Design of forging process variables under uncertainties

Forging is a complex nonlinear process that is vulnerable to various manufacturing anomalies, such as variations in billet geometry, billet/die temperatures, material properties, and workpiece and forging equipment positional errors. A combination of these uncertainties could induce heavy manufacturing losses through premature die failure, final part geometric distortion, and reduced productivity. Identifying, quantifying, and controlling the uncertainties will reduce variability risk in a manufacturing environment, which will minimize the overall production cost. In this article, various uncertainties that affect the forging process are identified, and their cumulative effect on the forging tool life is evaluated. Because the forging process simulation is time-consuming, a response surface model is used to reduce computation time by establishing a relationship between the process performance and the critical process variables. A robust design methodology is developed by incorporating reliability-based optimization techniques to obtain sound forging components. A case study of an automotive-component forging-process design is presented to demonstrate the applicability of the method.     

Connectionist simulation of quantification skills

The study of numerical abilities, and how they are acquired, is being used to explore the continuity between ontogenesis and environmental learning. One technique that proves useful in this exploration is the artificial simulation of numerical abilities with neural networks, using different learning paradigms to explore development. A neural network simulation of subitization, sometimes referred to as visual enumeration, and of counting, a recurrent operation, has been developed using the so-called multi-net architecture. Our numerical ability simulations use two or more neural networks combining supervised and unsupervised learning techniques to model subitization and counting. Subitization has been simulated using networks employing unsupervised self-organizing learning, the results of which agree with infant subitization experiments and are comparable with supervised neural network simulations of subitization reported in the literature. Counting has been simulated using a multi-net system of supervised static and recurrent backpropagation networks that learn their individual tasks within an unsupervised, competitive framework. The developmental profile of the counting simulation shows similarities to that of children learning to count and demonstrates how neural networks can learn how to be combined together in a process modelling development.     

Error quantification of the normalised right graph symbol for an errors-in-variables system

This paper proposes a novel method to quantify the error of a nominal normalized right graph symbol (NRGS) for an errors-in-variables (EIV) system corrupted with bounded noise. Following an identification framework for estimation of a perturbation model set, a worst-case v-gap error bound for the estimated nominal NRGS can be first determined from \textit{a priori} and \textit{a posteriori} information on the underlying EIV system. Then, an NRGS perturbation model set can be derived from a close relation between the v-gap metric of two models and ${\rm H}_\infty$-norm of their NRGSs' difference. The obtained NRGS perturbation model set paves the way for robust controller design using an ${\rm H}_\infty$ loop-shaping method because it is a standard form of the well-known NCF (normalized coprime factor) perturbation model set. Finally, a numerical simulation is used to demonstrate the effectiveness of the proposed identification method.     

Suboptimal robust synthesis for MIMO plant under coprime factor perturbations

Suboptimal robust synthesis for MIMO nominal system under coprime factor perturbations is considered in classical and non-classical statements. In the classical statement, weights of perturbations and upper bound on magnitude bounded exogenous disturbance are assumed to be known to controller designer. Suboptimal synthesis within ε tolerance is reduced to the solution of log₂(1/ε) standard mixed sensitivity problems of ℓ₁ optimization. In the non-classical statement, the upper bounds on perturbations and exogenous disturbance are to be estimated from measurement data and suboptimal synthesis is reduced to the solution of 1/ε mixed sensitivity problems.     

Characterization of surface deformation with the Edge of LightTM technique

The aircraft lap joints are inspected with an enhanced visual inspection technique named “Edge of Light”, which is patented by the NRC Institute for Aerospace Research. This technique is applicable for rapid detection of possible hidden corrosion in lap joints. The surface deformation due to hidden corrosion can be characterized by this optical-based inspection method. In this study, a calibration procedure is developed to quantify the lap joint surface deformation. The effect of surface reflectivity is investigated with the solid film highlighting technique (SolidHi^TM), which helps achieve a uniform reflectivity during the inspection. The efficiency of the technique is demonstrated with the experimental results. This work is supported by NRC-IAR New Initiative Research Funding.     

基于免疫网络的信息安全风险评估模型

风险评估是评价网络信息系统安全的有效措施之一。该文基于免疫网络可动态实时诊断的特性,提出一种新的信息安全风险评估模型,给出模型中各项指标的定量计算方法,以评估整个信息系统的风险值。该模型能够综合考虑评估要素的相互关联,针对风险动态更新,进行实时监控。实验验证了其评估信息系统安全状态的有效性。     

基于国家信息安全风险评估指南的量化模型研究

以2005年颁布的国标草案《信息安全风险评估指南》为主要指导,并参考NISTSP800—30的管理控制思想、评估方法以及威胁发生可能性的计算方法,给出了一个具有可操作性的信息安全风险评估量化模型。     

WSN中改进的分布式多维定标定位算法

针对无线传感器网络经典集中式多维定标MDS-MAP算法在定位精度与矩阵计算复杂度方面的不足,提出一种基于分布式的ADMDS-MAP定位算法.该算法通过采用测定距离量化,得到节点的近邻向量(CNV),然后通过相关的估算算法得到距离矩阵D和节点的初始估值,加入适当的求精计算得到节点的精确位置.理论分析和仿真实验表明,改进算法能够提高节点的定位精度.     

基于图像块均值量化的自适应盲水印算法

提出了一种新的基于图像分块DWT的自适应盲水印算法。算法以一幅有意义的二值水印图像为水印信号。同时,利用了Hilbert扫描能很好地保持图像空间局部连接性的特点。在图像块的Hilbert序列中选取相邻的3个分块分别进行一层小波变换,计算各分块对应子带的均值并比较大小,根据较大和较小的两个均值决定的步长,调整另外一个均值对应子带系数的大小,从而完成自适应的嵌入水印信号。而且,水印的提取无需原始图像参与,达到真正的盲水印。实验结果表明,该算法是有效的,水印具有较好的不可见性,对通常的图像处理操作和裁剪、挤压、像素移位等几何攻击有较强的鲁棒性.