BR Algorithm Research Articles

Neural-Network-Based Approaches for Optimization of Machining Parameters Using Small Dataset.

Surface quality is one of the most important indicators of the quality of machined parts. The analytical method of defining the arithmetic mean roughness is not applied in practice due to its complexity and empirical models are applied only for certain values of machining parameters. This paper presents the design and development of artificial neural networks (ANNs) for the prediction of the arithmetic mean roughness, which is one of the most common surface roughness parameters. The dataset used for ANN development were obtained experimentally by machining AA7075 aluminum alloy under various machining conditions. With four factors, each having three levels, the full factorial design considers a total of 81 experiments that have to be carried out. Using input factor-level settings and adopting the Taguchi method, the experiments were reduced from 81 runs to 27 runs through an orthogonal design. In this study we aimed to check how reliable the results of artificial neural networks were when obtained based on a small input-output dataset, as in the case of applying the Taguchi methodology of planning a four-factor and three-level experiment, in which 27 trials were conducted. Furthermore, this paper considers the optimization of machining parameters for minimizing surface roughness in machining AA7075 aluminum alloy. The results show that ANNs can be successfully trained with small data and used to predict the arithmetic mean roughness. The best results were achieved by backpropagation multilayer feedforward neural networks using the BR algorithm for training.

Prediction of effluent quality in a wastewater treatment plant by dynamic neural network modeling

Improving the operation, management, and consequent performance of wastewater treatment plants (WWTPs) for conserving the water environment is crucial. Recent advancements in artificial intelligence (AI) modeling have shown the potential to solve the non-linear simulation of processes in WWTPs and facilitate real-time operational adjustments. In this study, a dynamic nonlinear autoregressive network with an exogenous input (NARX) model was established for predicting effluent quality. The performance was optimized with different time-delay parameters and training algorithms. Then, a PCA-NARX hybrid model was established for high performance and comparison with two static artificial neural network (ANN) models. The BR algorithm exhibited the highest performance among the four training algorithms for the NARX model. The dynamic PCA-NARX model was significantly superior to static models in modeling effluent quality. The PCA-NARX model predicted the effluent chemical oxygen demand (CODcr) and total nitrogen (TN) with high accuracy (RMSECOD = 2.9 mg/L, RMSETN = 0.8 mg/L). Therefore, we propose a stable and sensitive dynamic neural network model for predicting effluent quality and potential real-time adjustment of wastewater treatment operations.

Assessment of energy consumption and modeling of output energy for wheat production by neural network (MLP and RBF) and Gaussian process regression (GPR) models

The objective of this study was to predict the irrigated and rainfed wheat output energy with three soft computing models include Artificial Neural Network (MLP and RBF models) and Gaussian Process Regression (GPR) for the first time, in Shahreza city, Isfahan province, Iran. Data were collected from an extensive research on wheat farms, including 120 irrigated and 90 rainfed wheat farms (totally 210 questionnares) at three levels (small: < 2ha, medium: 2–4 ha and large: > 4ha) using with face to face questionnaire method. Results of energy analysis showed that diesel fuel was the most influential factor on energy consumption in irrigated wheat production and also for medium and large lands of rainfed wheat production, but for small rainfed lands, total of fertilizers and poisons had the highest impact on total energy consumption. Results of output modeling showed that ANN-RBF model is more accurate than MLP-ANN and GPR models. RMSE and MAPE for irrigated and rainfed output modeling for ANN- RBF were 63.12–72.30 MJ and 0.05–0.14%, respectively. The results of selecting the best spread factor (one of the best parameters on RBF model performance) showed that for irrigated wheat with LM algorithm and at training phase (irrigated-LM-training) and irrigated wheat with BR algorithm and at training phase (irrigated-BR-training), this factor is equal to 7 and 4, respectively. The ANN-RBF model developed was capable of predicting irrigated and rainfed wheat output energy under different land size and using input energies.

A hybrid expectation maximisation and MCMC sampling algorithm to implement Bayesian mixture model based genomic prediction and QTL mapping

BackgroundBayesian mixture models in which the effects of SNP are assumed to come from normal distributions with different variances are attractive for simultaneous genomic prediction and QTL mapping. These models are usually implemented with Monte Carlo Markov Chain (MCMC) sampling, which requires long compute times with large genomic data sets. Here, we present an efficient approach (termed HyB_BR), which is a hybrid of an Expectation-Maximisation algorithm, followed by a limited number of MCMC without the requirement for burn-in.ResultsTo test prediction accuracy from HyB_BR, dairy cattle and human disease trait data were used. In the dairy cattle data, there were four quantitative traits (milk volume, protein kg, fat% in milk and fertility) measured in 16,214 cattle from two breeds genotyped for 632,002 SNPs. Validation of genomic predictions was in a subset of cattle either from the reference set or in animals from a third breeds that were not in the reference set. In all cases, HyB_BR gave almost identical accuracies to Bayesian mixture models implemented with full MCMC, however computational time was reduced by up to 1/17 of that required by full MCMC. The SNPs with high posterior probability of a non-zero effect were also very similar between full MCMC and HyB_BR, with several known genes affecting milk production in this category, as well as some novel genes. HyB_BR was also applied to seven human diseases with 4890 individuals genotyped for around 300 K SNPs in a case/control design, from the Welcome Trust Case Control Consortium (WTCCC). In this data set, the results demonstrated again that HyB_BR performed as well as Bayesian mixture models with full MCMC for genomic predictions and genetic architecture inference while reducing the computational time from 45 h with full MCMC to 3 h with HyB_BR.ConclusionsThe results for quantitative traits in cattle and disease in humans demonstrate that HyB_BR can perform equally well as Bayesian mixture models implemented with full MCMC in terms of prediction accuracy, but with up to 17 times faster than the full MCMC implementations. The HyB_BR algorithm makes simultaneous genomic prediction, QTL mapping and inference of genetic architecture feasible in large genomic data sets.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-3082-7) contains supplementary material, which is available to authorized users.

