基于VMD和改进TCN的短期光伏发电功率预测

光伏发电功率存在波动性,且光伏出力易受各种气象特征影响,传统TCN网络容易过度强化空间特性而弱化个体特性。针对上述问题,文中提出一种基于VMD和改进TCN的短期光伏发电功率预测模型。通过VMD将原始光伏发电功率时间序列分解为若干不同频率的模态分量,将各个模态分量以及相对应的气象数据输入至改进TCN网络进行建模学习。利用中心频率法确定VMD的最优分解模态分解个数。在传统TCN预测模型的基础上,使用DropBlock正则化取代Dropout正则化以达到抑制卷积层中信息协同的效果,并引入注意力机制自主挖掘并突出关键气象输入特征的影响,量化各气象因素对光伏发电的影响,从而提高预测精度。以江苏省某光伏电站真实数据为例进行仿真实验,结果表明所提预测方法的RMSE为0.62 MW,MAPE为2.03%。     

两种混合单元混合机理的数值模拟

为了研究SV型和SX型混合单元在全静态乳化器的混合机理,使用群体平衡模型对乳化器内部流场进行模拟分析。结果表明,SV型与SX型混合单元的混合机理是油相与水相材料通过混合单元时其空间流动方向改变,在流场内形成大量的漩涡,从而达到乳化的目的;通过比较流体在经过SV型和SX型混合单元后的水相索特平均直径(d_(32)),得出SV型混合单元的乳化效果更强,但同时消耗更多的能量,压降更大。     

基于多通道的ICU脑血管疾病死亡风险预测模型

死亡风险预测指根据病人临床体征监测数据来预测未来一段时间的死亡风险。对于ICU病患，通过死亡风险预测可以有针对性地对病人做出临床诊断，以及合理安排有限的医疗资源。基于临床使用的MEWS和Glasgow昏迷评分量表，针对ICU病人临床监测的17项生理参数，提出一种基于多通道的ICU脑血管疾病死亡风险预测模型。引入多通道概念应用于BiLSTM模型，用于突出每个生理参数对死亡风险预测的作用。采用Attention机制用于提高模型预测精度。实验数据来自MIMIC [Ⅲ]数据库，从中提取3?080位脑血管疾病患者的16?260条记录用于此次研究，除了六组超参数实验之外，将所提模型与LSTM、Multichannel-BiLSTM、逻辑回归（logistic regression）和支持向量机（support vector machine, SVM）四种模型进行了对比分析，准确率Accuracy、灵敏度Sensitive、特异性Specificity、AUC-ROC和AUC-PRC作为评价指标，实验结果表明，所提模型性能优于其他模型，AUC值达到94.3%。     

减水剂类型对赤泥—矿渣基地聚物注浆材料性能的影响及机理研究

针对赤泥等固体废弃物对环境危害性大且利用率低等问题,以碱激发赤泥-矿渣基地聚物注浆材料为 研究对象,研究了不同掺量的聚羧酸(PA)减水剂、醛酮缩合物(AKC)减水剂和萘系(N)减水剂对材料凝结时间、流动 性及强度等的影响,并通过 XRD、傅里叶红外光谱及 SEM 等设备对减水剂的作用机理进行研究。 结果表明:减水剂增 强了材料的流动性但降低了材料的剪切应力;N 和 PA 减水剂能缩短材料的凝结时间,但 AKC 减水剂会延长材料的凝 结时间;N 和 AKC 减水剂能提高材料的强度,但 PA 减水剂会降低材料的强度;N 减水剂对材料的综合性能提升效果 更加明显,其最优掺量为 0. 7%;减水剂对赤泥-矿渣基地聚物性能提升的作用机理主要是促进地聚合物凝胶的形成。 研究成果为拓展赤泥在工程上的使用途径和效率提供了理论指导。     

Corrosive wear behavior of HVOF-sprayed micro-nano-structured Cr3C2–NiCr cermet coatings under aqueous media

A novel micro-nano-structured Cr₃C₂–NiCr cermet coating was prepared on 316L stainless steel by high-velocity oxygen fuel spraying technology (HVOF). Cermet coatings with different contents of micro-and nano-sized Cr₃C₂ particles as the hard phase and a NiCr alloy matrix as the bonding phase were prepared and characterized in terms of porosity, microhardness, and corrosive wear resistance in a 3.5% NaCl solution and artificial seawater. Compared to nanostructured coatings, micro-nano-structured coatings avoid decarburization and reduce nanoparticle agglomeration during the spray process, and mechanical and electrochemical properties were improved in comparison with those of conventional coatings. The micro-nano-structured Cr₃C₂–NiCr coating rendered low porosity (≤0.34%) and high microhardness (≥1105.0HV_0.3). The coating comprising 50% nano-sized Cr₃C₂ grains exhibited the best corrosive wear resistance owing to its densest microstructure and highest microhardness. Furthermore, compared to static corrosion, the dynamic corrosion of the coatings led to more severe mechanical wear, because corrosion destroyed the coating surface and ions promoted corrosion to invade coatings through the pores during corrosion wear.     

