软件迷惑变换的鲁棒性量化评价

为了方便程序员比较多种迷惑变换方案的优劣,提出了一种量化评价迷惑变换鲁棒性的方法.该方法从软件复杂度变化与代码功能模糊性两个相对独立的层面来刻画迷惑变换导致的鲁棒性.首先,从系统的复杂性与信息的多样性角度建立软件系统复杂度模型,模型包含软件结构、信息流、分支、循环以及元素的嵌套层次,力求从复杂性层面更准确地反映变换对软件的保护;之后,为量化描述迷惑变换的功能模糊度,根据专家指标评分法建立单种迷惑变换模糊度模型,在此基础上建立多种迷惑变换复合模糊度模型;然后,阐述了如何联合所提出的模型实现对单种迷惑变换技术有效性判定与多种迷惑方案的选优,也给出了模型的实现算法及一些示例;最后,通过实例仿真详细展示了模型的工作过程.     

Modified differential evolution approach for optimization of planar parallel manipulators force capabilities

The global optimization problem is not easy to solve and is still an open challenge for researchers since an analytical optimal solution is difficult to obtain even for relatively simple application problems. Conventional deterministic numerical algorithms tend to stop the search in local minimum nearest to the input starting point, mainly when the optimization problem presents nonlinear, non-convex and non-differential functions, multimodal and nonlinear. Nowadays, the use of evolutionary algorithms (EAs) to solve optimization problems is a common practice due to their competitive performance on complex search spaces. EAs are well known for their ability to deal with nonlinear and complex optimization problems. The primary advantage of EAs over other numerical methods is that they just require the objective function values, while properties such as differentiability and continuity are not necessary. In this context, the differential evolution (DE), a paradigm of the evolutionary computation, has been widely used for solving numerical global optimization problems in continuous search space. DE is a powerful population-based stochastic direct search method. DE simulates natural evolution combined with a mechanism to generate multiple search directions based on the distribution of solutions in the current population. Among DE advantages are its simple structure, ease of use, speed, and robustness, which allows its application on several continuous nonlinear optimization problems. However, the performance of DE greatly depends on its control parameters, such as crossover rate, mutation factor, and population size and it often suffers from being trapped in local optima. Conventionally, users have to determine the parameters for problem at hand empirically. Recently, several adaptive variants of DE have been proposed. In this paper, a modified differential evolution (MDE) approach using generation-varying control parameters (mutation factor and crossover rate) is proposed and evaluated. The proposed MDE presents an efficient strategy to improve the search performance in preventing of premature convergence to local minima. The efficiency and feasibility of the proposed MDE approach is demonstrated on a force optimization problem in Robotics, where the force capabilities of a planar 3-RRR parallel manipulator are evaluated considering actuation limits and different assembly modes. Furthermore, some comparison results of MDE approach with classical DE to the mentioned force optimization problem are presented and discussed.     

Manifold learning based speaker dependent dimension reduction for robust text independent speaker verification

Speaker verification has been studied widely from different points of view, including accuracy, robustness and being real-time. Recent studies have turned toward better feature stability and robustness. In this paper we study the effect of nonlinear manifold based dimensionality reduction for feature robustness. Manifold learning is a popular recent approach for nonlinear dimensionality reduction. Algorithms for this task are based on the idea that each data point may be described as a function of only a few parameters. Manifold learning algorithms attempt to uncover these parameters in order to find a low-dimensional representation of the data. From the manifold based dimension reduction approaches, we applied the widely used Isometric mapping (Isomap) algorithm. Since in the problem of speaker verification, the input utterance is compared with the model of the claiming client, a speaker dependent feature transformation would be beneficial for deciding on the identity of the speaker. Therefore, our first contribution is to use Isomap dimension reduction approach in the speaker dependent context and compare its performance with two other widely used approaches, namely principle component analysis and factor analysis. The other contribution of our work is to perform the nonlinear transformation in a speaker-dependent framework. We evaluated this approach in a GMM based speaker verification framework using Tfarsdat Telephone speech dataset for different noises and SNRs and the evaluations have shown reliability and robustness even in low SNRs. The results also show better performance for the proposed Isomap approach compared to the other approaches.     

电液伺服系统多项式非线性建模与控制一体化设计

常规电液伺服系统PID控制无法克服非线性因素影响,存在跟踪准确性和鲁棒性问题.因此,本文提出电液伺服系统多项式非线性H∞控制律设计方法,改进电液伺服系统的控制性能与鲁棒性.首先利用多项式非线性模型对电液伺服系统进行系统辨识,得到以误差作为状态变量的多项式非线性模型;然后设计多项式非线性控制律,证明所提出控制律可以保证系...     

