基于SQL的多条件选择智能实现算法

B/S架构的信息系统开发中,一般情况下,根据用户的选择,n个条件的输入组合数是2n个。但是,该文中提出的算法,其组合数仅为n到n(n+1)/2,大大简化了编程。     

An improved new error estimation algorithm for optimal filter lengths for stereophonic acoustic echo cancellation

In this paper, we propose an new error estimate algorithm (NEEA) for stereophonic acoustic echo cancellation (SAEC) that is based on the error estimation algorithm (EEA) in [Nguyen-Ky T, Leis J, Xiang W. An improved error estimate algorithm for stereophonic acoustic echo cancellation system. In: International conference on signal processing and communication systems, ICSPCS’2007, Australia; December 2007]. In the EEA and NEEA, with the minimum error signal fixed, we compute the filter lengths so that the error signal may approximate the minimum error signal. When the echo paths change, the adaptive filter automatically adjusts the filter lengths to the optimum values. We also investigate the difference between the adaptive filter lengths. In contrast with the conclusions in [Khong AWH, Naylor PA. Stereophonic acoustic echo cancellation employing selective-tap adaptive algorithms. IEEE Trans Audio, Speech, Lang Process 2006;14(3):785-96, Gansler T, Benesty J. Stereophonic acoustic echo cancellation and two channel adaptive filtering: an overview. Int J Adapt Control Signal Process 2000;4:565-86, Benesty J, Gansler T. A multichannel acoustic echo canceler double-talk detector based on a normalized cross-correlation matrix. Acoust Echo Noise Control 2002;13(2):95-101, Gansler T, Benesty J. A frequency-domain double-talk detector based on a normalized cross-correlation vector. Signal Process 2001;81:1783-7, Eneroth P, Gay SL, Gansler T, Benesty J. A real-time implementation of a stereophonic acoustic echo canceler. IEEE Trans. Speech Audio Process 2001;9(5):513-23, Gansler T, Benesty J. New insights into the stereophonic acoustic echo cancellation problem and an adaptive nonlinearity solution. IEEE Trans. Speech Audio Process 2002; 10(5):257-67, Benesty J, Gansler T, Morgan DR, Sondhi MM, Gay SL. Advances in network and acoustic echo cancellation. Berlin: Springer-Verlag; 2001], our simulation results have shown that the filter lengths can be different. Our simulation results also confirm that the NEEA is better than EEA and SM-NLMS algorithm in terms of echo return loss enhancement.     

WBMOAIS: A novel artificial immune system for multiobjective optimization

This study presents a novel weight-based multiobjective artificial immune system (WBMOAIS) based on opt-aiNET, the artificial immune system algorithm for multi-modal optimization. The proposed algorithm follows the elementary structure of opt-aiNET, but has the following distinct characteristics: (1) a randomly weighted sum of multiple objectives is used as a fitness function. The fitness assignment has a much lower computational complexity than that based on Pareto ranking, (2) the individuals of the population are chosen from the memory, which is a set of elite solutions, and a local search procedure is utilized to facilitate the exploitation of the search space, and (3) in addition to the clonal suppression algorithm similar to that used in opt-aiNET, a new truncation algorithm with similar individuals (TASI) is presented in order to eliminate similar individuals in memory and obtain a well-distributed spread of non-dominated solutions. The proposed algorithm, WBMOAIS, is compared with the vector immune algorithm (VIS) and the elitist non-dominated sorting genetic system (NSGA-II) that are representative of the state-of-the-art in multiobjective optimization metaheuristics. Simulation results on seven standard problems (ZDT6, SCH2, DEB, KUR, POL, FON, and VNT) show WBMOAIS outperforms VIS and NSGA-II and can become a valid alternative to standard algorithms for solving multiobjective optimization problems.     

Optimization of optical lens-controlled scanning electron microscopic resolution using generalized regression neural network and genetic algorithm

A scanning electron microscope (SEM) is a sophisticated equipment employed for fine imaging of a variety of surfaces. In this study, prediction models of SEM were constructed by using a generalized regression neural network (GRNN) and genetic algorithm (GA). The SEM components examined include condenser lens 1 and 2 and objective lens (coarse and fine) referred to as CL1, CL2, OL-Coarse, and OL-Fine. For a systematic modeling of SEM resolution (R), a face-centered Box–Wilson experiment was conducted. Two sets of data were collected with or without the adjustment of magnification. Root-mean-squared prediction error of optimized GRNN models are GA 0.481 and 1.96×10^-12 for non-adjusted and adjusted data, respectively. The optimized models demonstrated a much improved prediction over statistical regression models. The optimized models were used to optimize parameters particularly under best tuned SEM environment. For the variations in CL2 and OL-Coarse, the highest R could be achieved at all conditions except a larger CL2 either at smaller or larger OL-Coarse. For the variations in CL1 and CL2, the highest R was obtained at all conditions but larger CL2 and smaller CL1.     

