Global Optimal Parameters Research Articles

Long‐Term Sorption and Sequestration Dynamics of the Antibiotic Sulfadiazine: A Batch Study

Understanding the long-term sequestration of veterinary antibiotics into soil fractions with different bioavailability is important in terms of assessing their eco-toxicological impact. We performed 60-d batch sorption experiments with radiolabeled sulfadiazine (SDZ) using samples from two agricultural soils. Sequential extraction with CaCl/MeOH (easily accessible fraction), microwave (residual fraction, RES), and combustion (nonextractable residues, NER) was used to quantify the sequestration dynamics of the C-derived SDZ-equivalent concentration. Multiple harsh extractions allowed us to mathematically extrapolate to the amount of SDZ equivalents that can be potentially extracted, resulting in halving the NER fraction after 60 d. A modified two-stage model with irreversible sorption combined with global parameter optimization was able to display the sequestration dynamics. We demonstrated this with sterilized samples in which no transformation of the parent compound was observed. This also showed that transformation was primarily biologically driven. These modeling results verified the procedure, which was then applied to nontreated samples from both soils to estimate effective parameter values for SDZ-derived equivalents. Observed initial sorption, to which up to 20% of the kinetic sorption sites attributed, was included in the model. Both the RES and NER fractions reached a sorption plateau, with NER occupying about 30% of the kinetic fraction (RES+NER) for all soils. The sorption and sequestration of SDZ were soil-specific and dominated by kinetics. Sequestration in the RES fraction was much slower (characteristic time: 60 d) than the redistribution in the NER fraction (characteristic time: <6 d). The work presented here contributes to the prediction of the dynamics of (bio-)availability.

Modeling Carbon Dioxide Production and Transport in a Mixed-Grass Rangeland Soil

The ability to quantify soil–plant–atmosphere water, heat, and gaseous exchange is important for understanding the linkages between terrestrial ecosystems and Earth’s climate. We combined a one-dimensional vertical soil water flow and heat transport algorithm with a soil CO 2 production and transport algorithm to study water, heat, and CO 2 exchange for vegetated areas in cold climates that are subject to seasonal snow accumulation and soil water freeze–thaw. The model was applied to a semiarid high-elevation mixed-grass rangeland near Laramie, WY. Calibration was achieved by optimizing seven parameters describing plant growth respiration, soil profile CO 2 production due to roots and microbes, and the depth distribution of roots and microbes using global parameter optimization. Modeling efficiency (ME) for the calibration and validation periods was high for soil temperature (ME = 0.93–0.96) and variable for soil water content (ME = −0.93 to 0.74), soil CO 2 concentration (ME = 0.01–0.59), ecosystem respiration ( R eco , ME = −0.71 to 0.59), and snow height (ME = 0.09). The calculated annual C budget for 2 yr yielded a gross primary production (GPP) of 92 to 107, an R eco of 183 to 188, and a net ecosystem exchange (NEE) of −81 to −90 g C m −2 yr −1 . The NEE values, suggesting that the site served as a significant C source, were deemed unrealistic considering the fact that precipitation for the site was about average for both years. Sensitivity analysis was used to explore the impact of N-limitation and soil–plant-respiration parameters on GPP, R eco , and NEE.

Global optimisation by simulated annealing for common reflection surface stacking and its application to low-fold marine data in southwest Japan

The common reflection surface (CRS) stack is an alternative method of producing a zero-offset stacked section with a higher signal-to-noise ratio (SNR) than the conventional normal moveout (NMO)/dip moveout (DMO) stack method. Since, however, it is difficult to determine global optimal parameters for the CRS stack method by the conventional three-step search method, especially for complex structures and low-fold data, we investigate the ability of simulated annealing (SA) to optimise our estimation of these parameters. We show a detailed but practical procedure for the application of SA to the CRS stack method. We applied the CRS stack method with SA to numerically modelled seismic reflection data, and to multichannel marine seismic data over complicated geological structures around the Median Tectonic Line (MTL) in Japan. We used the results of the conventional three-step search algorithm as the initial model for the SA search and showed that with this approach SA can estimate CRS parameters accurately within a reasonable number of calculations. The CRS stack method with this approach provided a clearer seismic profile with a higher SNR than either a conventional NMO stack method or a conventional CRS stack method.

