A new automatic target recognition system based on wavelet extreme learning machine

In this paper, an automatic system is presented for target recognition using target echo signals of High Resolution Range (HRR) radars. This paper especially deals with combination of the feature extraction and classification from measured real target echo signal waveforms by using X-band pulse radar. The past studies in the field of radar target recognition have shown that the learning speed of feedforward neural networks is in general much slower than required and it has been a major disadvantage. There are two key reasons forth is status of feedforward neural networks: (1) the slow gradient-based learning algorithms are extensively used to train neural networks, and (2) all the parameters of the networks are tuned iteratively by using such learning algorithms (Feng et al., 2009, Huang and Siew, 2004, Huang and Chen, 2007, Huang and Chen, 2008, Huang et al., 2006, Huang et al., 2010, Huang et al., 2004, Huang et al., 2005, Huang et al., 2012, Huang et al., 2008, Huang and Siew, 2005, Huang et al., 2011, Huang et al., 2006, Huang et al., 2006a, Huang et al., 2006b, Lan et al., 2009, Li et al., 2005, Liang et al., 2006, Liang et al., 2006, Rong et al., 2009, Wang and Huang, 2005, Wang et al., 2011, Yeu et al., 2006, Zhang et al., 2007, Zhu et al., 2005). To resolve these disadvantages of feedforward neural networks for automatic target recognition area in this paper suggested a new learning algorithm called extreme learning machine (ELM) for single-hidden layer feedforward neural networks (SLFNs) (Feng et al., 2009, Huang and Siew, 2004, Huang and Chen, 2007, Huang and Chen, 2008, Huang et al., 2006, Huang et al., 2010, Huang et al., 2004, Huang et al., 2005, Huang et al., 2012, Huang et al., 2008, Huang and Siew, 2005, Huang et al., 2011, Huang et al., 2006, Huang et al., 2006a, Huang et al., 2006b, Lan et al., 2009, Li et al., 2005, Liang et al., 2006, Liang et al., 2006, Rong et al., 2009, Wang and Huang, 2005, Wang et al., 2011, Yeu et al., 2006, Zhang et al., 2007, Zhu et al., 2005) which randomly choose hidden nodes and analytically determines the output weights of SLFNs. In theory, this algorithm tends to provide good generalization performance at extremely fast learning speed. Moreover, the Discrete Wavelet Transform (DWT) and wavelet entropy is used for adaptive feature extraction in the time-frequency domain in feature extraction stage to strengthen the premium features of the ELM in this study. The correct recognition performance of this new system is compared with feedforward neural networks. The experimental results show that the new algorithm can produce good generalization performance in most cases and can learn thousands of times faster than conventional popular learning algorithms for feedforward neural networks.     

Improved adaptive vector quantization algorithm using hybrid codebook data structure

Recently, Shen et al. [IEEE Transactions on Image Processing 2003;12:283–95] presented an efficient adaptive vector quantization (AVQ) algorithm and their proposed AVQ algorithm has a better peak signal-to-noise ratio (PSNR) than that of the previous benchmark AVQ algorithm. This paper presents an improved AVQ algorithm based on the proposed hybrid codebook data structure which consists of three codebooks—the locality codebook, the static codebook, and the history codebook. Due to easy maintenance advantage, the proposed AVQ algorithm leads to a considerable computation-saving effect while preserving the similar PSNR performance as in the previous AVQ algorithm by Shen et al. [IEEE Transactions on Image Processing 2003;12:283–95]. Experimental results show that the proposed AVQ algorithm over the previous AVQ algorithm has about 75% encoding time improvement ratio while both algorithms have the similar PSNR performance.     