Experimental study on the deformation and failure mechanism of overburden rock during coal mining using a comprehensive intelligent sensing method

Understanding the spatiotemporal evolution of overburden deformation during coal mining is still a challenge in engineering practice due to the limitation of monitoring techniques. Taking the Yangliu Coal Mine as an example, a similarity model test was designed and conducted to investigate the deformation and failure mechanism of overlying rocks in this study. Distributed fiber optic sensing (DFOS), high-density electrical resistivity tomography (HD-ERT) and close-range photogrammetry (CRP) technologies were used in the test for comprehensive analyses. The combined use of the three methods facilitates the investigation of the spatiotemporal evolution characteristics of overburden deformation, showing that the mining-induced deformation of overburden strata was a dynamic evolution process. This process was accompanied by the formation, propagation, closure and redevelopment of separation cracks. Moreover, the key rock stratum with high strength and high-quality lithology played a crucial role in the whole process of overburden deformation. There were generally three failure modes of overburden rock layers, including bending and tension, overall shearing, and shearing and sliding. Shear failure often leads to overburden falling off in blocks, which poses a serious threat to mining safety. Therefore, real-time and accurate monitoring of overburden deformation is of great significance for the safe mining of underground coal seams.     

基于推理注意力机制的二阶段网络图像修复

现有的图像修复方法在处理大面积缺失或高度纹理化的图像时，通常会产生扭曲的结构或与周围区域不一致的模糊纹理，无法重建合理的图像结构。为此，提出了一种基于推理注意力机制的二阶段网络图像修复方法。首先通过边缘生成网络生成合理的幻觉边缘信息，然后在图像补全网络完成图像的重建工作。为了进一步生成视觉效果更逼真的图像，提高图像修复的精确度，在图像补全网络采用推理注意力机制，有效控制了生成特征的不一致性，从而生成更有效的信息。所提方法在多个数据集上进行了实验验证，结果表明该图像修复方法的结构相似性指数达到了88.9%，峰值信噪比达到了25.56 dB，与现有的图像修复方法相比，该方法具有更高的图像修复精确度，生成的图像更逼真。     

Dilated and soft attention-guided convolutional neural network for breast cancer histology images classification

Breast cancer is one of the most common types of cancer in women, and histopathological imaging is considered the gold standard for its diagnosis. However, the great complexity of histopathological images and the considerable workload make this work extremely time-consuming, and the results may be affected by the subjectivity of the pathologist. Therefore, the development of an accurate, automated method for analysis of histopathological images is critical to this field. In this article, we propose a deep learning method guided by the attention mechanism for fast and effective classification of haematoxylin and eosin-stained breast biopsy images. First, this method takes advantage of DenseNet and uses the feature map's information. Second, we introduce dilated convolution to produce a larger receptive field. Finally, spatial attention and channel attention are used to guide the extraction of the most useful visual features. With the use of fivefold cross-validation, the best model obtained an accuracy of 96.47% on the BACH2018 dataset. We also evaluated our method on other datasets, and the experimental results demonstrated that our model has reliable performance. This study indicates that our histopathological image classifier with a soft attention-guided deep learning model for breast cancer shows significantly better results than the latest methods. It has great potential as an effective tool for automatic evaluation of digital histopathological microscopic images for computer-aided diagnosis.     

爆炸排淤填石法中淤泥的本构模型

以爆炸排淤填石法为背景 ,对不同应变率阶段淤泥的本构模型进行了分析 ,利用ANSYS/LS-DYNA动态有限元分析程序对所选择的模型进行验证和确认。计算和分析结果表明 :在形成爆炸空腔的高应变率阶段 ,淤泥表现为理想不可压缩流体的性质 ;在小药量小抵抗线条件下 ,甚至在淤泥自重作用下的较低应变率变形阶段 ,其黏性效应也可以忽略不计。