Reactive Transport Parameter Estimation and Global Sensitivity Analysis Using Sparse Polynomial Chaos Expansion

We present in this paper a new strategy based on the use of polynomial chaos expansion (PCE) for both global sensitivity analysis and parameter optimization. To limit the number of evaluations of the direct model, we develop a simple and efficient procedure to construct a sparse PCE where only coefficients that have a significant contribution to the variance of the model are retained. Parameter estimation is performed using an adaptive procedure where the intervals of variation of the parameters are progressively reduced using information from sensitivity analysis calculated using the sparse PCE. The strategy is shown to be effective for the parameter estimation of two reactive transport problems: a synthetic reactive transport problem involving the Freundlich sorption isotherm and a field experiment of Valocchi et al. (Water Resources Research 17:1517–1527, 1981) involving nonlinear ion exchange reactions.

Calibration of second order traffic models using continuous cross entropy method

Second order macroscopic traffic flow models are often used to replicate non-linear traffic flow phenomena such as phantom traffic jams or traffic instabilities. In contrast to the (first order) Lighthill–Whitham–Richards (LWR) traffic model, which assumes an equilibrium speed-density relationship (or so-called fundamental diagram), the second order model uses one more dynamic equation to describe the evolution of the speed, therefore allows the speed to fluctuate around the equilibrium diagram. In general, in the second order model, a given model parameter set may exhibit traffic instabilities due to a small initial traffic perturbation (e.g. lane-changing or sudden deceleration). Therefore, small changes of parameter set in second order models will lead to completely different model performance, which consequently leads to a complex calibration effort and hence prohibits its real-life application as compared to the LWR model. So far relatively few calibration results for general macroscopic traffic flow models have been reported. To contribute to the state-of-the-art, this paper puts forward an effort to find global optimal parameters of a second order macroscopic traffic model using a stochastic optimization approach, namely cross entropy method (CEM). Basically, the CEM is set up to solve combinatorial optimization problems so the main novelty of this paper is to apply the CEM to solve continuous multi-extremal optimization problems in transportation through the use of the Kernel density estimation method. Numerical studies are carried out to show that the Kernel-based CEM can search for the global optimal model parameters in a second order model and is a promising method for the calibration of traffic models in general.

A Novel Defogging Algorithm Based on Genetic Algorithm with Analysis of Scientific Data Materials

This paper proposes a novel defogging algorithm based on the improved model with analysis of scientific data materials. By integrating the merit of genetic algorithm for searching global optimal parameters, the problem of fog-degraded images defogging restoration is transformed into the problem of optimization estimation for original undegraded image by maximizing the global contrast object function, the proposed algorithm can restore the object image as complete as possible in probability sense. Experimental results for single object image defogging gain satisfy visual effect.

Optimal Design of T-S Fuzzy Controller Based on Improved Genetic Algorithm

To solving the problem that there had been too many undetermined parameters in the fuzzy control rules, it presented a simplified Takagi-Sugno, namely T-S, fuzzy reasoning method. It reduced the parameters of the IF-THEN rules greatly. In addition this paper also improved the genetic algorithm on the analysis of the prior genetic algorithm, by which the global optimal parameters of the controller can be found easily and quickly thus the control rules can be amended and perfected. The simulation results show that the improved genetic algorithm can find the optimal parameters at a high speed and the optimized T-S fuzzy controller can obtain an excellent control performance.

Modeling dynamic urban growth using cellular automata and particle swarm optimization rules

This paper presents an improved cellular automata (CA) model of urban growth based on particle swarm optimization (PSO) approach with inertia weight. An innovative feature of this cellular model is the incorporation of swarm intelligence to stochastically optimize the transition rules to reduce the simulation uncertainties and improve its locational accuracy in urban modeling. The similarity between the nature of self-organization of particle swarm optimizers and the bottom-up approach of cellular automata makes PSO particularly suitable to search for the global optimum parameters of CA transition rules. The CA parameters retrieved by the PSO technique are able to express precisely the contributions of various driving forces to urban growth; hence an effective cellular model can be realized for modeling urban dynamics. The PSO based CA model was applied in Fengxian District of Shanghai Municipality, eastern China, to simulate the spatio-temporal process of urban growth from 1992 to 2008 at 30 m spatial resolution. The simulation outcomes, evaluated with error matrix and simulation accuracies, demonstrate that the PSO-CA model outperforms other spatial statistical based CA models.

