From Reduction-based to Reduction-free Normalization

We present a systematic construction of a reduction-free normalization function. Starting from a reduction-based normalization function, i.e., the transitive closure of a one-step reduction function, we successively subject it to refocusing (i.e., deforestation of the intermediate reduced terms), simplification (i.e., fusing auxiliary functions), refunctionalization (i.e., Church encoding), and direct-style transformation (i.e., the converse of the CPS transformation). We consider two simple examples and treat them in detail: for the first one, arithmetic expressions, we construct an evaluation function; for the second one, terms in the free monoid, we construct an accumulator-based flatten function. The resulting two functions are traditional reduction-free normalization functions.The construction builds on previous work on refocusing and on a functional correspondence between evaluators and abstract machines. It is also reversible.     

11C(d,n)12N反应角分布的测量和虚衰变12N →11C+p渐近归一化系数的确定

利用HI 13串列加速器次级束流线产生的11C束测量了质心系能量为9.8 MeV的11C(d,n)12N反应的角分布。通过改变DWUCK4程序中径向积分的下限,验证了该反应的周边性。比较了实验结果与扭曲波玻恩近似(DWBA)计算,得出虚衰变12 N→11 C p的渐近归一化系数(ANC)平方为(2.86±0.91) fm-1。结果表明:直接俘获对11C(p,γ)12N反应有重要贡献。     

基于理想优度的包装升降装置运动方案设计

目的为更全面、更科学地评价包装升降装置的设计方案,为其设计与优化提供有效的决策依据。方法将TRIZ理论的理想度与可拓学的优度评价进行融合,构建集成理想度与优度的机械运动方案评价方法,即以规范化的理想度为关联函数,以多级可拓综合评价过程为路径,对机械运动方案进行可靠、客观地评价。结果将理想优度用于包装升降装置运动方案设计的实践,建立各评价指标的理想度测算方法,并采用分子与分母的规范化来克服理想度计算中出现的分子分母量纲不一致的问题,以规范化的理想度为关联度,经过与权重系数合成得到优度,将关联函数选择、关联度计算及其规范化等计算过程简化为单一的理想度计算,快速完成包装升降装置运动方案的优选。结论理想优度简化了机械运动方案的可拓评价流程,通过在包装升降装置运动方案设计的实践,进一步证实了理想优度评价的有效性,并加快了评价速度,有助于提高机械运动方案的设计与评价效率。     

A fast pixel matching method based on phase feature extraction in online phase-measuring profilometry

A fast pixel matching method based on phase feature extraction in online phase-measuring profilometry is proposed, which is used to solve the problem where the object motion is along a straight line. The movement of the object results in the displacement of images in the captured deformed patterns, so pixel matching is carried out to modify the positions of images, which not only realizes the point-to-point correspondence of the object, but extracts the equivalent phase-shifting deformed patterns. So, the 3D surface shape of the object moving on the pipeline can be reconstructed successfully with an equivalent phase-shifting algorithm. The equiphase surface phase feature extracted from the phase distributions of the object obtained by Fourier transform profilometry is used to assist pixel matching in this paper. Normalization and further binarization of the phase feature extraction improve pixel matching’s accuracy and speed. The simulation and experiments verify the feasibility and validity of the proposed method.     

Investigation on the Chinese Text Sentiment Analysis Based on Convolutional Neural Networks in Deep Learning

Nowadays, the amount of wed data is increasing at a rapid speed, which presents a serious challenge to the web monitoring. Text sentiment analysis, an important research topic in the area of natural language processing, is a crucial task in the web monitoring area. The accuracy of traditional text sentiment analysis methods might be degraded in dealing with mass data. Deep learning is a hot research topic of the artificial intelligence in the recent years. By now, several research groups have studied the sentiment analysis of English texts using deep learning methods. In contrary, relatively few works have so far considered the Chinese text sentiment analysis toward this direction. In this paper, a method for analyzing the Chinese text sentiment is proposed based on the convolutional neural network (CNN) in deep learning in order to improve the analysis accuracy. The feature values of the CNN after the training process are nonuniformly distributed. In order to overcome this problem, a method for normalizing the feature values is proposed. Moreover, the dimensions of the text features are optimized through simulations. Finally, a method for updating the learning rate in the training process of the CNN is presented in order to achieve better performances. Experiment results on the typical datasets indicate that the accuracy of the proposed method can be improved compared with that of the traditional supervised machine learning methods, e.g., the support vector machine method.     

