Forecasting of Software Reliability Using Neighborhood Fuzzy Particle Swarm Optimization Based Novel Neural Network

This paper proposes an artificial neural network (ANN) based software reliability model trained by novel particle swarm optimization (PSO) algorithm for enhanced forecasting of the reliability of software. The proposed ANN is developed considering the fault generation phenomenon during software testing with the fault complexity of different levels. We demonstrate the proposed model considering three types of faults residing in the software. We propose a neighborhood based fuzzy PSO algorithm for competent learning of the proposed ANN using software failure data. Fitting and prediction performances of the neighborhood fuzzy PSO based proposed neural network model are compared with the standard PSO based proposed neural network model and existing ANN based software reliability models in the literature through three real software failure data sets. We also compare the performance of the proposed PSO algorithm with the standard PSO algorithm through learning of the proposed ANN. Statistical analysis shows that the neighborhood fuzzy PSO based proposed neural network model has comparatively better fitting and predictive ability than the standard PSO based proposed neural network model and other ANN based software reliability models. Faster release of software is achievable by applying the proposed PSO based neural network model during the testing period.      

A dsPIC based novel digital sinusoidal pulse-width modulation technique for voltage source inverter applications

This paper presents a novel digital sinusoidal pulse-width modulation (SPWM) technique based on immediate calculation of duty cycle count (DCC) values of pulses with high speed Harvard architecture based RISC controllers. The DCC values are calculated with the multiplication of an instant modulation index count (MIC) and a preloaded sine count value (SCV) table in the program memory of the controller. The proposed technique overcomes the drawbacks of overhead time in digital carrier-reference based technique and large memory requirement in look-up table technique. The proposed technique is effective where frequent change of modulation index (MI) of SPWM is essential like grid-connected inverter applications. The effectiveness of the proposed technique is verified with both Proteus VSM based simulation and dsPIC based experiment.     

Robust feedforward and recurrent neural network based dynamic weighted combination models for software reliability prediction

Traditional parametric software reliability growth models (SRGMs) are based on some assumptions or distributions and none such single model can produce accurate prediction results in all circumstances. Non-parametric models like the artificial neural network (ANN) based models can predict software reliability based on only fault history data without any assumptions. In this paper, initially we propose a robust feedforward neural network (FFNN) based dynamic weighted combination model (PFFNNDWCM) for software reliability prediction. Four well-known traditional SRGMs are combined based on the dynamically evaluated weights determined by the learning algorithm of the proposed FFNN. Based on this proposed FFNN architecture, we also propose a robust recurrent neural network (RNN) based dynamic weighted combination model (PRNNDWCM) to predict the software reliability more justifiably. A real-coded genetic algorithm (GA) is proposed to train the ANNs. Predictability of the proposed models are compared with the existing ANN based software reliability models through three real software failure data sets. We also compare the performances of the proposed models with the models that can be developed by combining three or two of the four SRGMs. Comparative studies demonstrate that the PFFNNDWCM and PRNNDWCM present fairly accurate fitting and predictive capability than the other existing ANN based models. Numerical and graphical explanations show that PRNNDWCM is promising for software reliability prediction since its fitting and prediction error is much less relative to the PFFNNDWCM.     

基于无证书公钥的安全V2I通信协议

随着各种交通问题的增加,车载自组织网络的各种应用不断被提出,其安全性也成为研究的焦点。基于无证书公钥系统,提出了高效的安全V2I通信协议,其既避免了基于传统公钥系统的协议需做证书验证和维护的缺陷,又避免了基于身份的公钥密码系统的协议中密钥分发的缺陷。与具有相同安全级别的一个基于证书的安全V2I通信协议相比,本协议的效率提高了一倍,它更适合于车载自组织网络。     

A hybrid estimation of distribution algorithm for solving the resource-constrained project scheduling problem

In this paper, a hybrid estimation of distribution algorithm (HEDA) is proposed to solve the resource-constrained project scheduling problem (RCPSP). In the HEDA, the individuals are encoded based on the extended active list (EAL) and decoded by serial schedule generation scheme (SGS), and a novel probability model updating mechanism is proposed for well sampling the promising searching region. To further improve the searching quality, a Forward-Backward iteration (FBI) and a permutation based local search method (PBLS) are incorporated into the EDA based search to enhance the exploitation ability. Simulation results based on benchmarks and comparisons with some existing algorithms demonstrate the effectiveness of the proposed HEDA.     

