不同进水流速对泵站进水池漩涡的影响

为探究泵站进水流速大小与泵站进水池水流流态、漩涡的产生与发展变化规律,结合泵站实际运行情况,建立引渠、前池、进水池和进水管的泵站物理模型和湍流数学模型,采用VOF模型和非定常的SST k-ω湍流模型对9种不同流速的泵站进水水流特性进行数值模拟,分析不同进水流速的泵站进水池水流流场分布、漩涡涡量的变化及分布规律。研究结果表明:当进水流速为0.322 2～0.564 2 m/s时,泵站表面漩涡的强度随进水流速的增大而增强:当进水流速为0.322 2～0.401 6 m/s时,进水池出现Ⅲ、Ⅳ型漩涡;当进水流速为0.483 5 m/s时,进水池出现Ⅴ型漩涡;当进水流速为0.520 8～0.564 2 m/s时,进水池出现Ⅵ型漩涡。将数值计算结果与模型试验结果进行对比,两者基本吻合。研究结果可为泵站工程设计提供参考。     

Predictive process mapping for laser powder bed fusion: A review of existing analytical solutions

One of the main challenges in the laser powder bed fusion (LPBF) process is making dense and defect-free components. These porosity defects are dependent upon the melt pool geometry and the processing conditions. Power-velocity (PV) processing maps can aid in visualizing the effects of LPBF processing variables and mapping different defect regimes such as lack-of-fusion, under-melting, balling, and keyholing. This work presents an assessment of existing analytical equations and models that provide an estimate of the melt pool geometry as a function of material properties. The melt pool equations are then combined with defect criteria to provide a quick approximation of the PV processing maps for a variety of materials. Finally, the predictions of these processing maps are compared with experimental data from the literature. The predictive processing maps can be computed quickly and can be coupled with dimensionless numbers and high-throughput (HT) experiments for validation. The present work provides a boundary framework for designing the optimal processing parameters for new metals and alloys based on existing analytical solutions.     

A Smart Deep Convolutional Neural Network for Real-Time Surface Inspection

A proper detection and classification of defects in steel sheets in real time have become a requirement for manufacturing these products, largely used in many industrial sectors. However, computers used in the production line of small to medium size companies, in general, lack performance to attend real-time inspection with high processing demands. In this paper, a smart deep convolutional neural network for using in real-time surface inspection of steel rolling sheets is proposed. The architecture is based on the state-of-the-art SqueezeNet approach, which was originally developed for usage with autonomous vehicles. The main features of the proposed model are: small size and low computational burden. The model is 10 to 20 times smaller when compared to other networks designed for the same task, and more than 700 times smaller than general networks. Also, the number of floating-point operations for a prediction is about 50 times lower than the ones used for similar tasks. Despite its small size, the proposed model achieved near-perfect accuracy on a public dataset of 1800 images of six types of steel rolling defects.     

Reduced‐scale test to assess the effect of fire barriers on the flaming combustion of cored composites: An upholstery‐material case study

Herein, we describe a reduced‐scale test (“Cube” test), measuring the fire performance of specimens including a fire barrier (FB) and a flammable core material, which acts as the main fuel load. The specimen is intended to reproduce a cross‐section of a composite product where heat/mass transfer occurs primarily in a direction perpendicular to the FB. The Cube test procedure and benefits are discussed in this work by adopting residential upholstery furniture as an exemplary study. One flexible polyurethane foam, one polypropylene cover fabric, and 10 commercially available FBs were selected. They were used to compare the fire performance of FBs, measured in terms of peak of heat release rate, in the ASTM E1474‐14 standard test and the newly developed Cube test. Edge effects severely affected the performance of FBs in the ASTM E1474‐14 standard test but not in the Cube test. Furthermore, appropriate test conditions were determined in the Cube test to measure the so‐called “wetting point,” that is, the time and value of heat release rate measured when flammable liquid products were first observed on the bottom of the specimen. The relevance of the “wetting point” in terms of full‐scale fire performance and failure mechanism of FBs is discussed.     

