DNV相互影响腐蚀管道评价方法适应性分析

目的分析DNV-RP-F101方法评价相邻腐蚀管道失效压力的应用条件和计算方法的适应性。方法采用实验数据分析了DNV-RP-F101方法预测不同组合相互影响腐蚀管道失效数据的适应程度,应用已被实验数据验证可靠的非线性有限元数值,仿真研究了DNV-RP-F101标准的适应性。结果 DNV-RP-F101方法评价单腐蚀管道剩余强度的误差小,适应性强,作为相邻腐蚀管道失效压力评价基础合理。DNV-RP-F101方法评价相邻腐蚀管道失效压力的轴向间距限制条件设置合理,但环向角度限制应用条件远远偏离实际作用角度。DNV-RP-F101方法评价同尺寸相邻腐蚀管道失效压力整体误差在10%范围内;评价相邻腐蚀单独腐蚀长度系数之和超过某临界值后,预测值有大于真实值的趋势,评价结果存在预测风险;评价不同深度相邻腐蚀管道失效压力预测值明显高于真实值,评价结果存在预测风险,且误差范围跨度大,预测稳定性差。结论 DNV-RP-F101方法评价相互影响腐蚀管道剩余强度存在超过真实值的风险。     

LCD模糊熵和SOM神经网络在液压泵故障诊断中的应用

针对液压泵故障诊断问题,提出了一种基于局部特征尺度分解(Local Characteristic-scale Decomposition,LCD)、模糊熵和SOM神经网络三者相结合的故障诊断方法。对液压泵振动信号进行LCD分解,得到若干个内禀尺度分量(Intrinsic Scale Component,ISC);将ISC分量分别与原信号进行相关分析,筛选出包含主要故障信息的前几个ISC分量,计算其模糊熵并组成特征矩阵;将特征矩阵输入SOM神经网络进行分类识别。液压泵故障诊断实例表明,该方法能够准确识别液压泵典型故障,具有一定优势。通过与BP神经网络分类结果相对比,显示了SOM神经网络在特征分类方面的优越性。     

改进的神经网络模型在变形镁合金发展中的应用(英文)

采用更为合理的建模参数,将预测变形镁合金力学性能的神经网络模型进行改进,并将此模型用于发展新型镁合金;对所有建模参数以全排列组合训练的方式构建模型,并通过比较这些模型的预测误差及相关系数来确定最合理的建模参数。模型的应用主要有Mg-Zn-Mn和Mg-Zn-Y-Zr两种合金。运用改进后的模型对Mg-Zn-Mn合金的力学性能进行预测,研究Mg-Zn-Y-Zr合金中Y/Zn摩尔比对强度的影响。最后,还利用此模型发展了一种高强挤压态的Mg-Zn-Y-Zr合金。结果表明:模型预测值与实验值吻合较好,改进后的模型可以用于发展新型变形镁合金。     

Development of compacted vermicular graphite cast iron for railway brake discs

In the case of employing brake discs as a key component of mechanical brake equipment, the initiation of thermal cracking owing to repetitive thermal shock generated during braking may potentially lead to higher maintenance costs, worsened braking performance, and greater risk of railway accidents. The purpose of this study is to gain basic data to facilitate application of compacted vermicular (C. V.) graphite cast iron to brake discs in order to obtain high thermal crack resistance and improved lifetime. To this end, this study developed three types of C. V. graphite cast iron with differing content of key elements, including Ni, Cr, and Mo. Each test specimen underwent numerous tests for evaluation of materials characteristics, and the results were compared with those obtained for existing materials. The test results show that the thermal fatigue lifetime of material C is nearly double that of the conventional material. This demonstrates the suitability of material C as a material for brake discs in mid- to high-speed railway vehicles.     