Implications of breath-by-breath oxygen uptake determination on kinetics assessment during exercise

We tested that the breath-by-breath V(O2) determination by an algorithm termed BR did not bias the kinetics parameters. It was compared to two other algorithms using a correction for changes in lung gas stores between two successive breaths (AU, W) and one without correction (NC). Ten healthy male subjects cycled 10 min at 15 and 30 W below and above ventilatory threshold (VT) after 3 min at 0 W. The breath-by-breath V(O2) variability was lower with BR than the other methods during the last 3 min at each power. V(O2) kinetics was described by a mono-exponential model at power below VT and a bi-exponential model above VT. Differences in parameter were only observed for the primary component between estimates using AU and NC. The between-subject variability in the parameters of the slow component at 15 W above VT was lower with AU and BR than W and NC. It was concluded that the BR algorithm could be used to analyse the V(O2) kinetics during exercise.

Comparison of BR and QR Eigenvalue Algorithms for Power System Small Signal Stability Analysis

The BR algorithm is a novel and efficient method to find all eigenvalues of upper Hessenberg matrices and has never been applied to eigenanalysis for power system small signal stability. This paper analyzes differences between the BR and the QR algorithms with performance comparison in terms of CPU time based on stopping criteria and storage requirement. The BR algorithm utilizes accelerating strategies to improve its performance when computing eigenvalues of narrowly banded, nearly tridiagonal upper Hessenberg matrices. These strategies significantly reduce the computation time at a reasonable level of precision. Compared with the QR algorithm, the BR algorithm requires fewer iteration steps and less storage space without depriving of appropriate precision in solving eigenvalue problems of large-scale power systems. Numerical examples demonstrate the efficiency of the BR algorithm in pursuing eigenanalysis tasks of 39-, 68-, 115-, 300-, and 600-bus systems. Experiment results suggest that the BR algorithm is a more efficient algorithm for large-scale power system small signal stability eigenanalysis

A SINGLE-PASS APPROACH TO ADAPTIVE SIMPLIFICATION OF OUT-OF-CORE MODELS

The algorithm efficiency of existing adaptable out-of-core simplification algorithms is relatively low because these methods often require scanning the original model over one time. In this paper, we present an adaptive clustering method, known as the Balanced Tiling (BT) method, for out-of-core simplification which only needs one pass over the input model. The key idea behind BT is that the model surface can be recorded in a memory efficient manner using surface coding and the global distribution of surface details can be obtained through quadric quantizing of the original model. Besides the feature edge areas, our algorithm can position the corner areas, which are not noticed by some other out-of-core simplification approaches such as the Fei's BR algorithm, by analyzing the Garland's quadric error matrix. Based on this analysis, adaptive clustering is achieved by restoring detail areas with cluster split operations while further simplifying smooth areas with edge collapse operations. BT is especially suitable for handling super huge meshes because the I/O time is greatly reduced. BT produces high quality approximations and the memory requirement is small, being only related with the size of the output model.

The BR Eigenvalue Algorithm

The BR algorithm, a new method for calculating the eigenvalues of an upper Hessenberg matrix, is introduced. It is a bulge-chasing algorithm like the QR algorithm, but, unlike the QR algorithm, it is well adapted to computing the eigenvalues of the narrow-band, nearly tridiagonal matrices generated by the look-ahead Lanczos process. This paper describes the BR algorithm and gives numerical evidence that it works well in conjunction with the Lanczos process. On the biggest problems run so far, the BR algorithm beats the $QR$ algorithm by a factor of 30--60 in computing time and a factor of over 100 in matrix storage space.

Block recursive algorithm to generate Jacobi-sets

The pairs in the set {( i, j)∥ 1 ⩽ i < j ⩽ n} can be distributed into ( n − 1) sets, such that each set contains exactly n/2 disjoint pairs. We refer to these ( n−1) sets as complete Jacobi-sets. Gao and Thomas have [4] developed a recursive algorithm for exchanges of elements on a hypercube configuration to generate complete Jacobi sets. Inspired by the algorithm we present a block recursive algorithm to generate these sets continuously and return to its initial state after the last Jacobi set has been generated. In the process of the derivation of the BR algorithm, we developed some interesting combinatorial properties on the hypercube topology related to the Jacobi-sets. We include those results in this paper. An important application of the Jacobi-sets lies in the parallel implementation for Jacobi-type methods. The order in which the pairs ( i, j) are chosen is important in defining these algorithms. We present in this paper a study on the effect of different orderings on the convergence of Jacobi-type methods. Our tests indicate that the ordering defined by our block recursive algorithm is as good as the odd-even ordering.