Detecting Iris Liveness with Batch Normalized Convolutional Neural Network

Aim to countermeasure the presentation attack for iris recognition system, an iris liveness detection scheme based on batch normalized convolutional neural network (BNCNN) is proposed to improve the reliability of the iris authentication system. The BNCNN architecture with eighteen layers is constructed to detect the genuine iris and fake iris, including convolutional layer, batch-normalized (BN) layer, Relu layer, pooling layer and full connected layer. The iris image is first preprocessed by iris segmentation and is normalized to 256×256 pixels, and then the iris features are extracted by BNCNN. With these features, the genuine iris and fake iris are determined by the decision-making layer. Batch normalization technique is used in BNCNN to avoid the problem of over fitting and gradient disappearing during training. Extensive experiments are conducted on three classical databases: the CASIA Iris Lamp database, the CASIA Iris Syn database and Ndcontact database. The results show that the proposed method can effectively extract micro texture features of the iris, and achieve higher detection accuracy compared with some typical iris liveness detection methods.     

Adaptive nonlinear control using input normalized neural networks

An adaptive feedback linearization technique combined with the neural network is addressed to control uncertain nonlinear systems. The neural network-based adaptive control theory has been widely studied. However, the stability analysis of the closed-loop system with the neural network is rather complicated and difficult to understand, and sometimes unnecessary assumptions are involved. As a result, unnecessary assumptions for stability analysis are avoided by using the neural network with input normalization technique. The ultimate boundedness of the tracking error is simply proved by the Lyapunov stability theory. A new simple update law as an adaptive nonlinear control is derived by the simplification of the input normalized neural network assuming the variation of the uncertain term is sufficiently small.     

捆扎线材图像识别的研究

根据图像处理与识别的方法,文章在不考虑线材由于油污造成的孔洞和线材区域间的粘贴的情况下,利用方向图的方法,成功的实现了线材的分块,然后利用所分模块的特征,实现标准化打捆包装要求和棒材在线快速计数.试验结果验证:这种方法的可行性和有效性.     

交直流混合微电网分段协调控制策略

为解决交直流混合微电网因微源出力变化、负荷变化和储能装置因荷电状态导致充放电功率发生变化等引起的功率波动问题,提出一种混合微电网分段协调控制策略。针对孤岛状态下的混合微电网,分析了混合微电网的典型拓扑和各运行模式下的功率关系。采用标幺化的方法得到了可表征混合微电网整体运行状态的特征量,根据该特征量的变化量对控制策略进行分段,对各段控制的工作原理进行了详细的分析,具体研究了在不同控制段中各变流器的相互协调;针对可能出现的网间交换功率振荡以及互联变流器(ILC)运行模式频繁切换的问题,对动作判据进行了补偿。最后,在PSCAD/EMTDC中建立了仿真模型,仿真结果表明,在不同工况下各变流器均可迅速做出响应,保证系统的稳定运行。     

基于双向长短期记忆网络的电力系统暂态稳定评估

为进一步提升电力系统暂态稳定评估的准确率,依据电力系统暂态过程数据的时序特性,建立了一种基于双向长短期记忆(Bi-LSTM)网络的暂态稳定评估模型。该方法通过Bi-LSTM网络建立底层量测数据与电力系统暂态稳定类别之间的非线性映射关系,采用准确率、F1指标和FPR指标综合评估Bi-LSTM网络模型性能的优劣,在此基础上,采用t分布随机近邻嵌入(t-SNE)降维方法和k最近邻(KNN)分类器进一步提升暂态稳定评估的准确率。新英格兰10机39节点系统算例表明:所提模型比传统的机器学习模型和部分深度学习模型拥有更好的评估性能。通过可视化方法和网络预测分数对评估模型进行分析,结果表明Bi-LSTM网络模型具有较强的电力系统暂态过程特征提取能力,适用于电力系统暂态稳定性的评估。进一步研究了底层输入数据的归一化模式和方法对暂态评估模型的影响,结果表明z-score归一化方法要优于min-max归一化方法,采用总维数归一化模式的模型评估性能更好。