Fabrication of alumina ceramics with functional gradient structures by digital light processing 3D printing technology

Alumina ceramics with different unit numbers and gradient modes were prepared by digital light processing (DLP) 3D printing technology. The side length of each functional gradient structure was 10 mm, the porosity ratio was controlled to 70%, and the number of units were (1 × 1 × 1 unit) and (2 × 2 × 2 unit) respectively. The different gradient modes were named FCC, GFCC-1, GFCC-2 and GFCC-3. SEM, XRD, and other characterization methods proved that these gradient structures of alumina ceramics had only α-Al2O3 phase and good surface morphology. The mechanical properties and energy absorption properties of alumina ceramics with different functional gradient structures were studied by compression test. The results show that the gradient structure with 1 × 1 × 1 unit has better mechanical properties and energy absorption properties when the number of units is different. When the number of units is the same, GFCC-2 and GFCC-3 gradient structures have better compressive performance and energy absorption potential than FCC structures. The GFCC-2 gradient structure with 1 × 1 × 1 unit has a maximum compressive strength of 19.62 MPa and a maximum energy absorption value of 2.72 × 105 J/m3. The good performance of such functional gradient structures can provide new ideas for the design of lightweight and compressive energy absorption structures in the future.     

C105管桩混凝土设计参数取值的探讨

通过试验得出了C105混凝土100 mm×100 mm×100 mm立方体非标准试件抗压强度换算系数以及C105混凝土的标准方差和强度变异系数。采用理论计算,得到了棱柱体抗压强度与立方体抗压强度的比值α_c1,并与试验实测平均值进行了比较,验证了理论计算方法的可行性。此外,通过对比分析,探讨了直接外延法和引用公式法两种C105混凝土设计参数计算方法的正确性,研究结果可为C105管桩混凝土的设计和应用提供参考。     

Prediction of pullout interaction coefficient of geogrids by extreme gradient boosting model

Geogrids embedded in fill materials are checked against pullout failure through standard pullout testing methodology. The test determines the pullout interaction coefficient which is critical in fixing the embedment length of geogrids in mechanically stabilized earth walls. This paper proposes prediction of pullout interaction coefficient using data driven machine learning regression algorithms. The study primarily focusses on using extreme gradient boosting (XGBoost) method for prediction. A data set containing 220 test results from the literature has been used for training and testing. Predicted results of XGBoost have been compared with the results of random forest (RF) ensemble learning based algorithm. The predictions of XGBoost model indicates 85% accuracy and that of RF model shows 77% accuracy, indicating significantly superior and robust prediction through XGBoost above RF model. The importance analysis indicates that normal stress is the most significant factor that influences the pullout interaction coefficients. Subsequently pullout tests have been performed on geogrid embedded in four different fill materials at three normal stresses. The proposed XGBoost model gives 90% accuracy in prediction of pullout interaction coefficient compared to laboratory test results. Finally, an open-source graphical user interface based on the XGBoost model has been created for preliminary estimation of the pullout interaction coefficient of geogrid at different test conditions.     

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).     

基于数值模拟的挤压参数对AZ91镁合金管转角焊合室分流挤压焊合压力的影响规律研究

提出了一种镁合金管材转角焊合室分流挤压新工艺，该工艺可在有效延长焊合室长度和焊合时间前提下保证舌针刚度，从而保证管材尺寸精度，并且可通过转角剪切变形机制增加预焊合金属变形量和动态再结晶程度，从而有利于提高管材性能和焊缝焊合性能。利用有限元法揭示了转角焊合室分流挤压成形过程中金属的流动特征，应变分布特征和焊合室内的静水压力分布特征。结果表明，整个挤压过程无金属折叠，从而保证管材的表面质量；流经转角后预焊合金属变形量明显增加，有利于提高管材质量和焊缝质量。最后，研究揭示了坯料初始温度，挤压速度和模具转角对焊合室内静水压力的影响规律。结果表明，随着挤压速度的增加和模具转角的增大，转角焊合室内静水压力增大；随着坯料预热温度的增加，转角焊合室内静水压力呈先增大后减小的趋势。     