Discussions of worker ants’ rule-based CHC dealing with changing environments

Contrary to popular belief, biologists discovered that worker ants are really not all hardworking. It has been found that in three separate 30-strong colonies of black Japanese ants (Myrmecina nipponica), about 20% of worker ants are diligent, 60% are ordinary, and 20% are lazy. That is called 20:60:20 rule. Though they are lazy, biologists suggested that lazy worker ants could be contributing something to the colony that is yet to be determined. In our last research, we used CHC (cross generational elitist selection, heterogeneous recombination, and cataclysmic mutation) with the worker ants’ rule (WACHC) aiming at solving optimization problems in changing environments. CHC is a nontraditional genetic algorithm (GA) which combines a conservative selection strategy that always preserves the best individuals found so far with a radical (highly disruptive) recombination operator. In our last research, we verified that WACHC performs better than CHC in only one case of fully changing environment. In this paper, we further discuss our proposed WACHC dealing with changing environment problems with varying degree of difficulty, compare our proposal with hypermutation GA which is also proposed for dealing with changing environment problems, and discuss the difference between our proposal and ant colony optimization algorithms.     

高速网络Fast TCP拥塞控制算法的改进

随着网络技术的飞速发展和接入性能的不断提高,如今全世界的互联主干网络呈现出一种高速网络的特性。在这种网络特性下,传统网络中的TCP拥塞控制协议已经开始显现出不适应性,发现了FAST TCP协议中＂持续拥塞＂的形成机制,建立了数学模型来准确的描述＂持续拥塞＂所导致数据流之间带宽分配不公平的问题,进一步利用优先服务队列的方法解决了FAST TCP在网络中的持续拥塞现象。     

混沌权值变异的Huffman树图像加密算法

提出一种采用混沌权值变异的Huff man树的图像加密算法.此算法在熵编码过程中,以基本的Huffman树为标准,利用双耦合混沌序列1对DC系数进行树的结构未变异、路径值变异的加密;再利用双耦合混沌序列2对AC系数进行码字序号的加密.论文对算法进行了仿真,并对安全性、计算复杂度、压缩比性能进行了分析.实验结果表明,该算法基本上不影响压缩效率,且计算复杂度低、安全性高和实时性好,可用于网络上的图像服务.     

An inexact hybrid projection-proximal point algorithm for solving generalized mixed variational inequalities

In this paper, we investigate an inexact hybrid projection-proximal method for solving a class of generalized mixed variational inequalities in Hilbert spaces. We construct a general inexact hybrid projection-proximal point algorithm, in which an inexact relaxed proximal point step is followed by a suitable orthogonal projection onto a hyperplane. Under some suitable conditions concerned with the pseudomonotone set-valued mapping T, the nonsmooth convex function f and the step size λ_k, we prove the convergence of the inexact hybrid projection-proximal point algorithm for solving generalized mixed variational inequalities in Hilbert spaces.     

Growing fuzzy topology adaptive resonance theory models with a push-pull learning algorithm

A new incrementally growing neural network model, called the growing fuzzy topology ART (GFTART) model, is proposed based on integrating the conventional fuzzy ART model with the incremental topology-preserving mechanism of the growing cell structure (GCS) model. This is in addition, to a new training algorithm, called the push-pull learning algorithm. The proposed GFTART model has two purposes: First, to reduce the proliferation of incrementally generated nodes in the F2 layer by the conventional fuzzy ART model based on replacing each F2 node with a GCS. Second, to enhance the class-dependent clustering representation ability of the GCS model by including the categorization property of the conventional fuzzy ART model. In addition, the proposed push-pull training algorithm enhances the cluster discriminating property and partially improves the forgetting problem of the training algorithm in the GCS model.     

A genetic algorithm with gene rearrangement for K-means clustering

In this paper, a new clustering algorithm based on genetic algorithm (GA) with gene rearrangement (GAGR) is proposed, which in application may effectively remove the degeneracy for the purpose of a more efficient search. A new crossover operator that exploits a measure of similarity between chromosomes in a population is also presented. Adaptive probabilities of crossover and mutation are employed to prevent the convergence of the GAGR to a local optimum. Using the real-world data sets, we compare the performance of our GAGR clustering algorithm with K-means algorithm and other GA methods. An application of the GAGR clustering algorithm in unsupervised classification of multispectral remote sensing images is also provided. Experiment results demonstrate that the GAGR clustering algorithm has high performance, effectiveness and flexibility.