On the design of multiple key hashing files for concurrent orthogonal range retrieval between two disks

This paper concerns the following problem: given a set of multi-attribute records, a fixed number of buckets and a two-disk system, arrange the records into the buckets and then store the buckets between the disks in such a way that, over all possible orthogonal range queries (ORQs), the disk access concurrency is maximized. We shall adopt the multiple key hashing (MKH) method for arranging records into buckets and use the disk modulo (DM) allocation method for storing buckets onto disks. Since the DM allocation method has been shown to be superior to any other allocation methods for allocating an MKH file onto a two-disk system for answering ORQs, the real issue is knowing how to determine an optimal way for organizing the records into buckets based upon the MKH concept.

A performance formula that can be used to evaluate the average response time, over all possible ORQs, of an MKH file in a two-disk system using the DM allocation method is first presented. Based upon this formula, it is shown that our design problem is related to a notoriously difficult problem, namely the Prime Number Problem. Then a performance lower bound and an efficient algorithm for designing optimal MKH files in certain cases are presented. It is pointed out that in some cases the optimal MKH file for ORQs in a two-disk system using the DM allocation method is identical to the optimal MKH file for ORQs in a single-disk system and the optimal average response time in a two-disk system is slightly greater than one half of that in a single-disk system.     

Application of the blind source separation method to feature extraction of machine sound signals

As the result of vibration emission in air, a machine sound signal carries important information about the working condition of machinery. But in practice, the sound signal is typically received with a very low signal-to-noise ratio. To obtain features of the original sound signal, uncorrelated sound signals must be removed and the wavelet coefficients related to fault condition must be retrieved. In this paper, the blind source separation technique is used to recover the wavelet coefficients of a monitored source from complex observed signals. Since in the proposed blind source separation (BSS) algorithms it is generally assumed that the number of sources is known, the Gerschgorin disk estimator method is introduced to determine the number of sound sources before applying the BSS method. This method can estimate the number of sound sources under non-Gaussian and non-white noise conditions. Then, the partial singular value analysis method is used to select these significant observations for BSS analysis. This method ensures that signals are separated with the smallest distortion. Afterwards, the time-frequency separation algorithm, converted to a suitable BSS algorithm for the separation of a non-stationary signal, is introduced. The transfer channel between observations and sources and the wavelet coefficients of the source signals can be blindly identified via this algorithm. The reconstructed wavelet coefficients can be used for diagnosis. Finally, the separation results obtained from the observed signals recorded in a semi-anechoic chamber demonstrate the effectiveness of the presented methods .     

Transformations in machining. Part 1. enhancement of wavelet transformation neural network (WT-NN) combination with a preprocessor

Properly selected transformation methods obtain the most significant characteristics of metal cutting data efficiently and simplify the classification. Wavelet Transformation (WT) and Neural Networks (NN) combination was used to classify the experimental cutting force data of milling operations previously. Preprocessing (PreP) of the approximation coefficients of the WT is proposed just before the classification by using the Adaptive Resonance Theory (ART2) type NNs. Genetic Algorithm (GA) was used to estimate the weights of each coefficient of the PreP. The WT-PreP-NN (ART2) combination worked at lower vigilances by creating only a few meaningful categories without any errors. The WT-NN (ART2) combination could obtain the same error rate only if very high vigilances are used and many categories are allowed.     

Solving regularized least squares with qualitatively controlled adaptive cross‐approximated matrices

The adaptive cross‐approximation (ACA) technique is applied to accelerating an inverse‐problem solver that estimates charge distribution on a dielectric spacer. The ACA generates an approximated system‐matrix that enables us to carry out high‐speed inverse calculation. We designed an approximation procedure based on ACA with some additional concepts, that is, (a) partitioning of matrix based on algebraic information, (b) approximation quality control based on matrix norms, and so on. The tested solver (LSQR for regularized least squares) with ACA demonstrates about 10 times faster performance than that without ACA. © 2007 Wiley Periodicals, Inc. Electr Eng Jpn, 159(3): 10–18, 2007; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.20326     

Evolving Fault-Tolerant Neural Networks

In this paper, genetic algorithm is used to help improve the tolerance of feedforward neural networks against an open fault. The proposed method does not explicitly add any redundancy to the network, nor does it modify the training algorithm. Experiments show that it may profit the fault tolerance as well as the generalisation ability of neural networks.     

