NiTi形状记忆合金超声波焊接温度场数值计算与分析

NiTi形状记忆合金因具有优异的超弹性、形状记忆效应而被广泛应用。超声波焊接作为一种固相连接技术，在NiTi合金薄片材料的连接方面具有一定的优势，由于超声波焊接方法具有瞬时性且焊接过程十分复杂，所以难以通过试验观察的手段来研究其连接机理。针对NiTi形状记忆合金超声波焊接过程中温度难以监测的问题，采用ANSYS有限元分析软件建立NiTi合金的超声波焊接二维有限元分析模型，探究了超声波振幅对焊接温度场分布的影响规律，搭建热电偶测温平台采集焊接试验过程的温度数据对模型进行验证，结合数值模拟结果，分析了NiTi合金超声波焊接的连接机理。结果表明，焊接温度场与振幅呈正相关，在固定焊接条件下，振幅每增加5 μm，焊接最高温度约提高45℃。经试验测定，模拟结果与试验数据吻合较好，最高温度仅相差4℃，误差不超过最高温度的3%。将试验结果与模拟结果相结合，NiTi合金的超声波过程中铝没有熔化，接头为固相连接。     

极值超出量服从广义Pareto分布的载荷谱外推方法研究

提出了极值超出量服从广义Pareto分布的载荷谱外推方法。将所提方法运用到汽车用户典型路面载荷谱的外推中，分别拟合了形状参数为零和不为零的广义Pareto分布函数，对比分析了不同形状参数的分布函数对载荷谱外推结果的影响。将外推前后的载荷谱、采集载荷谱分别从幅值域和频率域进行了对比分析，研究结果表明：基于广义Pareto分布函数形状参数为零的载荷谱外推方法可得到偏于保守的外推结果；基于广义Pareto分布函数形状参数不为零的外推方法得到的外推载荷谱幅值比采集载荷谱幅值偏大。     

基于 GA-SVM 算法的通信网络入侵信号自动识别技术

针对当前通信系统入侵行为自动识别技术存在入侵信号样本识别成功率较低、误识别率和漏识别率较高的问题,提出基于 GA-SVM 算法的通信网络入侵信号自动识别技术。利用混沌原理提取通信网络入侵的非平稳信号时域特征,并凭借自回归模型提取对应频域特征,捕捉邻域入侵信号间的非线性时空动作频率,评价相邻行为间的状态关联性,预测入侵信号后续行为,完成入侵信号的识别。实验表明,所提方法识别精度高、误识别率较低,漏识别率非常低,具有可应用于实际的理论价值。     

Finite element analysis on shape deformation of the macro-structure region during hot-rolling process for gear steel FAS3420H and its modification

The rectangular macro-segregation region in the core of hot-rolled steel bar has a great influence on the heat treatment distortion of an automotive gear steel FAS3420H. In order to understand the distortion process of the segregation region during hot-rolling process, a finite element (FE) analysis of the whole hot-rolling process was carried out by software DEFORM-3D, and a constructed constitutive model was embedded into DEFORM-3D to describe the deformation process. The results show there is an inhomogeneous strain distribution at the cross-section of hot-rolled round bar, which results in uneven distortion of the segregation region in different directions. A square segregation region, which can reduce inhomogeneous distortion of gear in the subsequent heat treatment, is obtained after being rolled by simulation calculation verified through experiment.     

Progressive mean control chart is not a special case of an exponentially weighted moving average control chart

The progressive mean (PM) statistic is based on a simple idea of accumulating information of each subgroup by calculating the average progressively. Its weighting structure is based on a subgroup number that changes arithmetically, which makes the PM chart unique and efficient compared with the existing classical memory control charts. In a recent article (see reference 1), it was claimed that the PM chart is a special case of the exponentially weighted moving average (EMWA) chart. In this article, it is shown that even though the PM statistic can be written in the form of an EWMA statistic, the variance of the PM statistic is different from that of the EWMA statistic. Consequently, the limits of the PM chart are different from that of the EWMA chart. Therefore, it is found that the PM chart is not a special case of the EWMA chart; hence, the claim in reference 1 is incorrect. Furthermore, it is pointed out in this paper that no adaptive property in the weighting parameter of the PM statistic exists, further contradicting the claim in reference 1.     

一种新型地雷战斗部的数值模拟研究

针对地雷的毁伤目标和特性,提出一种新颖的多棱柱状药型罩结构,其可看作由八个楔形罩对称排列相邻连接而成。应用非线性有限元软件完成了爆炸载荷下多棱柱状药型罩形成射流过程的数值模拟,结果表明新型药型罩结构能够实现预期设想,形成一股汇聚射流。研究结果为地雷战斗部研究提供了一种新的选择。     

基于多通道的ICU脑血管疾病死亡风险预测模型

死亡风险预测指根据病人临床体征监测数据来预测未来一段时间的死亡风险。对于ICU病患，通过死亡风险预测可以有针对性地对病人做出临床诊断，以及合理安排有限的医疗资源。基于临床使用的MEWS和Glasgow昏迷评分量表，针对ICU病人临床监测的17项生理参数，提出一种基于多通道的ICU脑血管疾病死亡风险预测模型。引入多通道概念应用于BiLSTM模型，用于突出每个生理参数对死亡风险预测的作用。采用Attention机制用于提高模型预测精度。实验数据来自MIMIC [Ⅲ]数据库，从中提取3?080位脑血管疾病患者的16?260条记录用于此次研究，除了六组超参数实验之外，将所提模型与LSTM、Multichannel-BiLSTM、逻辑回归（logistic regression）和支持向量机（support vector machine, SVM）四种模型进行了对比分析，准确率Accuracy、灵敏度Sensitive、特异性Specificity、AUC-ROC和AUC-PRC作为评价指标，实验结果表明，所提模型性能优于其他模型，AUC值达到94.3%。     

A novel long short-term memory networks-based data-driven prognostic strategy for proton exchange membrane fuel cells

Proton exchange membrane fuel cell (PEMFC) long-term prognostic facilitates reducing the time/cost of the durability tests and is a critical starting point for control/maintenance suggestions. Long short-term memory (LSTM) recurrent neural networks have excellent time series processing capabilities and are proved to be useful for the short-term prognostic of PEMFC. However, LSTM prognostic models usually suffer from accumulated errors and model recognition uncertainties, which make it difficult to break the historical degradation data limitations, resulting in unsatisfactory long-term prediction performance. To tackle the problem, this paper proposes a novel model named navigation sequence driven LSTM (NSD-LSTM) for long-term prognostic. In the strategy, a navigation sequence is firstly generated by using an autoregressive integrated moving average model with exogenous variables. The sequence is then fed iteratively into LSTM in the implementation stage to achieve long-term perdition. The proposed strategy is evaluated using the aging experimental data of two types of PEMFC under different operating conditions. The long-term prognostic performance of the proposed model and other two state-of-the-art prognostic models, namely, nonlinear autoregressive exogenous and echo state network, are evaluated through comparison experiments. The simulation and experimental results show that the proposed prognostic strategy has better long-term degradation trend prediction consistency and remaining useful life estimation robustness.