Requirements for model development environments

This paper deals with the initial phase of our ongoing research project on the Definition of a Discrete Event Simulation MDE which started on 1 June 1983. The first phase of the rapid prototyping approach we are using in designing the MDE involves the requirements specification. A literature review revealed eleven current problems in modeling. To address these problems, a MDE was identified as composed of four layers: (1) hardware and operating system, (2) kernel MDE, (3) minimal MDE and (4) MDEs. Requirements were then perceived for each layer and are reported in this paper. The feasibility of the requirements have been assessed throughout our prototyping efforts. This paper has provided significant guidance to our research group in designing the MDE and its associated tools. We believe that the designers and implementers of other types of MDEs can benefit from the research described herein.     

On the robustness of optimal regulators for nonlinear discrete-time systems

In this paper the robustness of nonlinear discrete-time systems is analyzed. The nominal plant is supposed to be controlled by means of a feedback control law which is optimal with respect to some given criterion. The robustness of the closed-loop system is studied for two different classes of perturbations in the control law, which are called gain and additive nonlinear perturbations. The results are entirely based on the existence of a stationary solution of the dynamic programming equation (DPE), which provides directly a Lyapunov function associated to the closed-loop system. The convexity of that solution and the use of the Taylor formula appear to be the key to establish the robustness properties of the nominal plant. Two examples are solved in order to show an interesting fact: the existence of a compromise between the robustness of the system subjected to the two different classes of perturbations.     

Design,creation, and analysis of Czech corpora for structural metadata extraction from speech

Structural metadata extraction (MDE) research aims to develop techniques for automatic conversion of raw speech recognition output to forms that are more useful to humans and downstream automatic processes. The MDE annotation includes inserting boundaries of sentence-like units to the flow of speech, labeling non-content words like filled pauses and discourse markers for optional removal, and identifying sections of disfluent speech. This paper describes design, creation, and analysis of data resources for structural MDE from spoken Czech. The annotation is based on the LDC’s MDE annotation standard for English, with changes applied to accommodate specific phenomena of Czech. In addition to the necessary language-dependent modifications, we further proposed and applied several language-independent modifications slightly refining the original annotation scheme. We created two Czech MDE speech corpora—one in the domain of broadcast news and the other in the domain of broadcast conversations. Both corpora have already been published at LDC. The analysis section of this paper presents a variety of statistics about fillers, edit disfluencies, and sentence-like units. The two Czech corpora are not only compared with each other, but also with statistics relating to the available English MDE corpora. We also report the statistics indicating that edit disfluencies have a different part of speech (POS) distribution in comparison with the overall POS distribution. The findings from the corpus analysis should help guide strategies for developing automatic MDE systems.     

求解混合整数非线性规划问题的改进差分进化算法

针对混合整数非线性规划问题的特点,在差分进化算法的变异操作中加入取整运算,提出了一种适合于求解各种混合整数非线性规划问题的改进差分进化算法.同时,采用时变交叉概率因子的方法以提高算法的全局搜索能力和收敛速率.用四个典型测试函数进行了实验研究,实验结果表明,改进的差分进化算法用于求解混合整数非线性规划问题时收敛速度快,精度高,鲁棒性强.     

用于车辆重识别的视角感知局部注意力网络

车辆重识别的目的是从大型车辆数据库中找到与查询车辆相同特征的所有车辆图片。目前,由于同一车辆在不同视角下外观差异大或颜色、车型相同的不同车辆在特定视角下外观差异小,导致车辆重识别的准确度和鲁棒性均有待提高。提出一个视角感知局部注意力网络,采用弱监督注意力学习方式代替人工手动的车辆局部部件标注,自适应学习每个视角内所有显著性局部特征。通过局部注意力裁剪操作裁剪并放大该视角领域内部件细节信息,并基于局部注意力擦除操作擦除一些局部区域,以鼓励模型发掘该视角领域内其他更多的显著性局部线索。构建一种共同视角的注意力增强模块,以强化共同视角特征学习,并根据视角的相似度给每个视角分配相应的权重,使同一视角特征学习得到增强,不同视角特征学习受到抑制。实验结果表明,所提网络在VeRi-776数据集下的mAP为81.2%,在VehicleID数据集下的CMC@1、CMC@5分别为85.7%、98.0%,相较于PRN、PVEN、SAVER等重识别网络具有更高的识别精度和更强的泛化能力。     

一种基于最速控制的二次积分系统的非线性控制方法

考虑到系统的鲁棒性和动态抗干扰能力,在二次积分系统最速控制的基础上,提出了一种二次积分系统的非线性控制分析、设计方法。理论分析和仿真表明,这种非线性控制具有很好的控制效果和鲁棒性;同时,通过对控制器形式的修正,对其他一些线性或非线性二阶系统也有比较广泛的适用性。     