Experimental and analytical study on the spray characteristics of dimethyl ether (DME) and diesel fuels within a common-rail injection system in a diesel engine

This paper investigates the effect of injection parameters on the characteristics of dimethyl ether (DME) as an alternative fuel in a diesel engine with experimental and analytical models based on empirical equations. In order to study macroscopic and microscopic characteristics of DME fuel, this work focuses on the atomization characteristics of DME and compares experimental and predicted results for spray development obtained by empirical models for diesel and DME fuel. Detailed comparisons of spray tip penetration from three different empirical correlations and from visualization experiments of diesel and DME fuels were conducted under various fuel injection conditions. In comparison with the results of different empirical equations for measured spray tip penetration, the experimental results of this study provide good agreement with the calculation results based on empirical equations, except during the earliest stage of the injected spray sequence. The results of atomization characteristics indicate that DME showed better spray characteristics than conventional diesel fuel. Also, the fuel injection delay and maximum injection rate of DME fuel are shorter and lower than those of diesel fuel at the same injection conditions, respectively.     

Development of calibration models for quality control in the production of ethylene/propylene copolymers by FTIR spectroscopy,multivariate statistical tools,and artificial neural networks

Principal component regression (PCR), partial least squares (PLS), StepWise ordinary least squares regression (OLS), and back‐propagation artificial neural network (BP‐ANN) are applied here for the determination of the propylene concentration of a set of 83 production samples of ethylene–propylene copolymers from their infrared spectra. The set of available samples was split into (a) a training set, for models calculation; (b) a test set, for selecting the correct number of latent variables in PCR and PLS and the end point of the training phase of BP‐ANN; (c) a production set, for evaluating the predictive ability of the models. The predictive ability of the models is thus evaluated by genuine predictions. The model obtained by StepWise OLS turned out to be the best one, both in fitting and prediction. The study of the breakdown number of samples to be included in the training set showed that at least 52 experiments are necessary to build a reliable and predictive calibration model. It can be concluded that FTIR spectroscopy and OLS can be properly employed for monitoring the synthesis or the final product of ethylene–propylene copolymers, by predicting the concentration of propylene directly along the process line. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2008     

Tuning fuzzy power-system stabilizers in multi-machine systems by global optimization algorithms based on efficient domain partitions

In this paper, the problem of global tuning of fuzzy power-system stabilizers (FPSSs) present in a multi-machine power system in order to damp the power system oscillations is considered. In particular, it is formulated as a problem of global minimization of a multiextremal black-box function over a multidimensional hyperinterval. A global optimization technique, recently proposed, is used for solving the stated problem: the search hyperinterval is partitioned into smaller hyperintervals and the objective function is evaluated only at two vertices corresponding to the main diagonal of the generated hyperintervals, thus avoiding unnecessary ponderous simulations. Then, the performances of this technique are numerically compared with ones of a genetic algorithm (GA).     

直接斜率波前复原算法的控制效果分析

建立自适应光学系统功率谱抑制函数的概念，分析了采用直接斜率波前复原算法的自适应当光学系统的控制效果，理论分析与６１单元自适应光学系统上的实验结果表明，直接斜率波前复原算法将导致控制效果下降。     

3^n阶矩阵乘法的分块算法

针对３＾ｎ阶矩阵的乘法运行，给出了一种分块算法，其乘法运行量比常规的矩阵乘法计算方法和补零的基－２算法都有所减少。