工艺参数对动态流量控制法挤压弯管应力的影响

采用有限元模拟和实验研究了挤压钛合金弯曲管件。通过实验验证了工件的形状和尺寸精度,并通过有限元模拟分析了工艺参数对挤出过程中变形体的平均压应力分布情况和挤出弯管件的曲率半径的影响规律。结果表明:有限元模拟中,弯管件的曲率半径误差为6.03%,弯管直径误差为3.82%;在靠近定径带处,平均压应力呈非均匀分布;在焊合腔内,靠近细分流孔区域的平均压应力小于靠近粗分流孔区域的平均压应力,平均压应力的大小顺序在通过粗、细分流孔前后相反;在模具结构固定不变时,弯管件的曲率半径随挤压速度的减小而增大,不随挤压温度的变化而变化。     

一类分数阶微积分的中值定理及应用

Riemann-Liouville分数阶微积分算子是一类带有一个函数的分数阶微积分算子的特殊情形，以Riemann-Liouville分数阶微积分算子的积分中值定理和微分中值定理为基础，我们得到了一类带有一个函数的分数阶微积分算子的积分中值定理和微分中值定理，并给出其在计算方面的一些应用。     

Convolutional Neural Network Auto Encoder Channel Estimation Algorithm in MIMO-OFDM System

Higher transmission rate is one of the technological features of prominently used wireless communication namely Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO–OFDM). One among an effective solution for channel estimation in wireless communication system, specifically in different environments is Deep Learning (DL) method. This research greatly utilizes channel estimator on the basis of Convolutional Neural Network Auto Encoder (CNNAE) classifier for MIMO-OFDM systems. A CNNAE classifier is one among Deep Learning (DL) algorithm, in which video signal is fed as input by allotting significant learnable weights and biases in various aspects/objects for video signal and capable of differentiating from one another. Improved performances are achieved by using CNNAE based channel estimation, in which extension is done for channel selection as well as achieve enhanced performances numerically, when compared with conventional estimators in quite a lot of scenarios. Considering reduction in number of parameters involved and re-usability of weights, CNNAE based channel estimation is quite suitable and properly fits to the video signal. CNNAE classifier weights updation are done with minimized Signal to Noise Ratio (SNR), Bit Error Rate (BER) and Mean Square Error (MSE).     

Deterministic and probabilistic ship pitch prediction using a multi-predictor integration model based on hybrid data preprocessing,reinforcement learning and improved QRNN

The deterministic and probabilistic prediction of ship motion is important for safe navigation and stable real-time operational control of ships at sea. However, the volatility and randomness of ship motion, the non-adaptive nature of single predictors and the poor coverage of quantile regression pose serious challenges to uncertainty prediction, making research in this field limited. In this paper, a multi-predictor integration model based on hybrid data preprocessing, reinforcement learning and improved quantile regression neural network (QRNN) is proposed to explore the deterministic and probabilistic prediction of ship pitch motion. To validate the performance of the proposed multi-predictor integrated prediction model, an experimental study is conducted with three sets of actual ship longitudinal motions during sea trials in the South China Sea. The experimental results indicate that the root mean square errors (RMSEs) of the proposed model of deterministic prediction are 0.0254°, 0.0359°, and 0.0188°, respectively. Taking series #2 as an example, the prediction interval coverage probabilities (PICPs) of the proposed model of probability predictions at 90%, 95%, and 99% confidence levels (CLs) are 0.9400, 0.9800, and 1.0000, respectively. This study signifies that the proposed model can provide trusted deterministic predictions and can effectively quantify the uncertainty of ship pitch motion, which has the potential to provide practical support for ship early warning systems.     

空间曲线的特征识别与高质量非均匀采样

为避免传统均匀采样方法因忽视曲线重要特征而生成不理想的采样结果,获得给定数量且由特征点和辅助点组成的采样点序列,提出基于特征识别的高质量空间曲线非均匀采样方法.首先使用抛物线插值法得到曲线上所有曲率极大值点和挠率极大值点的近似位置,经筛选后产生特征点,以更好地抓住空间曲线的轮廓特征.然后定义基于弧长、曲率和挠率加权组合的特征函数,并以此自适应地选取曲线上的辅助点.与3种主流采样方法比较的实验结果表明,该方法能够获得更高质量的采样结果且具有更好的实用性,从而进一步改善空间曲线的B样条拟合效果.     

