A novel adaptive sampling based methodology for feasible region identification of compute intensive models using artificial neural network

Identification of feasible region of operations in multivariate processes is a problem of interest in several fields. This is particularly challenging when the process model is black-box in nature and/or is computationally expensive, as analytical solutions are not available and the number of possible model evaluations is limited. An efficient methodology is required to identify samples where the model is evaluated for developing a computationally efficient surrogate model. In this work, an artificial neural network based surrogate model is proposed which is integrated with a statistical-based approach (Jack-knifing) to estimate the variance of the surrogate model prediction. This allows implementation of an adaptive sampling approach where new samples are identified close to the feasible region boundary or in regions of high prediction uncertainty. The proposed approach performs better than a previously published kriging based method for different dimensionality case studies.     

Mixture ratio modeling of dynamic systems

Any knowledge extraction relies (possibly implicitly) on a hypothesis about the modelled-data dependence. The extracted knowledge ultimately serves to a decision-making (DM). DM always faces uncertainty and this makes probabilistic modelling adequate. The inspected black-box modeling deals with “universal” approximators of the relevant probabilistic model. Finite mixtures with components in the exponential family are often exploited. Their attractiveness stems from their flexibility, the cluster interpretability of components and the existence of algorithms for processing high-dimensional data streams. They are even used in dynamic cases with mutually dependent data records while regression and auto-regression mixture components serve to the dependence modeling. These dynamic models, however, mostly assume data-independent component weights, that is, memoryless transitions between dynamic mixture components. Such mixtures are not universal approximators of dynamic probabilistic models. Formally, this follows from the fact that the set of finite probabilistic mixtures is not closed with respect to the conditioning, which is the key estimation and predictive operation. The paper overcomes this drawback by using ratios of finite mixtures as universally approximating dynamic parametric models. The paper motivates them, elaborates their approximate Bayesian recursive estimation and reveals their application potential.     

Multilevel surrogate modeling of an amine scrubbing process for CO2capture

Surrogate models provide a powerful method for simplifying calculations within complex simulations. While surrogate models are broadly applied within chemical engineering, little research exists investigating the level of surrogacy's impact on a simplified process model. In this work, artificial neural networks (ANN) and Kriging models are used as surrogate models at the process, process unit, and thermodynamic levels for a CO₂ amine scrubbing process. The surrogated models are evaluated against an Aspen Plus simulation for accuracy, convergence behavior, computational cost, and ability to extrapolate. The thermodynamic and process unit models can better handle discontinuous, non-smooth behavior, and convergence issues in the surrogated truth model, but poor conditioning in the final system of equations results in a lower accuracy and convergence rate than the process level surrogate. Beyond model accuracy, availability of diverse data, intended re-usability, and the desired outputs must be considered when selecting a level of abstraction.     

对抗样本生成技术综述

如今,深度学习已被广泛应用于图像分类和图像识别的问题中,取得了令人满意的实际效果,成为许多人工智能应用的关键所在.在对于模型准确率的不断探究中,研究人员在近期提出了“对抗样本”这一概念.通过在原有样本中添加微小扰动的方法,成功地大幅度降低原有分类深度模型的准确率,实现了对于深度学习的对抗目的,同时也给深度学习的攻方提供了新的思路,对如何开展防御提出了新的要求.在介绍对抗样本生成技术的起源和原理的基础上,对近年来有关对抗样本的研究和文献进行了总结,按照各自的算法原理将经典的生成算法分成两大类——全像素添加扰动和部分像素添加扰动.之后,以目标定向和目标非定向、黑盒测试和白盒测试、肉眼可见和肉眼不可见的二级分类标准进行二次分类.同时,使用MNIST数据集对各类代表性的方法进行了实验验证,以探究各种方法的优缺点.最后总结了生成对抗样本所面临的挑战及其可以发展的方向,并就该技术的发展前景进行了探讨.     