自组织分层金属掺杂类金刚石薄膜的研究进展

近年来,研究学者发现在沉积过程中,某些金属元素掺杂类金刚石薄膜时能够形成一种特殊的自组织分层纳米结构,这种纳米结构克服了人为调控多层薄膜的工艺复杂性及局限性,同时赋予了薄膜更加优异的性能。主要综述了国内外对金属掺杂类金刚石薄膜中自组织分层结构的影响因素、形成机理等方面的研究现状。详细阐述了金属类型及含量、沉积条件(脉冲频率、基体偏压、气流比、沉积温度、沉积时间)、沉积方法等参数对自组织分层结构的生成及富金属层厚、富碳层厚、层数等尺寸的作用规律。重点介绍了离子重排机理、金属催化机理、强离子辐照诱导机理和靶中毒机理四种自组织分层结构形成机理的特点,并探讨了目前研究工作中存在的一些不足,如自组织分层结构的形成机理尚不清晰。上述四种机理模型均具有一定的局限性,且如何设计工艺参数实现自组织分层结构的内在调控仍是一个科学难点。针对这些问题,提出了自组织分层结构碳基薄膜的未来研究方向。     

A constrained neural learning rule for eliminating the border effect in online self-organising maps

The self-organising map (SOM) is a concise and powerful algorithm for clustering and visualisation of high-dimensional data. However, this robust algorithm still suffers from the border effect. Most of the approaches proposed to eliminate this effect use a borderless topological structure. We prefer to keep the original topological structure of the SOM for visualisation. A novel approach is proposed for the elimination of the border effect from the perspective of self-organising learning. Based on an assumption that the best matching unit (BMU) should be the most active unit, the approach proposes that the BMU should move more towards its associated input sample than its neighbours in the fine-tuned learning stage. Our constrained approach emphasises the effect of the lateral connections and neutralises the effect on the distance between the input sample and units. This approach is able to make units of the map stretch wider than the traditional SOM and thus the border effect is alleviated. Our proposed approach is proved to satisfy the requirements of the topologically ordered neural networks and is evaluated by both qualitative and quantitative criteria. All experiments conclude that performance is improved if the proposed constrained learning rule is used.     

使用石墨预处理方法生产风电机球墨铸铁件

介绍了使用石墨预处理方法生产风电机球墨铸铁件的生产实例,试验表明,使用预处理可以在一定程度上细化石墨,单位面积上的石墨球数量提高了2%,较为圆整的石墨球数量提高了8%,球化率提高了1%。采用预处理工艺获得的试样与采用传统工艺获得的试样相比,其平均屈服强度提高5%,抗拉强度提高3%,伸长率提高9%,低温抗冲击强度的平均值和3点最小值都有小幅上升,硬度则有小幅下降。采用预处理工艺后,生产的稳定性和可靠性亦有所提高。     

基于口服吸收生理药动学模型的创新药早期临床食物影响研究探索

食物对药物暴露的影响可能增加对创新药安全性和有效性评价失误的风险，因此通常创新药早期临床开发中应在多次给药剂量递增（MAD）试验前完成探索性食物影响研究。随着口服吸收生理药动学模型对食物影响预测能力的提升，本研究室提出基于口服吸收生理药动学模型的探索性食物影响的研究新路径，借助口服吸收生理药动学模型的准确预测，将符合条件的创新药探索性食物影响研究内容嵌套于MAD试验中，拟取代早期独立的食物影响研究，以降低药物开发成本和节约上市时间，并为我国早期食物影响临床研究提供参考。     

Multiple-resolution classification with combination of density estimators

We introduce a classification algorithm based on an idea of ‘multiple-resolution’ (or ‘multiscale’) approach to analysis of the data. In practice, the method uses an average of kernel density estimators where each estimator corresponds to a different data ‘resolution’. First, we examine theoretical properties of this method; next, we propose a practical implementation of such an algorithm with parameters of density estimators adjusted to minimise the misclassification probability. Subsequently, we test the algorithm on artificial data sets characterised by a ‘multiple-resolution’ property. The tests show that the introduced algorithm is superior to the basic version based on one estimator per class. We also test the algorithm on benchmark data sets and compare the results obtained with the results of other classification algorithms.     

多孔氧化铝膜在功能材料的应用

铝阳极氧化形成的具有独特结构的多孔膜的功能化，越来越引起人们的重视，在用于研制如光学元件、光电材料、分离膜、磁性材料、太阳能选择性吸收膜等功能材料方面，已显示出良好的应用前景。本文介绍了近年来有关多孔氧化铝膜在功能化应用方面的进展。     