求解多集分裂可行问题的一种共轭梯度法

基于求解多集分裂可行问题与非线性最优化问题的等价性,考虑Jinling Zhao and Qingzhi Yang在[1]中提出的求解SFP的共轭梯度法和Censor等在[2]中提出的梯度投影法,尝试运用共轭梯度法求解多集分裂可行问题;并且证明了所构造算法的收敛性．提出的新算法克NT求矩阵逆的缺点．初步的数值结果表明新算法对于不同的问题都能够有较快的收敛速度,具有良好的稳定性和可行性,在问题维数增大时表现得越发明显．     

可转换签密的几种改进方案

基于椭圆曲线密码体制建立了几个具有语义安全的可转换签密方案。这些方案都实现了当签密者抵赖其签密时,接收者可将签密转换成普通签名以向第三方证明签密者的欺骗;解决了H-C方案和W-B方案不满足语安全的弱点以及H-C方案不能抵抗已知明文攻击的缺陷;相对目前的一些方案,本文方案具有更小的通信代价和计算代价。     

Note on: An improved algorithm for the packing of unequal circles within a larger containing circle

This note proposed an improved version of the algorithm proposed by Wang et al. [Wang, H. Q., Huang, W. Q., Zhang, Q., & Xu, D. M. (2002). An improved algorithm for the packing of unequal circles within a larger containing circle. European Journal of Operational Research, 141, 339–347] for solving the disk packing problem with equilibrium constraints. An efficient strategy of accelerating the search process is introduced in the gradient method to shorten the execution time. A number of computational results are presented, showing the effectiveness of the proposed method.     

Solving the bi-objective personnel assignment problem using particle swarm optimization

A particle swarm optimization (PSO) algorithm combined with the random-key (RK) encoding scheme (named as PSORK) for solving a bi-objective personnel assignment problem (BOPAP) is presented. The main contribution of this work is to improve the f₁_f₂ heuristic algorithm which was proposed by Huang et al. [3]. The objective of the f₁_f₂ heuristic algorithm is to get a satisfaction level (SL) value which is satisfied to the bi-objective values f₁, and f₂ for the personnel assignment problem. In this paper, PSORK algorithm searches the solution of BOPAP space thoroughly. The experimental results show that the solution quality of BOPAP based on the proposed method is far better than that of the f₁_f₂ heuristic algorithm.     

An efficient fault-containing self-stabilizing algorithm for finding a maximal independent set

An independent set is a useful structure because, in some situations, it defines a set of mutually compatible operations, i.e., operations that can be executed simultaneously. We design a fault-containing self-stabilizing algorithm that finds a maximal independent set for an asynchronous distributed system. Our algorithm is an improvement on the self-stabilizing algorithm in Shukla et al. [1995]. In the single-fault situation, the worst-case stabilization time of Shukla's algorithm is /spl Omega/(n), where n is the number of nodes in the system, whereas the worst-case stabilization time of our algorithm is O(/spl Delta/), where /spl Delta/ is the maximum node degree in the system. Compared also with the fault-containing algorithm that is induced from applying the general transformer in Ghosh et al. [1996] to Shukla's algorithm, our algorithm is also seen to be faster in stabilization time, in the single-fault situation. Therefore, our algorithm can be considered to be the most efficient fault-containing self-stabilizing algorithm for the maximal independent set finding up to this point.     

Computation of incompressible bubble dynamics with a stabilized finite element level set method

This paper presents a stabilized finite element method for the three dimensional computation of incompressible bubble dynamics using a level set method. The interface between the two phases is resolved using the level set approach developed by Sethian [Level Set Methods and Fast Marching Methods, Cambridge University Press, 1999], Sussman et al. [J. Comput. Phys. 114 (1994) 146], and Sussman et al. [J. Comput. Phys. 148 (1999) 81–124]. In this approach the interface is represented as a zero level set of a smooth function. The streamline-upwind/Petrov–Galerkin method was used to discretize the governing flow and level set equations. The continuum surface force (CSF) model proposed by Brackbill et al. [J. Comput. Phys. 100 (1992) 335–354] was applied in order to account for surface tension effects. To restrict the interface from moving while re-distancing, an improved re-distancing scheme proposed in the finite difference context [J. Comput. Phys. 148 (1999) 81–124] is adapted for finite element discretization. This enables us to accurately compute the flows with large density and viscosity differences, as well as surface tension. The capability of the resultant algorithm is demonstrated with two and three dimensional numerical examples of a single bubble rising through a quiescent liquid, and two bubble coalescence.     