Recurrent neural network modeling combined with bilinear model structure

In this paper, we propose a neural network modeling scheme for nonlinear systems. The proposed architecture is a new combination of neural network and bilinear system model in which the terms of cross-products of input and output signals within the bilinear model are taken as the inputs into the neural network. Compared with the original bilinear system, this kind of network model possesses much more adjustable parameters to fulfill the system identification. Moreover, instead of the general back-propagation method an evolutionary computation called the differential evolution algorithm is presented to update the network parameters. This algorithm is with multiple direction searches toward the global optimal solution for given optimization problem. To show the feasibility of the proposed scheme, a nonlinear chemical process system of continuously stirred tank reactor is illustrated. Many simulations and examinations are considered to verify the robustness of the proposed neural network structure on the modeling performance, including different sets of initial conditions of the algorithm and model orders.     

An iterative approach to determine the complexity of local models for robust identification of nonlinear systems

In this paper, a new multi-model approach is proposed for identification of nonlinear systems. In similar identification methods, the operating space is partitioned and a local model is suggested for each partition. In such approaches, since the same linear structure is often used for all local models; huge number of local linear models is usually required to reasonably model an operating region with severely nonlinear dynamics. Therefore the size of the global model may exponentially increase; and as a result model robustness may decrease. In the proposed approach the best model structure is selected for the particular nonlinear study system in an iterative approach. At each iteration, a choice is made to increase number of local models and/or increase the local model complexity. Furthermore, it determines the complexity of local models based on increasing the model accuracy and ensuring the model robustness. In order to optimize the model approximation capability and model robustness, a model term selection approach based on a forward orthogonal least squares algorithm and a criterion that minimizes the sum of the variance of the parameter estimates is applied. Simulation results show that the proposed method results in an excellent validation performance with fewer parameters.     

一种基于修正差分进化的虹膜定位算法

提出一种用于虹膜定位的差分进化算法(modified differential evolution,MDE).MDE和原始差分进化算法(differential evolution,DE)主要有3点不同:第一,MDE采用了基于混沌序列的尺度因子和基于均匀分布的交叉率,这有助于提高候选解的多样性;第二,MDE使用中心解来修正最差解的变异操作,这有助于提高候选解的质量;第三,MDE使用最好解来帮助受困解摆脱局部最优点.在搜索边缘前,两种有效的去噪方法被用来减少虹膜图像中噪声的影响.去噪后,再使用MDE和其他4种方法来进行虹膜定位.在中科院(Chinese Academy of Sciences Institute of Automation,CASIA)眼图数据库中选择200幅来自不同个体的虹膜图像来验证和比较MDE及其他4种方法的效率.实验结果表明,与其他4种方法相比,MDE使用更少的执行时间来定位瞳孔边缘和虹膜边缘.     

Optimal short-term hydro-thermal scheduling using quasi-oppositional teaching learning based optimization

This paper presents a new approach for solving short-term hydrothermal scheduling (HTS) using an integrated algorithm based on teaching learning based optimization (TLBO) and oppositional based learning (OBL). The practical hydrothermal system is highly complex and possesses nonlinear relationship of the problem variables, cascading nature of hydro reservoirs, water transport delay and scheduling time linkage that make the problem of optimization difficult using standard optimization methods. To overcome these problems, the proposed quasi-oppositional teaching learning based optimization (QOTLBO) is employed. To show its efficiency and robustness, the proposed QOTLBO algorithm is applied on two test systems. Numerical results of QOTLBO are compared with those obtained by two phase neural network, augmented Lagrange method, particle swarm optimization (PSO), improved self-adaptive PSO (ISAPSO), improved PSO (IPSO), differential evolution (DE), modified DE (MDE), fuzzy based evolutionary programming (Fuzzy EP), clonal selection algorithm (CSA) and TLBO approaches. The simulation results reveal that the proposed algorithm appears to be the best in terms of convergence speed, solution time and minimum cost when compared with other established methods. This method is considered to be a promising alternative approach for solving the short-term HTS problems in practical power system.     

非线性量化小脑模型关节控制器神经网络控制器

常规小脑模型关节控制器(CMAC)神经网络采用线性均匀量化,稳态控制精度与量化级数相关,增加量化级数可提高稳态精度但会导致内存空间和计算量的增加.本文提出一种可采用幂函数、高斯、分段3种非线性量化方法的非线性量CMAC神经网络,并分析了非线性量化CMAC的收敛性,解释了非线性量化提高稳态精度的本质.面向一阶惯性环节、二阶系统、一阶时变系统及二阶时变系统,分别跟踪方波、斜坡、正弦波、三角波和加速度等输入信号,仿真验证了非线性量化CMAC神经网络控制器的有效性,给出了不同非线性量化方法的适用性.结果表明,非线性量化CMAC参数容易设定,物理意义清晰,与常规CMAC对比,其快速性和控制精度显著提高,可以有效解决实际复杂非线性时变系统的控制.     