基于PSO-ELM的316L不锈钢细长管磁粒研磨内表面粗糙度预测模型

针对316L不锈钢细长管磁粒研磨加工过程中,最佳工艺参数难以选择,以及加工后对工件内表面粗糙度（Ra）的预测问题,将影响磁粒研磨316L不锈钢细长管内表面粗糙度的四个工艺参数作为输入值,内表面粗糙度作为输出值,构建粒子群（PSO）优化极限学习机（ELM）模型来预测316L不锈钢细长管内表面粗糙度,利用PSO对工艺参数进行全局寻优,获得最佳工艺参数组合,最后通过试验与预测结果进行对比。构建的PSO-ELM表面粗糙度预测模型拟合优度R2为0.984 8,绝对误差（MAE）为0.013 4,均方根误差（RMSE）为0.021 4。得到的最佳工艺参数组合为：主轴转速2 389.011r/min,进给速度3.167 mm/s,磨料粒径216.185μm,加工时间35.856 min,预测Ra为0.178μm。对工艺参数进行调整,试验得到的Ra为0.182μm,与预测值相比误差为2.24%。基于PSO-ELM方法构建316L不锈钢细长管内表面粗糙度预测模型,实现对工件内表面粗糙度的精确预测,应用粒子群方法得到最佳工艺参数组合,提高了磁粒研磨316L不锈钢细长管的加工效率。     

Behavioural Aspects of Combining Backpropagation Learning and Self-organizing Maps

Backpropagation learning (BP) is known for its serious limitations in generalizing knowledge from certain types of learning material. In this paper, we describe a new learning algorithm, BP-SOM, which overcomes some of these limitations as is shown by its application to four benchmark tasks. BP-SOM is a combination of a multi-layered feedforward network (MFN) trained with BP and Kohonen's self-organizing maps (SOMs). During the learning process, hidden-unit activations of the MFN are presented as learning vectors to SOMs trained in parallel. The SOM information is used when updating the connection weights of the MFN in addition to standard error backpropagation. The effect of the augmented error signal is that, during learning, clusters of hiddenunit activation patterns of instances associated with the same class tend to become highly similar. In a number of experiments, BP-SOM is shown (i) to improve generalization performance (i.e. avoid overfitting); (ii) to increase the amount of hidden units that can be pruned without loss of generalization performance and (iii) to provide a means for automatic rule extraction from trained networks. The results are compared with results achieved by two other learning algorithms for MFNs: conventional BP and BP augmented with weight decay. From the experiments and the comparisons, we conclude that the hybrid BP-SOM architecture, in which supervised and unsupervised and learning co-operate in finding adequate hidden-layer representations, successfully combines the advantages of supervised and unsupervised learning.     

基于优化随机森林的H2S腐蚀产物类型及腐蚀速率预测

目的研究H2S环境下碳钢腐蚀产物类型及失重腐蚀速率预测模型,为含硫油气田管道腐蚀防护设计与选材提供依据。方法整合H2S腐蚀模拟实验数据,采用随机森林算法对各腐蚀因素重要性进行排序,一方面以腐蚀产物类型为输出量,通过随机森林分类算法建立硫铁腐蚀产物类别预测模型,另一方面以腐蚀速率为输出量,通过随机森林回归算法建立腐蚀速率预测模型,并与其他模型进行比较。运用网格搜索方法对各类算法的超参数进行优选,以提高预测可靠性。结果随机森林算法得出的影响H2S腐蚀产物类型的因素重要性排序为:H2S分压、温度、pH值、实验周期、总压、CO2分压。基于网格搜索优化的随机森林分类模型交叉验证得分超过0.9,f1得分达到0.96,优于其他三种常用分类模型。采用网格搜索优化的随机森林回归模型预测结果与实际值的均方误差为0.86%。相关系数R值为0.979,优于其他两个回归模型。结论网格搜索优化后的随机森林分类、回归模型对含H2S复杂环境下的碳钢腐蚀产物类型及腐蚀速率预测准确性较高,能够为油气田管道腐蚀防护提供参考。     

基于神经网络的连接器外壳注射工艺研究

连接器外壳结构复杂,为探究其注射工艺对成型塑件翘曲的影响,以正交方案为样本,建立三层拓扑神经网络模型。以注射温度、模具温度、注射时间、保压压力及保压时间为输入层神经元,翘曲量作为输出层神经元,由Matlab软件对模型进行编码训练,经多次迭代计算后,模型误差收敛于目标误差。经5组测试样本结果对比,所建立的模型预测误差在2.5%~3.6%,预测性能良好,将训练后模型用于实际生产指导,获得了质量合格的塑件,为后续工艺改进提供参考。     

