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.     

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.     

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.     

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。     

一种基于深度森林的恶意代码分类方法

针对当前恶意代码静态分析方法精度不足的问题，将恶意代码映射为无压缩的灰度图像，然后根据图像变换方法将图像变换为恒定大小的图像，使用方向梯度直方图提取图像的特征，最后提出一种基于深度森林的恶意代码分类方法.实验中选择不同家族的多个恶意代码样本进行分类，验证了该方法的有效性，并且实验结果优于近期提出的SPAM-GIST方法.     

基于双应力加速寿命试验的关节轴承寿命预测与可靠性分析

自润滑关节轴承寿命主要是由衬垫的磨损性能决定的,但衬垫的磨损一般是非线性的,使得寿命难以预测。采用同时改变载荷和摆频2种应力的方法进行加速寿命试验,建立以pv值、磨损量退化数据为输入参数,寿命值为输出参数的灰色神经网络预测模型。经验证该预测模型对关节轴承寿命预测的最大误差仅为7.33%,平均误差仅为3.892%。对不同加速应力下自润滑关节轴承可靠性进行评估,结果表明,关节轴承的可靠性在L_(10)(可靠度为90%时的寿命)之前下降趋势缓慢,然后迅速下降,pv值越大可靠性下降越迅速;随着pv值的增大,关节轴承寿命近似呈指数下降,经验证可用逆幂率加速模型反映二者关系。     

Hybrid self-inertia weight adaptive particle swarm optimisation with local search using C4.5 decision tree classifier for feature selection problems

ABSTRACT

Feature selection is an important task to improve the classifier’s accuracy and to decrease the problem size. A number of methodologies have been presented for feature selection problems using metaheuristic algorithms. In this paper, an improved self-adaptive inertia weight particle swarm optimisation with local search and combined with C4.5 classifiers for feature selection algorithm is proposed. In this proposed algorithm, the gradient base local search with its capacity of helping to explore the feature space and an improved self-adaptive inertia weight particle swarm optimisation with its ability to converge a best global solution in the search space. Experimental results have verified that the SIW-APSO-LS performed well compared with other state of art feature selection techniques on a suit of 16 standard data sets.     

Learning bipartite Bayesian networks under monotonicity restrictions

ABSTRACT

Learning parameters of a probabilistic model is a necessary step in machine learning tasks. We present a method to improve learning from small datasets by using monotonicity conditions. Monotonicity simplifies the learning and it is often required by users. We present an algorithm for Bayesian Networks parameter learning. The algorithm and monotonicity conditions are described, and it is shown that with the monotonicity conditions we can better fit underlying data. Our algorithm is tested on artificial and empiric datasets. We use different methods satisfying monotonicity conditions: the proposed gradient descent, isotonic regression EM, and non-linear optimization. We also provide results of unrestricted EM and gradient descent methods. Learned models are compared with respect to their ability to fit data in terms of log-likelihood and their fit of parameters of the generating model. Our proposed method outperforms other methods for small sets, and provides better or comparable results for larger sets.