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. 相似文献
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 . 相似文献
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. 相似文献
Power system control equipment needs higher sensitivity and operational reliability. Advanced voltage control equipment is needed for reducing the frequency of tap changes and improving the characteristics (the relationship between the actual voltage and reference voltage) of the voltage to meet today's power system requirements. However, these objectives are in a trade-off relationship. Studies of voltage control derived from a knowledge base suitable for electric power systems can satisfy these objectives using fuzzy inference. Compared with corresponding conventional equipment, the new equipment improved the deviation of 30 min average voltage of 30 percent. This paper describes the design concept of new voltage control equipment using fuzzy inference. In addition, field test results are described along with rules of fuzzy inference, membership functions, and the deviation of 30 min average voltage through detailed simulation. 相似文献
In this paper, definitions of strongly fuzzy convergent sequence, l-fuzzy weakly convergent sequence and l-fuzzy weakly compact set are given in a fuzzy normed linear space. The concepts of fuzzy normal structure, fuzzy non-expansive mapping, uniformly convex fuzzy normed linear space are introduced and fixed point theorems for fuzzy non-expansive mappings are proved. 相似文献
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.相似文献
In today's competitive business environment, it is important that customers are able to obtain their preferred items in the shops they visit, particularly for convenience store chains such as 7–Eleven where popular items are expected to be readily available on the shelves of the stores for buyers. To minimize the cost of running such store chains, it is essential that stocks be kept to a minimum and at the same time large varieties of popular items are available for customers. In this respect, the replenishment system needs to be able to cope with the taxing demands of minimal inventory but at the same time keeping large varieties of needed items. This paper proposes a replenishment system which is able to respond to the fluctuating demands of customers and provide a timely supply of needed items in a cost–effective way. The proposed system embraces the principle of fuzzy logic which is able to deal with uncertainties by virtue of its fuzzy rules reasoning mechanism, thereby leveraging the responsiveness of the entire replenishment system for the chain stores. To validate the feasibility of the approach, a case study has been conducted in an emulated environment with promising results. 相似文献
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). 相似文献