装备磨损状态在线评估系统开发研究

针对装备在运行过程中因磨损造成装备失效的问题，进行了基于油液监测技术的装备磨损状态在线评估系统的研究与开发。通过Java技术、Microsoft SQL数据库建立监测装备的磨损健康档案并确立油液分析方法，采用改进的GM（1,1）模型实现了对在线油液数据指标发展趋势的实时预测，对装备异常磨损状态评估及装备健康演化趋势预测具有重要意义。     

基于MEA-NARX神经网络主轴热误差建模

为了有效提高基于非线性时间序列的热误差预测模型精度,利用F统计检验确定模糊C均值聚类的聚类数目,结合不同量纲一化处理的灰色关联分析排序筛选出关键温度测点,建立基于NARX神经网络的热误差预测模型,通过设置输入延时阶数、输出延时阶数和隐含层神经元个数的范围,利用思维进化算法对输入、输出延时阶数和隐含层神经元个数进行寻优,与随机选取参数的NARX神经网络预测模型相比,模型预测精度提高了36.98%。     

Syntactic Neural Networks

We introduce a new connectionist paradigm which views neural networks as implementations of syntactic pattern recognition algorithms. Thus, learning is seen as a process of grammatical inference and recognition as a process of parsing. Naturally, the possible realizations of this theme are diverse; in this paper we present some initial explorations of the case where the pattern grammar is context-free, inferred (from examples) by a separate procedure, and then mapped onto a connectionist paper. Unlike most neural networks for which structure is pre-defined, the resulting network has as many levels as are necessary and arbitrary connections between levels. Furthermore, by the addition of a delay element, the network becomes capable of dealing with time-varying patterns in a simple and efficient manner. Since grammatical inference algorithms are notoriously expensive computationally, we place an important restriction on the type of context-free grammars which can be inferred. This dramatically reduces complexity. The resulting grammars are called ‘strictly-hierarchical’ and map straightforwardly onto a temporal connectionist parser (TCP) using a relatively small number of neurons. The new paradigm is applicable to a variety of pattern-processing tasks such as speech recognition and character recognition. We concentrate here on hand-written character recognition; performance in other problem domains will be reported in future publications. Results are presented to illustrate the performance of the system with respect to a number of parameters, namely, the inherent variability of the data, the nature of the learning (supervised or unsupervised) and the details of the clustering procedure used to limit the number of non-terminals inferred. In each of these cases (eight in total), we contrast the performance of a stochastic and a non-stochastic TCP. The stochastic TCP does have greater powers of discrimination, but in many cases the results were very similar. If this result holds in practical situations it is important, because the non-stochastic version has a straightforward implementation in silicon.     

PARALLEL SELF-ORGANIZING MAP

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主成分分析和小波神经网络在气缸疲劳失效预测中的应用

对气缸疲劳失效的预测一方面可以为基于状态的维修(CBM)提供重要参考,保证系统可靠运行,另一方面可以在设计过程中针对性地提供所需的寿命值,防止气缸冗余设计。常用的基于失效样本的预测方法由于样本数据量相当有限使得所建立的预测模型不具有说服力。通过主成分分析法(PCA)和小波神经网络(WNN)建立了一种失效预测方法,并将其运用于气缸性能失效的预测。利用主成分分析法处理气缸运行过程中监测得到的多维性能参数,在保留信息完整性的条件下选择出尽量少的主成分。对样本数据进行合理分组作为小波神经网络的输入。采用遗传算法(GA)来获取小波神经网络的初始权值和阈值;对小波神经网络进行训练和测试,完成对气缸性能失效预测;实验结果表明这种方法在气缸的疲劳失效预测方面具有令人满意的效果。     

飞机防滑刹车系统自适应控制器的设计与实现

飞机的刹车系统是飞机机电系统的一个重要组成部分，当跑道有冰、雪时，飞机不但不易刹停，还难以保持两侧平衡。本文设计了基于输入输出的某型飞机防滑刹车系统自适应控制器，在机载机电系统综合管理仿真平台中进行仿真试验研究，最后比较了在湿滑路面情况下采用自适应控制器前后的仿真结果。仿真结果证明，采用自适应控制器后，刹车系统性能有很大提高。