Estimating continuous highway vertical alignment using the least-squares method

Highway profile information may not be available or may not be up to date, especially for old highways. In these cases, profile data are collected using global positioning systems (GPS) or by extracting them from digital images. This paper presents an optimization model for estimating the parameters of continuous vertical alignments, involving multiple parabolic vertical curves that best fit existing highway profile data using the least-squares method. The optimization involves two levels: single-curve optimization and multiple-curve optimization. The former is used to obtain the approximate length of each vertical curve based on the approximate tangent parameters determined from the outline controlling points. The latter is used to obtain the global optimal parameters for tangents and vertical curves. The model is validated and applied using actual data of a vertical alignment. The proposed model presents a useful extension to existing methods for estimating simple vertical alignments and therefore should be of interest to highway engineering professionals.

Pairwise optimized Rocchio algorithm for text categorization

This paper examines the Rocchio algorithm and its application in text categorization. Existing approaches using global parameters optimization of Rocchio algorithm result in choosing one fixed prototype representing each category for multi-category text categorization problems. Therefore, they have limited discriminating power on different category’s distribution and their parameter optimization methods are based on weak representation ability of the negative samples consisting of several categories. We present a pairwise optimized Rocchio algorithm, which dynamically adjusts the prototype position between pairs of categories. Experiments were conducted on three benchmark corpora, the 20-Newsgroup, Reuters-21578 and TDT2. The results confirm that our proposed pairwise method achieves encouraging performance improvement over the conventional Rocchio method. A comparative study with the top notch text classifier Support Vector Machine (SVM) also shows the pairwise Rocchio method achieves competitive results.

Multi-response design of Nd:YAG laser drilling of Ni-based superalloy sheets using Taguchi’s quality loss function, multivariate statistical methods and artificial intelligence

This paper presents a hybrid design strategy for the determination of the optimum laser drilling parameters which simultaneously meets the requirements for seven quality characteristics (responses) of the holes produced during pulsed Nd:YAG laser drilling of a thin sheet of nickel-based superalloy Nimonic 263. The process was designed using two approaches based on the experimental data. In the first approach, the quality losses of seven correlated responses were uncorrelated into a set of components using the principal component analysis; then the grey relational analysis was applied to synthesise components into a synthetic performance measure. Since this approach considered only parameter values used in the experiment, the second approach was developed to find the global optimal parameters solution using an artificial neural network to model the relation between parameters and a synthetic performance measure, and a genetic algorithm to perform a search for the global optimum in a continual multidimensional space. The analysis of the application indicated that the proposed approaches gave a better result, in terms of the optimal parameter settings that yield the maximal synthetic performance measure, than several commonly used methods for multi-response process parameters design. The results demonstrated that the robust Nd:YAG laser drilling of Ni-based superalloy sheets was designed with respect to the requirements for seven quality characteristics of the drilled holes, by using the proposed strategy.

Global Optimization of Pavement Structural Parameters during Back-Calculation Using Hybrid Shuffled Complex Evolution Algorithm

In this paper, the use of a hybrid evolutionary optimization algorithm is proposed for global optimization of pavement structural parameters through inverse modeling. Shuffled complex evolution (SCE) is a population-based stochastic optimization technique combining the competitive complex evolution with the controlled random search, the implicit clustering, and the complex shuffling. Back-calculation of pavement layer moduli is an ill-posed inverse engineering problem, which involves searching for the optimal combination of pavement layer stiffness solutions in an unsmooth, multimodal, complex search space. SCE is especially considered a robust and efficient approach for global optimization of multimodal functions. A desirable characteristic of the SCE algorithm is that it uses information about the nature of the response surface, extracted using the deterministic Simplex geometric shape, to direct the search into regions with higher posterior probability. The hybrid back-calculation system described in this paper combines the robustness of the SCE in global optimization with the computational efficiency of neural networks and advanced pavement system characterization offered by employing finite-element models. This is the first time the SCE approach is applied to real-time nondestructive evaluation of pavement systems required in the routine maintenance and rehabilitation activities for sustainable transportation infrastructure.