Sequential alignment attention model for scene text recognition

Scene text recognition has been a hot research topic in computer vision due to its various applications. The state-of-the-art solutions usually depend on the attention-based encoder-decoder framework that learns the mapping between input images and output sequences in a purely data-driven way. Unfortunately, there often exists severe misalignment between feature areas and text labels in real-world scenarios. To address this problem, this paper proposes a sequential alignment attention model to enhance the alignment between input images and output character sequences. In this model, an attention gated recurrent unit (AGRU) is first devised to distinguish the text and background regions, and further extract the localized features focusing on sequential text regions. Furthermore, CTC guided decoding strategy is integrated into the popular attention-based decoder, which not only helps to boost the convergence of the training but also enhances the well-aligned sequence recognition. Extensive experiments on various benchmarks, including the IIIT5k, SVT, and ICDAR datasets, show that our method substantially outperforms the state-of-the-art methods.     

石墨烯纳米制冷剂核态池沸腾换热的实验研究

为分析单层石墨烯纳米片对核态池沸腾换热的影响机理，对基液为R141b、分散相为单层石墨烯纳米片的纳米制冷剂的核态池沸腾换热特征进行了测定，采用Hot Disk热物性分析仪和铂金板法分别测定了石墨烯纳米制冷剂的热导率和表面张力，采用接触角测量仪和扫描电子显微镜（SEM）观测了沸腾后加热表面的润湿性和形貌特征。实验中，单层石墨烯纳米片的质量百分含量（ω）为0.02%~0.50%，实验压力为一个标准大气压，热流密度为20~200 kW/m2。实验结果表明：单层石墨烯纳米片的加入，使制冷剂R141b的核态池沸腾换热得到强化；当ω=0.2%时，换热系数提高比例出现峰值，为57.7%。伴随ω的增加，石墨烯纳米制冷剂的热导率增大、表面张力减小，沸腾表面润湿性增强且微腔数先增后减，综合作用的结果导致存在一个最佳的单层石墨烯纳米片浓度（即ω=0.2%）使换热系数最高。     

基于全相位预处理的时域多重信号分类波达方向估计方法

低信噪比下,针对宽带短脉冲情况下频域多重信号分类(MUSIC)中噪声子空间估计不稳定问题,提出一种基于全相位预处理的时域多重信号分类波达方向(DOA)估计方法。①对线列阵接收数据进行分组处理;②按搜索角度对各组数据进行相移预处理,并对各组数据预处理结果进行相加,得到一组新数据;③对线列阵接收数据在时域构建相移后的协方差矩阵,在更短数据长度下,稳定实现噪声子空间估计,并依据估计出的噪声子空间含有的正交特性,通过单位矩阵加法器得到相应空间谱估计值,实现波达方向估计。数值仿真和实测数据处理结果表明,相比频域MUSIC方法,该方法有效提高了线列阵接收数据协方差矩阵中信号含有量和信噪比,能够在更短数据长度情况下实现对噪声子空间的稳定估计,具有较好的稳定性和检测性能,提高了MUSIC方法在实际波达方向估计中的鲁棒性。     

带权决策表的属性约简

属性约简是粗糙集理论的重要应用。考虑将决策表中的每行都作为一条决策规则时，若把表中出现相同决策规则的次数作为权，可得到带权决策表。提出了关于带权决策表的正域约简相应的辨识矩阵并给出了证明，从而得到了约简算法。相比于决策表中的正域约简时发现，通过将决策表转化为带权决策表后，再利用算法1进行约简时，其在一定程度上优于前者。提出了近似分类精度约简相应的辨识矩阵并给出了证明。对于2个算法，在选取的UCI数据集上进行了实验验证。通过实验进一步说明了所提出算法的可行性和有效性。     

基于改进的Random Subspace的客户投诉分类方法

电信业的客户投诉不断增多而又亟待高效处理。针对电信客户投诉数据的特点，提出了一种面向高维数据的改进的集成学习分类方法。该方法综合考虑客户投诉中的文本信息及客户通讯状态信息，基于Random Subspace方法，以支持向量机（Support Vector Machine，SVM）为基分类器，采用证据推理（Evidential Reasoning，ER）规则为一种新的集成策略，构造分类模型对电信客户投诉进行分类。所提模型和方法在某电信公司客户投诉数据上进行了验证，实验结果显示该方法能够显著提高客户投诉分类的准确率和投诉处理效率。