition from signal averaging to bistability arises as a natural consequence of combining population coding and wide receptive fields, common to higher cortical areas. The model architecture does not contain any assumption that would limit it to this particular example of stimulus rivalry. An emergent physiological interpretation is that differentiated inhibitory feedback may play an important role for increasing percept stability without reducing sensitivity to large stimulus changes, which for bistable conditions leads to increased alternation rate as a function of signal conflict.