Size Effects on the Thermal Properties of Self-assembled Ge Quantum Dots in Single-crystal Silicon

AbstractSuperlattices with an ultra-low thermal conductivity were extensively studied to design thermoelectric materials. However, since they are made up of superposed materials showing lattice mismatches, they often show cracks and dislocations. Therefore, it is challenging to fabricate superlattices with a thermoelectric figure of merit ZT higher than unity. Moreover, like nanowires, they decrease heat transport in only one main direction. Self-assembly from epitaxial layers on a Si substrate is a major bottom-up technology to fabricate 3D Ge quantum-dot (QD) arrays in Si, which have been used for 3D quantum-device applications. Using the model of the atomic-scale 3D phononic crystal, we showed that 3D high-density arrays of self-assembled Ge QDs in Si can also show an ultra-low thermal conductivity in 3D, which can be several orders of magnitude lower than that of bulk Si. As a result, they can be considered to design novel 3D thermoelectric devices showing CMOS compatibility. In an example QD crystal, the thermal conductivity can be decreased below only 0.2 W/m/K. The main objective of this paper is to show the size dependence of the thermal conductivity versus the supercell lattice parameter d. For a constant QD-crystal filling ratio x = 12.5 at%, a decrease of the thermal conductivity is observed for an increasing d. This analysis enables us to predict that the optimal d-value will be of the order of 11 nm for the given filling ratio. At this optimum, the thermal conductivity decreases to the global minimum value of 0.9 W/m/K. The presented results are a first step towards the optimal design of thermoelectric devices with a maximal ZT obtained by global optimization of the size parameters.

Profile conditional random fields for modeling protein families with structural information.

A statistical model of protein families, called profile conditional random fields (CRFs), is proposed. This model may be regarded as an integration of the profile hidden Markov model (HMM) and the Finkelstein-Reva (FR) theory of protein folding. While the model structure of the profile CRF is almost identical to the profile HMM, it can incorporate arbitrary correlations in the sequences to be aligned to the model. In addition, like in the FR theory, the profile CRF can incorporate long-range pair-wise interactions between model states via mean-field-like approximations. We give the detailed formulation of the model, self-consistent approximations for treating long-range interactions, and algorithms for computing partition functions and marginal probabilities. We also outline the methods for the global optimization of model parameters as well as a Bayesian framework for parameter learning and selection of optimal alignments.

Global Minimization of Longitudinal Emittance in RF Linac Structures

Relativistic electron bunches in linear accelerators are characterized by 6-D phase spaces. In most linear accelerators, the longitudinal phase space distribution does not interact significantly with the transverse distributions. This assumption allows the use of a 2-D design model of the longitudinal phase space. In this paper a global convergent parameter optimization is proposed to achieve an optimal bunch compression design with respect to an exact bunch energy, peak current and a vanishing skewness in the current profile, minimizing the longitudinal emittance by using a genetic algorithm and sequential quadratic programming hybrid formulation to find an optimal bunch.

Closed-Loop System Identification with Genetic Algorithms

High-performance control design for a flexible space structure is challenging as highfidelity plant models are difficult to obtain a priori. Uncertainty in the control design models typically require a very robust, low-performance control design, which must be tuned onorbittoachievetherequiredperformance.Closed-loopsystemidentificationisoftenrequired to obtain a multivariable open-loop plant model based on closed-loop response data. To provide an accurate initial plant model to guarantee convergence for standard local optimization methods, this paper presents a global parameter optimization method using genetic algorithms. A minimal representation of the state space dynamics is used to mitigate the nonuniqueness and overparameterization of general state space realizations. This controlrelevant system identification procedure stresses the joint nature of the system identification and control design problem by seeking to obtain a model that minimizes the difference between the predicted and actual closed-loop performance.

