Shape from focus based on 3D structure tensor using optical microscopy

Shape from focus (SFF) is a widely used passive optical method for 3D shape reconstruction. In SFF, a focus measure, which is used to estimate the relative focus level, plays a critical role in depth estimation. In this article, we present a new focus measure for accurate 3D shape estimation in optical microscopy based on the analysis of 3D structure tensor. First, the 3D tensors are computed from the input image sequence for each pixel. Then, each tensor is decomposed into point, curve, and surface tensors by decomposing tensors into eigenvalues and eigenvectors. Finally, the surfaceness is used to measure the quality of sharpness. The proposed focus measure provides accurate focus values and better resistance against noise. The proposed measure is evaluated by conducting experiments using image sequences of simulated and microscopic real objects. The comparative analysis demonstrates the effectiveness of the proposed focus measure in recovering 3D shape.     

Estimating posterior inference quality of the relational infinite latent feature model for overlapping community detection

Overlapping community detection has become a very hot research topic in recent decades, and a plethora of methods have been proposed. But, a common challenge in many existing overlapping community detection approaches is that the number of communities K must be predefinedmanually. We propose a flexible nonparametric Bayesian generative model for count-value networks, which can allow K to increase as more and more data are encountered instead of to be fixed in advance. The Indian buffet process was used to model the community assignment matrix Z, and an uncollapsed Gibbs sampler has been derived.However, as the community assignment matrix Z is a structured multi-variable parameter, how to summarize the posterior inference results and estimate the inference quality about Z, is still a considerable challenge in the literature. In this paper, a graph convolutional neural network based graph classifier was utilized to help to summarize the results and to estimate the inference quality about Z. We conduct extensive experiments on synthetic data and real data, and find that empirically, the traditional posterior summarization strategy is reliable.     

改进ILoG算子的故障检测方法

针对强背景噪声干扰下轮对轴承故障特征微弱、难以准确检测的问题,提出了一种自适应改进高斯拉普拉斯(improved Laplacian of Gaussian,简称ILoG)算子的微弱故障检测方法。ILoG算子滤波器具有优良的信号突变特征检测能力,将其用于轮对轴承故障信号的冲击特征检测,同时利用水循环算法(water cycle algorithm,简称WCA)的寻优特性,并行搜寻筛选最佳的ILoG算子影响参数,通过对参数优化后ILoG算子滤波后信号做进一步包络解调分析,提取出轮对轴承微弱的故障特征信息。对实际轮对轴承外圈和内圈故障信号分析的结果表明,该方法可以有效检测出轴承微弱故障特征频率,故障检测效果优于小波阈值和多尺度形态学差值滤波方法。     

低能量密度炸药连续装药光面爆破技术研究

为了提高光面爆破成型质量，提出了一种低能量密度炸药连续装药光面爆破技术，利用LS DYNA进行数值模拟，对比2#岩石乳化炸药间隔装药和低能量密度炸药连续装药的应力场，然后将此技术用于长九（神山）灰岩矿四号隧洞控制爆破和露天边坡光面爆破。结果表明：2#岩石乳化炸药间隔装药模型中，装药段与非装药段测点应力峰值相差很大；而在低能量密度炸药连续装药模型中，沿炮孔轴向相同爆心距的测点应力峰值保持一致；使用低能量密度炸药进行连续装药，能够避免装药段的过度破坏和非装药段的欠挖，提高残孔率，且省去了导爆索捆绑时间，提高周边孔装药效率，节省了爆破成本。     

Improved Input and State Estimation for Linear Stochastic Systems with Direct Feedthrough

This paper is concerned with the problem of joint input and state estimation for linear stochastic systems with direct feedthrough. Based on the fact that each unknown input between any two time steps is always bounded, a novel improved algorithm is proposed. Compared with existing results, this algorithm can effectively enhance estimation accuracy. Moreover, the stability of the algorithm is also discussed. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed approach.     

缓冷出口温度对连续退火SPCC冷轧薄板屈服强度的影响

研究了缓冷出口温度对连续退火SPCC冷轧薄板组织和屈服强度的影响。结果表明:连续退火SPCC冷轧薄板的组织由等轴的铁素体晶粒和晶界处的块状渗碳体及晶粒内的颗粒状渗碳体组成。随着缓冷出口温度从680 ℃提高至700 ℃,平均晶粒尺寸从10.1 μm增加至12.5 μm;此外,提高缓冷出口温度还能够抑制晶界处块状渗碳体的形成,促进晶粒内细小渗碳体更加细小弥散的析出。当缓冷出口温度从680 ℃提高至700 ℃时,冷轧薄板的屈服强度约降低了28 MPa。     

基于双转子连续混炼挤出机的PPS/CF复合材料制备及性能研究

采用双转子连续混炼挤出机并通过熔融共混法制备了碳纤维(CF)增强聚苯硫醚(PPS)复合材料，并对其微观形貌、动态力学性能、力学性能和导电性能进行了研究，且对相关的影响因素进行了分析。结果表明，适当降低挤出机转子转速、提高CF含量可以改善PPS/CF复合材料的力学性能和导电性能；当转子转速为200r/min时，采用含量为20 % (质量分数，下同)的CF制得的PPS/CF复合材料的冲击强度达到49.94 J/m，体积电阻率达到60.65 Ω·cm，均优于纯PPS。     

基于VMD瞬时能量与GA- RBF的滚动轴承故障诊断

针对滚动轴承早期故障的有效识别,提出了一种基于VMD瞬时能量与GA优化的RBF神经网络的滚动轴承故障诊断方法,可以有效对滚动故障做出诊断。首先,VMD将滚动轴承振动信号进行分解成合适数目的本证模态函数;其次,计算本证模态函数分量的瞬时能量并组成特征向量;最后,将特征向量输入到GA优化的RBF神经网络实现轴承故障识别。通过滚动轴承故障诊断实验对该方法进行验证。结果表明,该方法识别滚动轴承故障的准确率为96.43%,较默认参数的RBF神经网络和EEMD瞬时能量与GA-RBF神经网络有明显的提高,证明了所提方法的可行性。     

355 MPa级转向架构架用钢连续冷却转变行为

高速动车组转向架构架对高速动车组的运行品质、行车安全起到至关重要的作用。为向355 MPa级转向架构架用钢的工业试制提供指导，在Gleeble-3800热模拟试验机上进行了热模拟试验，结合热膨胀法和金相-硬度法，绘制了355 MPa级转向架构架用钢动态连续冷却转变曲线，研究了试验钢在连续冷却条件下的显微组织演变规律。结果表明，冷速小于1 ℃/s时，其组织主要为多边形铁素体和珠光体；冷速大于5 ℃/s时，贝氏体类组织快速增加；冷速达到50 ℃/s时，为板条贝氏体组织。因此，对于355 MPa级转向架构架用钢，冷速控制在1~5 ℃/s较为适宜，其组织主要由比例适中软硬性相结合的铁素体、珠光体和贝氏体构成，可以获得优良的强韧性匹配。     

2号连铸机矩形坯断面改造与生产实践

福建罗源闽光根据三钢（集团）发展规划需要,在2号连铸机150 mm×150 mm断面的基础之上,增加160 mm×230 mm矩形坯断面。通过设备改造和工艺参数优化,在满足铸坯质量的前提下,达到炉机匹配与快速切换断面的双目标,满足了生产要求。