梯度铝蜂窝夹芯板的力学行为

梯度分层铝合金蜂窝板是一种有效的吸能结构,本工作在梯度铝蜂窝结构的基础上根据梯度率的概念,通过改变蜂窝芯层的胞壁长度,设计了4种质量相同、梯度率不同的铝蜂窝夹芯结构。通过准静态压缩实验,并结合非线性有限元模拟准静态及冲击态下梯度铝蜂窝夹芯结构的变形情况及其力学性能,分析对比了相同质量下梯度铝蜂窝夹芯结构在准静态下的变形模式以及冲击载荷下分层均质蜂窝结构和不同梯度率的分层梯度蜂窝结构的动态响应和能量吸收特性。结果表明:在准静态压缩过程中,铝蜂窝梯度夹芯板的变形具有明显的局部化特征,蜂窝芯的变形为低密度优先变形直至密实,层级之间的密实化应变差随芯层密度的增大而逐渐减小;在高速冲击下,梯度蜂窝板并非严格按照准静态过程中逐级变形直至密实,而是在锤头冲击惯性及芯层密度的相互作用下整体发生的线弹性变形、弹性屈曲、塑性坍塌及密实化;另外,在本工作所设计的梯度率中,当梯度率为γ₁=0.0276时,梯度蜂窝夹芯板的吸能性达到最好,相较于同等质量下的均质蜂窝夹芯板,能量吸收提高了10.63%。     

Effect of pressure on elemental diffusion in sulfur–iron reaction

The reaction between sulfur and iron under high pressure and high temperature (HPHT) was studied. Sulfur–iron reaction models under different pressure levels were constructed. The morphology and formation mechanism of the reactants were comprehensively analyzed by scanning electron microscopy, energy-dispersive spectroscopy–line scanning, metallographic microscopy, and Raman spectroscopy. The results indicated that the pressure of the reaction could significantly affect the diffusion behavior of sulfur and iron during the reaction. With an increase in pressure, the diffusion of iron in the system was inhibited, whereas that of sulfur was enhanced. The pressure distribution gradient at the reaction interface was simulated by finite element calculation. The effect of pressure gradient as the driving force of the reaction on the diffusion behavior of elements was evaluated by thermodynamics combined with experimental results. Based on the experimental results, finite element simulation, and formula derivation, a new standpoint was proposed: the diffusion of substances in the HPHT system was affected by the pressure gradient at the interface.     

基于归一化水体指数及其阈值自适应算法的水体遥感反演效果分析

归一化水体指数（NDWI）是水体遥感反演的一种重要指标，其阈值及修正直接影响反演结果的精度。基于Landsat-8与GF-1光学多光谱影像，使用归一化水体指数法阈值0（TH₀）、最大类间方差法（OTSU）自适应阈值（TH_otsu）与均值漂移聚类算法（Mean-Shift）自适应阈值（TH_MS）分别对典型正常水体、云雾覆盖水体、富营养化水体、高含泥沙水体进行水体遥感提取与效果分析，结果表明：正常水体以TH₀为阈值提取精度最高，THMS提取精度次之，TH_otsu提取精度最差；而云雾覆盖水体、富营养化水体以及含泥沙水体使用THMS提取精度最高，尤其少量云雾覆盖下的水体，TH_MS具有更明显的优势，TH₀提取精度次之，TH_otsu提取精度最差；对于不同的阈值，Land?sat-8比GF-1总体表现出更高的水体提取精度。Mean-Shift算法应用于NDWI阈值修正与水体遥感反演具有快速、水质适应性强、效果稳定的优势，对尤其是复杂条件下的水体信息遥感反演具有较好的提取效果。     

High-roubustness keystroke recognition method based on acoustic spatial gradient

For the fluctuation of CFCC caused by environmental noise is the main reason for the low accuracy of keystroke detection,the spatial characteristics of adjacent between CFCC were studied,and the spatial gradient structure of CFCC based on points was established.On this basis,the effect of CFCC spatial gradient on keystroke content recognition and the selection of precise neighborhood points were studied on training and testing.Finally,a high-robustness keystroke recognition algorithm based on acoustic signals was constructed.Extensive experiments in different environments demonstrate that the proposed CFCC spatial gradient sound feature achieves great performance and the recognition accuracy is 96.15%.     

提升管内颗粒浓度梯度力的特性

由于Kutta-Joukowski横向力与浓度梯度力的共同作用，提升管内颗粒沿径向在边壁大量聚集并形成稳定的环?核结构。根据实验数据，分析了颗粒浓度梯度的径向分布特征，考察了不同操作条件下浓度梯度力系数K的分布特性。由Kutta-Joukowski横向力与浓度梯度的关系，提出了浓度梯度力的表达式Fρ=K(dρ/dr)A及浓度梯度力系数K的表达式K=[?ρg(νg?νp)(?v/?r)r]/?dρ/dr+(d2ρ/dr2)r?。提升管内颗粒群受到的浓度梯度力与浓度梯度力系数K有关。浓度梯度力系数K在提升管中心处为0，沿提升管径向呈“N”型分布，随表观气速增加而增加；提升管内充分发展段K的数值明显大于提升管加速区和出口约束区，总结了浓度梯度力系数K的经验关联式。