Modeling of Hydraulic Pipeline Transients Accompanied With Cavitation and Gas Bubbles Using Parallel Genetic Algorithms

Mathematical models of pressure transients accompanied with cavitation and gas bubbles are studied in this paper to describe the flow behavior in a hydraulic pipeline. The reasonable prediction for pressure transients in a low pressure hydraulic pipeline largely depends on several unknown parameters involved in the mathematical models, including the initial gas bubble volumes in hydraulic oils, gas releasing and resolving time constants. In order to identify the parameters in the mathematical models and to shorten the computation time of the identification, a new method—parallel genetic algorithm (PGA)—is applied in this paper. Based on the least-square errors between the experimental data and simulation results, the fitness function of parallel genetic algorithms is programed and implemented. The global optimal parameters for hydraulic pipeline pressure transient models are obtained. The computation time of parallel genetic algorithms is much shorter than that of serial genetic algorithms. By using PGAs, the executing time is 20h. However, it takes about 204h by using GAs. Simulation results with identified parameters obtained by parallel genetic algorithms agree well with the experimental data. The comparison between simulation results and the experimental data indicates that parallel genetic algorithms are feasible and efficient to estimate the unknown parameters in hydraulic pipeline transient models accompanied with cavitation and gas bubbles.

함수 근사화를 위한 방사 기저함수 네트워크의 전역 최적화 기법

This paper proposes a training algorithm for global optimization of the parameters of radial basis function networks. Since conventional training algorithms usually perform only local optimization, the performance of the network is limited and the final network significantly depends on the initial network parameters. The proposed hybrid simulated annealing algorithm performs global optimization of the network parameters by combining global search capability of simulated annealing and local optimization capability of gradient-based algorithms. Via experiments for function approximation problems, we demonstrate that the proposed algorithm can find networks showing better training and test performance and reduce effects of the initial network parameters on the final results.

Water and Solute Transport in a Cultivated Silt Loam Soil: 2. Numerical Analysis

A field experiment was performed to study the effects of soil structure heterogeneity generated by farming practices (i.e., compaction by wheel traffic, plowing, surface tillage) on plot‐scale water flow and solute transport. The experiment involved a 4 by 2 m2 field plot that was uniformly sprinkle irrigated with water and bromide for about 6 h. Independently measured soil hydraulic functions were used to simulate the experiment with a numerical flow and transport model (HYDRUS‐2D) using a fully deterministic approach for describing soil heterogeneity. The numerical model reproduced observed flow and transport processes only after adjustments were made to the soil hydraulic functions. Adjustments were needed to account for increased flow and transport into and through the soil between the compacted zones below the wheel tracks, and to predict double concentration peaks caused by the umbrella (or shadow) effects of compacted soil clods. Global optimization of the soil hydraulic parameters produced a satisfactory description of the very heterogeneous flow patterns, with the resulting hydraulic parameters showing only limited correlation among each other. We demonstrate that double‐peak concentration profiles can result from the presence of tillage‐induced soil heterogeneities.

Rate Control for Real-Time Video Network Transmission on End-To-End Rate-Distortion and Application-Oriented QoS

In the merged multimedia packet-switched networks, the limited or no end-to-end Quality of Service (QoS) guarantees induce that rate control has been evolved to joint source-channel adjustment architecture based on application-oriented QoS. Based on the statistical analysis under the spatial intra and temporal inter prediction subject to a universal spatial-temporal coding framework and a general error concealment by the decoder, an end-to-end distortion estimation model is proposed. On the basis of the analytic model, this paper fulfills a picture quality parameters selection solution with rate-distortion (R-D) Lagrange optimization for a general coding engine including H.264/AVC, exploiting either the source-driven iterative prediction or feedback recursion. Further, a corresponding joint source-channel rate control strategy is proposed. For the real-time variable bit-rate (VBR) video transmission under a given time-varying network condition, the strategy could estimate an instantaneous available transmission rate on traffic smoothing and codec's buffer control, adopt the proposed an end-to-end distortion regressive model and a global optimal error control parameters selection, and address the consistent bit allocation in group of picture (GOP) level, picture level, and MB level. The extensive network simulation experiments show a better and more consistent end-to-end picture quality, in contrast with the locally optimal control strategy at MB level.