Comparative Corrigendum note on papers “Fuzzy adaptive GA for multi-objective assembly line balancing” continued “Modified GA for different types of assembly line balancing with fuzzy processing times”: differences and similarities [Appl. Soft Comput. 34 (September 2015) 655–677]

In the very few recently published paper by Alavidoost et al. [1], they proposed a novel fuzzy adaptive genetic algorithm for multi-objective assembly line balancing problem, in continue of their previous presented work by Alavidoost et al. [2], as a modification on genetic algorithm for assembly line balancing with fuzzy processing times. Despite the fact that both of them are well-written, and completely discussed their contributions, this note looks forward to collate these papers together. Likewise provides the correct order of the figures in [1] matching with their corresponding caption.     

A high order fuzzy time series forecasting model based on adaptive expectation and artificial neural networks

Many fuzzy time series approaches have been proposed in recent years. These methods include three main phases such as fuzzification, defining fuzzy relationships and, defuzzification. Aladag et al. [2] improved the forecasting accuracy by utilizing feed forward neural networks to determine fuzzy relationships in high order fuzzy time series. Another study for increasing forecasting accuracy was made by Cheng et al. [6]. In their study, they employ adaptive expectation model to adopt forecasts obtained from first order fuzzy time series forecasting model. In this study, we propose a novel high order fuzzy time series method in order to obtain more accurate forecasts. In the proposed method, fuzzy relationships are defined by feed forward neural networks and adaptive expectation model is used for adjusting forecasted values. Unlike the papers of Cheng et al. [6] and Liu et al. [14], forecast adjusting is done by using constraint optimization for weighted parameter. The proposed method is applied to the enrollments of the University of Alabama and the obtained forecasting results compared to those obtained from other approaches are available in the literature. As a result of comparison, it is clearly seen that the proposed method significantly increases the forecasting accuracy.     

Erratum to: Drift analysis and average time complexity of evolutionary algorithms: [Artificial Intelligence 127 (2001) 57–85]

The proof of Theorem 6 in the paper by J. He and X. Yao [Artificial Intelligence 127 (1) (2001) 57–85] contains a mistake, although the theorem is correct [S. Droste et al., Theoret. Comput. Sci. 276 (2002) 51–81]. This note gives a revised proof and theorem. It turns out that the revised theorem is more general than the original one given an evolutionary algorithm with mutation probability p_m=1/(2n), using the same proof method as given by J. He and X. Yao [Artificial Intelligence 127 (1) (2001) 57–85].     

An effort prediction framework for software defect correction

This article tackles the problem of predicting effort (in person–hours) required to fix a software defect posted on an Issue Tracking System. The proposed method is inspired by the Nearest Neighbour Approach presented by the pioneering work of Weiss et al. (2007) [1]. We propose four enhancements to Weiss et al. (2007) [1]: Data Enrichment, Majority Voting, Adaptive Threshold and Binary Clustering. Data Enrichment infuses additional issue information into the similarity-scoring procedure, aiming to increase the accuracy of similarity scores. Majority Voting exploits the fact that many of the similar historical issues have repeating effort values, which are close to the actual. Adaptive Threshold automatically adjusts the similarity threshold to ensure that we obtain only the most similar matches. We use Binary Clustering if the similarity scores are very low, which might result in misleading predictions. This uses common properties of issues to form clusters (independent of the similarity scores) which are then used to produce the predictions. Numerical results are presented showing a noticeable improvement over the method proposed in Weiss et al. (2007) [1].     