考虑硅衬底效应的基于TSV的3D IC电源分配网络建模

基于硅通孔TSV的3D-IC在电源分配网络PDN中引入了新的结构--TSV，另外，3D堆叠使得硅衬底效应成为不可忽略的因素，因此为3D-IC建立PDN模型必须要考虑TSV以及硅衬底效应。为基于TSV的3D-IC建立了一个考虑硅衬底效应的3D PDN模型，该模型由P/G TSV对模型和片上PDN模型组成。P/G TSV对模型是在已有模型基础上，引入bump和接触孔的RLGC集总模型而建立的，该模型可以更好地体现P/G TSV对的电学特性；片上PDN模型则是基于Pak J S提出的模型，通过共形映射法将硅衬底效应引入单元模块模型而建立的，该模型可以有效地反映硅衬底对PDN电学特性的影响。经实验表明，建立的3D PDN模型可以有效、快速地估算3D-IC PDN阻抗。     

A self-organization mining based hybrid evolution learning for?TSK-type fuzzy model design

In this paper, a self-organization mining based hybrid evolution (SOME) learning algorithm for designing a TSK-type fuzzy model (TFM) is proposed. In the proposed SOME, group-based symbiotic evolution (GSE) is adopted in which each group in the GSE represents a collection of only one fuzzy rule. The proposed SOME consists of structure learning and parameter learning. In structure learning, the proposed SOME uses a two-step self-organization algorithm to decide the suitable number of rules in a TFM. In parameter learning, the proposed SOME uses the data mining based selection strategy and data mining based crossover strategy to decide groups and parental groups by the data mining algorithm that called frequent pattern growth. Illustrative examples were conducted to verify the performance and applicability of the proposed SOME method.     

一种新的核线性鉴别分析算法及其在人脸识别上的应用

基于核策略的核Fisher鉴别分析（KFD）算法已成为非线性特征抽取的最有效方法之一。但是先前的基于核Fisher鉴别分析算法的特征抽取过程都是基于2值分类问题而言的。如何从重叠（离群）样本中抽取有效的分类特征没有得到有效的解决。本文在结合模糊集理论的基础上,利用模糊隶属度函数的概念,在特征提取过程中融入了样本的分布信息,提出了一种新的核Fisher鉴别分析方法——模糊核鉴别分析算法。在ORL人脸数据库上的实验结果验证了该算法的有效性。     

An efficient VLSI architecture for lifting based 1D/2D discrete wavelet transform

In this paper, high performance VLSI architectures for lifting based 1D and 2D-Discrete wavelet transforms (DWTs) are proposed. The proposed logic used for area efficient lifting based DWT is to perform the whole operation with one processing element. Similarly, the proposed logic used for delay efficient lifting based DWT is to perform the whole operation with multiple processing elements in parallel. In both the cases, the processing element consists of one floating point adder and one proposed fused multiply add design. The proposed and existing lifting based 1D and 2D lifting based DWTs are implemented with 45 nm technology. The results show that the proposed designs achieve significant improvement compared with existing architectures. For example, 9-point 2-parallel proposed (9, 7) single level 1D-DWT achieves 33.5% of reduction in total cycle delay compared with direct form. Similarly, 9-point single PE proposed (9, 7) single level 1D-DWT achieves 59.8% and 75.5% of reduction in total area and net power over direct form respectively.     