文本无关的说话人识别系统抗噪方法研究

研究了基于美尔倒谱特征参数及高斯混合模型的文本无关的说话人识别系统,为了提高噪声环境下识别系统的识别率,从两个角度研究改善该系统抗噪性能的方法,即利用语音识别将文本无关的系统转化为文本有关的说话人识别方法和通过选择鲁棒性较强的帧进行说话人识别的方法,分析了以上方法对系统识别性能的改善作用,并通过实验验证上述方法确实可以提高系统在噪声环境下的识别率。     

高阶大惯性系统的线性自抗扰控制器设计

针对自抗扰控制器在热力系统高阶大惯性过程控制中效果不佳的问题,提出一种利用高阶系统模型信息进行补偿的自抗扰控制器设计方法.基于理论分析,给出各可调参数的物理意义及其定量化参数整定方法,并从观测误差、开环频率特性和参数稳定域等方面分析补偿自抗扰控制器能够提高控制效果的原因.仿真对比实验和鲁棒性检验结果表明,所提方法在设定值跟踪、抗扰能力和性能鲁棒性方面均优于PI/PID,同时能够显著改善低阶自抗扰控制器对高阶大惯性过程的控制效果,具有很好的工程推广潜力.     

优化非线性激活函数-全局卷积神经网络的物体识别算法

传统的物体识别算法识别精度、自适应能力弱等问题已然不能满足实际的仓储物流领域对物体识别精度的要求.近年来,相关学者提出了基于深度学习的物体识别算法,它得到一定的推广和应用.但是,深度学习在物体识别的应用过程中存在以下问题:一是深度学习模型中激活函数的非线性建模能力弱;二是深度学习模型大量重复的池化操作丢失信息.鉴于此,本文提出了一种参数形式统一且可学习的指数非线性单元(Multiple Parameters Exponential Linear Units,MPELU).它通过在ELU(Exponential Linear Units)中引入两个学习的参数,提升模型的非线性建模能力.同时,本文提出了一种新的全局卷积神经网络结构,减少大量池化操作丢失特征信息的问题.基于上述思想,本文提出了优化非线性激活函数-全局卷积神经网络的物体识别算法.利用本文所提算法对CIFAR100数据集和ImageNet数据集分别进行实验.结果表明,本文所提物体识别方法不仅识别准确率较传统机器学习、其他深度学习模型有较大幅度提升,而且具有良好的稳定性和鲁棒性.     

不确定离散时间系统的复合非线性反馈积分滑模控制

针对不确定离散时间系统,提出一种基于离散复合非线性反馈的积分滑模(DCNF-ISM)控制策略,并将该算法应用于扰动下的磁盘跟踪问题.该算法由离散复合非线性反馈(DCNF)控制律与积分滑模(ISM)控制律两部分组成,其中DCNF控制律用于保证系统具有较好瞬态性能,基于改进的离散趋近律设计的ISM控制律用于保证系统鲁棒性.基于Lyapunov稳定性理论对本文提出的控制策略的稳定性进行了推导证明,证明了离散时间系统的一致最终有界性.仿真结果表明,本文提出的控制策略能保证系统在扰动下仍然能够精确跟踪给定的参考信号,与传统的DCNF控制相比,该算法能够保证系统具有响应速度快超调量小、鲁棒性强等优点.     

Where does model-driven engineering help? Experiences from three industrial cases

There have been few experience reports from industry on how Model-Driven Engineering (MDE) is applied and what the benefits are. This paper summarizes the experiences of three large industrial participants in a European research project with the objective of developing techniques and tools for applying MDE on the development of large and complex software systems. The participants had varying degrees of previous experience with MDE. They found MDE to be particularly useful for providing abstractions of complex systems at multiple levels or from different viewpoints, for the development of domain-specific models that facilitate communication with non-technical experts, for the purposes of simulation and testing, and for the consumption of models for analysis, such as performance-related decision support and system design improvements. From the industrial perspective, a methodology is considered to be useful and cost-efficient if it is possible to reuse solutions in multiple projects or products. However, developing reusable solutions required extra effort and sometimes had a negative impact on the performance of tools. While the companies identified several benefits of MDE, merging different tools with one another in a seamless development environment required several transformations, which increased the required implementation effort and complexity. Additionally, user-friendliness of tools and the provision of features for managing models of complex systems were identified as crucial for a wider industrial adoption of MDE.