A note on “Fully fuzzy fixed charge multi-item solid transportation problem”

Giri et al. [P.K. Giri, M.K. Maiti, M. Maiti, Fully fuzzy fixed charge multi-item solid transportation problem, Applied Soft Computing, 27 (2015) 77–91] proposed an approach for solving mathematical programming of fully fuzzy fixed charge multi-item solid transportation problems (FFFCMISTP) and claimed that it is better to use their proposed approach as compared to the existing method [A. Kumar, J. Kaur, P. Singh, A new method for solving fully fuzzy linear programming problems, Applied Mathematical Modelling, 35 (2011) 817–823]. The aim of this note is to point out that Giri et al. have used some mathematical incorrect assumptions in their proposed approach and hence the claim of Giri et al. is not valid.     

Performance Evaluation of GAP-RBF Network in Channel Equalization

A Growing and Pruning Radial Basis Function (GAP-RBF) network has been recently proposed by Huang et al. [IEEE Transactions on Systems, Man and Cybernetics-Part B: Cybernetics, 34(6) (2004), 2284–2292]. However, its performance in signal processing areas is not clear yet. In this paper, GAP-RBF network is used for solving the communication channel equalization problem. The simulation results demonstrate that GAP-RBF equalizer outperforms other equalizers such as recurrent neural network and MRAN on linear and nonlinear channel model in terms of bit error rate.     

Security analysis of the public key algorithm based on Chebyshev polynomials over the integer ring ZN

Recently Kocarev and Tasev [20] proposed to use Chebyshev polynomials over real numbers to design a public key algorithm by employing the semigroup property. Bergamo et al. [4] pointed out that the public key algorithm based on Chebyshev polynomials working on real numbers is not secure and devised an attack which permits to recover the corresponding plaintext from a given ciphertext. Later Kocarev et al. [19] generalized the Chebyshev polynomials from real number fields to finite fields and finite rings to make the public key algorithm more secure and practical. However, we analyzed the period distribution of the sequences generated by the Chebyshev polynomials over finite fields [21]. When the modulus N is prime, we found this algorithm was also not secure and proposed an attack on this algorithm over finite fields. We then proposed some schemes to improve the security. In this paper, we further analyze in detail the period distribution of the sequences generated by Chebyshev polynomials over the integer ring Z_N when N is composite. It turns out that the period distribution is poor if N is not chosen properly and there are many small periods, which are not secure in the sense of cryptology. Based on these findings, we devise an attack on the public key algorithm based on Chebyshev polynomials over the integer ring Z_N. We also propose some suggestions to avoid this attack.     

多变量极点配置自校正控制器

本文讨论了一种新的具有指定极点的多变量自校正控制器的设计方法。它改进了文献所提出的算法。该算法能够控制非最小相位系统,开环不稳定系统和B_0阵为奇异的不同时延系统,即可在线配置闭环极点又可离线配置闭环极点。同时,本文还表明文献给出的算法可以认为是本文结果的特例。最后给出计算机仿真结果,说明这种控制器的实用性和有效性。     

On a simple randomized algorithm for finding a 2-factor in sparse graphs

We analyze the performance of a simple randomized algorithm for finding 2-factors in directed Hamiltonian graphs of out-degree at most two and in undirected Hamiltonian graphs of degree at most three. For the directed case, the algorithm finds a 2-factor in O(n²) expected time. The analysis of our algorithm is based on random walks on the line and interestingly resembles the analysis of a randomized algorithm for the 2-SAT problem given by Papadimitriou [On selecting a satisfying truth assignment, in: Proc. 32nd Annual IEEE Symp. on the Foundations of Computer Science (FOCS), 1991, p. 163]. For the undirected case, the algorithm finds a 2-factor in O(n³) expected time. We also analyze random versions of these graphs and show that cycles of length Ω(n/logn) can be found with high probability in polynomial time. This partially answers an open question of Broder et al. [Finding hidden Hamilton cycles, Random Structures Algorithms 5 (1994) 395] on finding hidden Hamiltonian cycles in sparse random graphs and improves on a result of Karger et al. [On approximating the longest path in a graph, Algorithmica 18 (1997) 82].     