Weighted power means of q-rung orthopair fuzzy information and their applications in multiattribute decision making

Weighted power means with weights and exponents serving as their parameters are generalizations of arithmetic means. Taking into account decision makers' flexibility in decision making, each attribute value is usually expressed by a $q$-rung orthopair fuzzy value ($q$-ROFV, $q\ge 1$), where the former indicates the support for membership, the latter support against membership, and the sum of their $q$th powers is bounded by one. In this paper, we propose the weighted power means of $q$-rung orthopair fuzzy values to enrich and flourish aggregations on $q$-ROFVs. First, the $q$-rung orthopair fuzzy weighted power mean operator is presented, and its boundedness is precisely characterized in terms of the power exponent. Then, the $q$-rung orthopair fuzzy ordered weighted power mean operator is introduced, and some of its fundamental properties are investigated in detail. Finally, a novel multiattribute decision making method is explored based on developed operators under the $q$-rung orthopair fuzzy environment. A numerical example is given to illustrate the feasibility and validity of the proposed approach, and it is shown that the power exponent is an index suggesting the degree of the optimism of decision makers.     

基于EVSEWT的下击暴流时变平均风提取

下击暴流等极端风具有强烈的非平稳特性，给风场特性分析、结构响应计算带来挑战。根据经验将非平稳风分为时变平均风和零均值脉动风分别进行分析，是处理非平稳风速信号的有效方法。分析了几种常见时变均值提取方法的优缺点，在此基础上提出一种基于能量波谷寻找的经验小波变换方法，用于下击暴流时变平均风速提取，并将提取的时变平均风和脉动风用于分析某幢高层建筑顶部位移响应。对2组下击暴流的分析结果得出：与经验模态分解和离散小波变换方法相比，基于能量波谷寻找的经验小波变换方法模型提取的时变平均风更符合预期，对应的结构响应偏于安全。     

Turbulent flame structure characteristics of hydrogen enriched natural gas with CO2 dilution

This paper investigated the hydrogen enriched methane/air flames diluted with CO₂. The turbulent premixed flame was stabilized on a Bunsen type burner and the two dimensional instantaneous OH profile was measured by Planar Laser Induced Fluorescence (PLIF). The flame front structure characteristics were obtained by extracting the flame front from OH-PLIF images. And the turbulence-flame interaction was analyzed through the statistic parameters. The role of hydrogen addition as well as CO₂ dilution on the features of turbulent flame were revealed by those parameters. In this work, hydrogen fractions of 0, 0.2 and CO₂ dilution ratios of 0, 0.05 and 0.1 were studied. Results showed that hydrogen addition can enhance turbulent burning velocity ST/S_L through decreasing the scale of the finer structure of the wrinkled flame front, caused by the smaller flame instability scale. In contrast, CO₂ dilution decreased turbulent burning velocity S_T/S_L due to its inactive response to turbulence perturbation and larger flame wrinkles. For all flames, the probability density function (PDF) profile of the local curvature radius R shows a bias to positive value, resulted from the flame intrinsic instability. The PDF profile of R decreases with CO₂ dilution, while the value of local curvature radius corresponding to the peak PDF is larger. This indicates that larger wrinkles structure was generated due to CO₂ dilution, which leads to the decrease in S_T/S_L as a consequence. Hydrogen addition increases the flame volume and results in more intense combustion. CO₂ dilution has a decrease effect on flame volume for both X_H2 = 0 and X_H2 = 0.2 while the decrease is obvious at X_H2 = 0.2, Z_CO2 = 0.1. In all, hydrogen enrichment improves the combustion while CO₂ can moderate combustion. Therefore, adding hydrogen and CO₂ in natural gas can be a potential method for adjusting the combustion intensity in combustion chamber during the combustor design.     

一类两性分枝过程条件均值增长率的极限性质

本文主要研究随机环境中配对依赖人口数两性 Galton-Watson 分枝过程的条件均值增长率的极限性质．利用上可加函数的性质，得到配对单元平均增长率的极限性质和该过程条件均值的上界和下界．文中给出了关于过程条件均值增长率的两个序列，利用配对单元平均增长率的性质，获得了这两个序列的极限性质．随机环境中配对依赖人口数两性分枝过程比较复杂，本文的结论推广了现有的研究成果．