Parallel randomized sampling for support vector machine (SVM) and support vector regression (SVR)

A parallel randomized support vector machine (PRSVM) and a parallel randomized support vector regression (PRSVR) algorithm based on a randomized sampling technique are proposed in this paper. The proposed PRSVM and PRSVR have four major advantages over previous methods. (1) We prove that the proposed algorithms achieve an average convergence rate that is so far the fastest bounded convergence rate, among all SVM decomposition training algorithms to the best of our knowledge. The fast average convergence bound is achieved by a unique priority based sampling mechanism. (2) Unlike previous work (Provably fast training algorithm for support vector machines, 2001) the proposed algorithms work for general linear-nonseparable SVM and general non-linear SVR problems. This improvement is achieved by modeling new LP-type problems based on Karush–Kuhn–Tucker optimality conditions. (3) The proposed algorithms are the first parallel version of randomized sampling algorithms for SVM and SVR. Both the analytical convergence bound and the numerical results in a real application show that the proposed algorithm has good scalability. (4) We present demonstrations of the algorithms based on both synthetic data and data obtained from a real word application. Performance comparisons with SVM^light show that the proposed algorithms may be efficiently implemented.     

基于曼哈顿距离加权协同表示分类的车辆识别

加权稀疏表示分类（WSRC）在声频传感器网络下的车辆识别中取得了不错的效果。但是稀疏表示分类（SRC）中实际上起较大作用的是字典中所有类的协同表示,因此协同表示分类（CRC）被提出用来提升算法效率,CRC框架还改进了残差计算方式来提高识别精度。在WSRC中发现保局性对提升识别率起到很好的作用,因此在CRC中引入加权编码,提出了声频传感器网络下基于加权协同表示分类（WCRC）的车辆识别方法,取得了明显的速度（相比WSRC、SRC）以及不错的精度（对比WSRC、CRC、SRC）提升。同时针对欧氏距离对样本相似性判断的不足,将曼哈顿距离引入加权编码,进一步地提出了基于曼哈顿距离加权协同表示分类（Manhattan-WCRC）的车辆识别方法,取得了最高的识别率,而运算速度与WCRC接近。     

基于扫描匹配的室外环境SLAM 方法

针对室外环境中的机器人“绑架”问题,提出了基于地图匹配的SLAM方法．该方法舍弃了机器人里程计信息, 只利用局部地图和全局地图的图形相关性进行机器人定位．方法的核心是多重估计数据关联,并将奇异值分解应用到机器人位姿计算中．利用Victoria Park数据集将本算法与基于扩展卡尔曼滤波器的方法进行比较,实验结果证明了本文提出的算法的有效性．     

MCEDA: A novel many-objective optimization approach based on model and clustering

To solve many-objective optimization problems (MaOPs) by evolutionary algorithms (EAs), the maintenance of convergence and diversity is essential and difficult. Improved multi-objective optimization evolutionary algorithms (MOEAs), usually based on the genetic algorithm (GA), have been applied to MaOPs, which use the crossover and mutation operators of GAs to generate new solutions. In this paper, a new approach, based on decomposition and the MOEA/D framework, is proposed: model and clustering based estimation of distribution algorithm (MCEDA). MOEA/D means the multi-objective evolutionary algorithm based on decomposition. The proposed MCEDA is a new estimation of distribution algorithm (EDA) framework, which is intended to extend the application of estimation of distribution algorithm to MaOPs. MCEDA was implemented by two similar algorithm, MCEDA/B (based on bits model) and MCEDA/RM (based on regular model) to deal with MaOPs. In MCEDA, the problem is decomposed into several subproblems. For each subproblem, clustering algorithm is applied to divide the population into several subgroups. On each subgroup, an estimation model is created to generate the new population. In this work, two kinds of models are adopted, the new proposed bits model and the regular model used in RM-MEDA (a regularity model based multi-objective estimation of distribution algorithm). The non-dominated selection operator is applied to improve convergence. The proposed algorithms have been tested on the benchmark test suite for evolutionary algorithms (DTLZ). The comparison with several state-of-the-art algorithms indicates that the proposed MCEDA is a competitive and promising approach.     