A new method for expert target recognition system: Genetic wavelet extreme learning machine (GAWELM)

In last year’s, the expert target recognition has been become very important topic in radar literature. In this study, a target recognition system is introduced for expert target recognition (ATR) using radar target echo signals of High Range Resolution (HRR) radars. This study includes a combination of an adaptive feature extraction and classification using optimum wavelet entropy parameter values. The features used in this study are extracted from radar target echo signals. Herein, a genetic wavelet extreme learning machine classifier model (GAWELM) is developed for expert target recognition. The GAWELM composes of three stages. These stages of GAWELM are genetic algorithm, wavelet analysis and extreme learning machine (ELM) classifier. In previous studies of radar target recognition have shown that the learning speed of feedforward networks is in general much slower than required and it has been a major disadvantage. There are two important causes. These are: (1) the slow gradient-based learning algorithms are commonly used to train neural networks, and (2) all the parameters of the networks are fixed iteratively by using such learning algorithms. In this paper, a new learning algorithm named extreme learning machine (ELM) for single-hidden layer feedforward networks (SLFNs) Ahern et al., 1989, Al-Otum and Al-Sowayan, 2011, Avci et al., 2005a, Avci et al., 2005b, Biswal et al., 2009, Frigui et al., in press, Cao et al., 2010, Guo et al., 2011, Famili et al., 1997, Han and Huang, 2006, Huang et al., 2011, Huang et al., 2006, Huang and Siew, 2005, Huang et al., 2009, Jiang et al., 2011, Kubrusly and Levan, 2009, Le et al., 2011, Lhermitte et al., in press, Martínez-Martínez et al., 2011, Matlab, 2011, Nelson et al., 2002, Nejad and Zakeri, 2011, Tabib et al., 2009, Tang et al., 2011, which randomly choose hidden nodes and analytically determines the output weights of SLFNs, to eliminate the these disadvantages of feedforward networks for expert target recognition area. Then, the genetic algorithm (GA) stage is used for obtaining the feature extraction method and finding the optimum wavelet entropy parameter values. Herein, the optimal one of four variant feature extraction methods is obtained by using a genetic algorithm (GA). The four feature extraction methods proposed GAWELM model are discrete wavelet transform (DWT), discrete wavelet transform–short-time Fourier transform (DWT–STFT), discrete wavelet transform–Born–Jordan time–frequency transform (DWT–BJTFT), and discrete wavelet transform–Choi–Williams time–frequency transform (DWT–CWTFT). The discrete wavelet transform stage is performed for optimum feature extraction in the time–frequency domain. The discrete wavelet transform stage includes discrete wavelet transform and calculating of discrete wavelet entropies. The extreme learning machine (ELM) classifier is performed for evaluating the fitness function of the genetic algorithm and classification of radar targets. The performance of the developed GAWELM expert radar target recognition system is examined by using noisy real radar target echo signals. The applications results of the developed GAWELM expert radar target recognition system show that this GAWELM system is effective in rating real radar target echo signals. The correct classification rate of this GAWELM system is about 90% for radar target types used in this study.     

ROBUST SAMPLED-DATA CONTROL SYSTEMS ANALYSIS BY APPROXIMATE Z-TRANSFORM USING HIGHER-ORDER INTEGRATORS

In this paper, robust sampled-data control systems are analyzed in the frequency domain. Stability analysis of a sampled-data control system using higher-order integrators was proposed by Wang et al. in 1990 [1] and adopted in the approximate Z-transform by Wang et al. in 1997 [2]. We further apply the approximate Z-transform to analyze the stability boundary of a sampled-data control system in which the plant transfer function has bounds prescribed on its parameters.