并网变流器全工况阻抗模型的黑箱辨识方法

阻抗分析法因适用于并网变流器控制结构和参数均未知的黑箱系统，已成为工程中评估宽频振荡风险的重要选择。以跟网型三相两电平变流器为例，首先，使用谐波线性化方法，严格推导了计及直流侧耦合的交流侧全工况开/闭环理论阻抗模型，实现了模型参数与工作点高阶项的完全解耦。其次，依据理论模型特征，明确了黑箱辨识方法的基本原理，分析了辨识单一开/闭环阻抗模型频率响应所需的工况数量。使用辨识得到的全工况阻抗模型，实现了不同短路比条件下系统安全运行域的快速评估，并通过时域仿真验证了其准确性。此外，讨论了所提方法的合理性、普适性以及相对于深度学习方法的优越性，为阻抗分析法的实际应用提供了参考。     

Black-box models for fault detection and performance monitoring of buildings

Black-box models such as linear regression have proven to be helpful in ongoing building commissioning in many ways. The aim of this work is to improve linear models with change point for fault detection in buildings. Building simulations revealed poor performance of them (R 2 2. Remark: In the Inverse Modelling Toolkit (IMT) (Kissock et al. 2003) similar models are called CP-MVR or rather 3P-MVR or 4P-MVR. The difference to the CPMLRs used here is that there are different intercept terms for all linear regressions for the CPMLRs instead of one equal intercept term for all linear regression models 3P-MVR. This is introducing discontinuities on continuous independent variables but for the mean daily perspective taken here this is not disadvantageous.  < 0.7) for some low energy buildings. The regression models (RMs) can be considerably improved by introducing the rate of change of the indoor air temperature (ΔT _ind) as an independent variable. Thus, R ² values were raised by up to 0.5 (e.g. from 0.2 to 0.7, example with the lowest R ²). A new training and application process for the RMs revealed further improvements by using a hierarchical agglomerative clustering algorithm to determine different day-types as additional (categorical) variables in the RM. The application of these improved RMs for outlier detection is demonstrated in three buildings.     

小议软件黑盒测试技术

软件开发中出现错误或缺陷的机会越来越多,市场对软件质量重要性的认识逐渐增强。所以,软件测试在软件项目实施过程中的重要性日益突出。文章从基本概念入手具体介绍了软件黑盒测试编写测试用例、测试方法和测试工具几方面内容,让大家对黑盒测试有一个系统了解。     

基于改进加权协同过滤的集群用户黑箱个性意象预测

为了解决传统意象定位中感性意象部分信息丢失及用户模糊的个性化需求不完全表达的问题,提出基于改进加权SO（WSO）算法的集群用户个性意象预测研究. 建立用户特征域,基于K-modes算法计算用户差异度,确立用户集群. 对甄选样本实施兴趣度排序及自主性意象评价,创建集群意象因子集. 引入用户相似度优化WSO算法,增强集群用户间的内在联系,精准预测目标用户黑箱的个性意象分值. 基于语义差异问卷及平均绝对误差分析验证黑箱意象,输出集群中单一用户的个性化意象,实现意象预测. 以无人机为例,预测用户的个性意象,误差小于0.5被舍去,表明该方法能够较好地实现用户模糊的意象黑箱透明化,且预测的意象符合用户的个性化需求,可以有效辅助设计师有针对性地设计.     

基于P89C669的汽车驾驶状态纪录仪系统设计

介绍了一种基于Philips公司的最新的增强型51内核单片机P89C669作为核心控制器的汽车状态记录仪系统的设计和实现方法。对微控制器P89C669作出了简要的性能分析,又参照了针对汽车行驶记录仪的国家标准GB/T19056-2003,从系统总体设计、各子模块的设计等各个角度对汽车状态记录仪系统的设计方法作了较为全面的介绍。这种设计方法能够适应其他车辆状态记录仪的设计。     

颜色模型扰动的语义对抗样本生成方法

卷积神经网络是一种具有强大特征提取能力的深度神经网络,其在众多领域得到了广泛应用。但是,研究表明卷积神经网络易受对抗样本攻击。不同于传统的以梯度迭代生成对抗扰动的方法,提出了一种基于颜色模型的语义对抗样本生成方法,利用人类视觉和卷积模型在识别物体中表现出的形状偏好特性,通过颜色模型的扰动变换来生成对抗样本。在样本生成过程中其不需要目标模型的网络参数、损失函数或者相关结构信息,仅依靠颜色模型的变换和通道信息的随机扰动,所以这是一种可以完成黑盒攻击的对抗样本。