基于DCT的DMT系统设计与峰均功率比研究

提出一种新的基于离散余弦变换(Discrete cosin transform,DCT)及其逆变换的离散多音调制系统实现方案,利用IDCT/DCT变换替代IFFT/FFT变换实现多载波信号的调制与解调,并对该系统的峰均功率比(Peakto average power ratio,PAPR)进行了理论分析。在此基础上,提出一种利用离散余弦变换能量集中特性降低系统PAPR的方法,与基于选择性映射算法的离散多音调制系统(SLM-FFT-DMT)相比,基于DCT的DMT系统比SLM-FFT-DMT系统的PAPR低1.5dB,且具有计算法复杂度低的优点。     

Indifference threshold-based attribute ratio analysis: A method for assigning the weights to the attributes in multiple attribute decision making

In this paper, a new method called Indifference Threshold-based Attribute Ratio Analysis (ITARA for short) is proposed to assign the weights to the attributes in Multiple Attribute Decision Making (MADM) problems. The proposed method is based on the concept of “Indifference Threshold (IT)” and belongs to a group of techniques which are based on measuring data dispersion. The proposed technique is applied to solve a numerical example. Additionally, a simulation experiment is designed to investigate the performance of the proposed method and other relevant methods. The results indicate that the proposed method is substantially superior to other methods in almost all the cases studied.     

Step impedance resonator‐based tunable perfect metamaterial absorber with polarization insensitivity for solar cell applications

In this work, a step impedance resonator (SIR)‐based structure is proposed to develop a compact tunable metamaterial (MTM)‐based perfect absorber for solar cell applications. This MTM absorber is able to improve the absorption over a wide range of visible frequency range from 550 to 650 THz. The absorption is high around the frequency 600 THz. The proposed model is designed based on SIR technique to achieve miniaturization. The parametric study of overall size of the proposed MTM absorber analyzed over the frequency range 430‐750 THz. The thickness of dielectric spacer, and top most layer (MTM Structure) illustrates the tunable characteristics of the proposed model. A complete comparative analysis of proposed model with different dielectric spacers like AlGaAs, InAs, GaAs, and AlAs are presented with the help of absorption (S₁₁) and transmission (S₁₂). The proposed model is suitable for high efficiency solar cell energy harvesting applications.     

TSP 基于参考点的相邻插入法和两阶段方法

通过分析已有的最近插入法，提出了一种基于参考点的相邻插入法(RPBNI)及其改进策略(I-RPBNI)，用于求解旅行商问题(TSP)，时间性能分别为O(n^2)NO(n^3)；进而提出了结合模拟退火算法和I-RPBNI的两阶段方法．通过典型算例的数值仿真，验证了所提出算法的有效性、高效性和鲁棒性．     

An effective estimation of distribution algorithm for the multi-mode resource-constrained project scheduling problem

In this paper, an estimation of distribution algorithm (EDA) is proposed to solve the multi-mode resource-constrained project scheduling problem (MRCPSP). In the EDA, the individuals are encoded based on the activity-mode list (AML) and decoded by the multi-mode serial schedule generation scheme (MSSGS), and a novel probability model and an updating mechanism are proposed for well sampling the promising searching region. To further improve the searching quality, a multi-mode forward backward iteration (MFBI) and a multi-mode permutation based local search method (MPBLS) are proposed and incorporated into the EDA based search framework to enhance the exploitation ability. Based on the design-of-experiment (DOE) test, suitable parameter combinations are determined and some guidelines are provided to set the parameters. Simulation results based on a set of benchmarks and comparisons with some existing algorithms demonstrate the effectiveness of the proposed EDA.     

基于时间顺序标码的可计算的信任评价模型

信任本身是一个社会认知的概念,因此从认知的角度来研究信任是合适的。通过引入时间认知,建立一种基于时间顺序标码(TSM)的信任评价的概念模型。在分析了经验信息的三种遗忘效应（即距离效应、边界效应以及层级效应）后,将其应用到个体信任值的计算中。在概念模型的基础上,采用D-S信度理论来建立信任的可计算模型。对比了理论与实验结果,表明该模型是有效的。     

基于加权函数选择的伺服系统性能优化设计方法

为了尽可能提高伺服系统的性能, 提出一种基于加权函数选择的性能优化设计方法. 首先, 给出性能优化设计问题的描述; 其次, 给出了线性矩阵不等式(Linear matrix inequality, LMI)形式的优化指标; 再次, 提出一种基于加权函数选择的系统性能优化设计迭代算法; 最后, 利用Bode积分关系分析了算法的收敛性. 所提出的系统性能优化设计算法易于迭代计算, 仿真实例验证了算法的有效性.