基于改进MobileFaceNet的人脸识别方法北大核心

针对目前大多数人脸识别算法参数多、计算量大,难以部署到移动端和嵌入式设备中的问题,提出了一种基于改进MobileFaceNet的人脸识别方法。通过对MobileFaceNet模型结构的调整,将bottleneck模块优化为sandglass模块,改良深度卷积和逐点卷积的相对位置,适当增大sandglass模块的输出通道数,从而减少特征压缩时的信息丢失,增强人脸空间特征的提取。实验结果表明:改进后的方法在LFW测试数据集上准确率达99.15%,模型大小和计算量分别仅为原算法的61%和45%,验证了所提方法的有效性。     

Mutual information maximizing GAN inversion for real face with identity preservation

Recent generative adversarial networks (GANs) have yielded remarkable performance in face image synthesis. GAN inversion embeds an image into the latent space of a pretrained generator, enabling it to be used for real face manipulation. However, current inversion approaches for real faces suffer the dilemma of initialization collapse and identity loss. In this paper, we propose a hierarchical GAN inversion for real faces with identity preservation based on mutual information maximization. We first use a facial domain guaranteed initialization to avoid the initialization collapse. Furthermore, we prove that maximizing the mutual information between inverted faces and their identities is equivalent to minimizing the distance between identity features from inverted and original faces. Optimization for real face inversion with identity preservation is implemented on this mutual information-maximizing constraint. Extensive experimental results show that our approach outperforms state-of-the-art solutions for inverting and editing real faces, particularly in terms of face identity preservation.     

Multi-scale and multi-patch transformer for sandstorm image enhancement

Sandstorm is a meteorological phenomenon common in arid and semi-arid regions. A sandstorm can carry large volumes of sand unexpectedly, which leads to severe color deviations and significantly degraded visibility when an image is taken in such a scenario. However, existing image enhancement methods cannot enhance sandstorm images well due to the challenging degradations and the scarcity of sandstorm training data. In this paper, we propose a Transformer with rotary position embedding to perform sandstorm image enhancement via building multi-scale and multi-patch dependencies. Our key insights in this work are 1) a multi-scale Transformer can globally eliminate the color deviations of sandstorm images via aggregating global information, 2) a multi-patch Transformer can recover local details well via learning the spatial variant degradations, and 3) a U-shape Transformer with rotary position embedding as the core unit of multi-scale and multi-patch Transformer can effectively build the long-range dependencies. We also contribute a real-world Sandstorm Image Enhancement (SIE) dataset including 1,400 sandstorm images with different degrees of degradations and various scenes. Experiments performed on synthetic images and real-world sandstorm images demonstrate that our proposed method not only obtains visually pleasing results but also outperforms state-of-the-art methods qualitatively and quantitatively.     

碳中和背景下配电网差异化节能降耗潜力评估分

为了提高配电网差异化节能降耗效果，解决现有潜力评估方法存在的应用性能差的问题，提出碳中和背景下配电网差异化节能降耗潜力优化评估方法。根据配电网的空间结构，构建相应的等值电路模型。在该模型下，从设备损耗和运行附加损耗2个方面计算配电网的损耗量。根据损耗量计算结果，确定配电网差异化碳中和节能降耗方式。从静态和动态2个角度设置潜力评估指标，通过指标数据处理、指标权重求解等步骤，得出配电网差异化节能降耗潜力的综合量化评估结果。将设计潜力评估方法应用到配电网的差异化节能降耗改造工作中，能够有效降低配电网的实际线损量、降低区域损耗费用，并具有较高的应用价值。     

Structural regulation and polishing performance of dendritic mesoporous silica (D-mSiO2) supported with samarium-doped cerium oxide composites

Ceria (CeO₂) particles are prevalent polishing abrasive materials. Trivalent lanthanide ions are the popular category of dopants for enriched surface defects and thus improved physicochemical properties, since they are highly compatible with CeO₂ lattices. Herein, a series of dendritic-like mesoporous silica (D-mSiO₂)-supported samarium (Sm)-doped CeO₂ nanocrystals were synthesized via a facile chemical precipitation method. The relation of the structural characteristics and chemical mechanical polishing (CMP) performances were investigated to explore the effect of Sm-doping amounts on the D-mSiO₂/Sm_xCe_1?xO_2?δ (x = 0–1) composite abrasives. The involved low-modulus D-mSiO₂ cores aimed to eliminate surface scratch and damage, resulting from the optimized contact behavior between abrasives and surfaces. The trivalent cerium (Ce³⁺) and oxygen vacancy (V_O) at CeO₂ surfaces were expected to be reactive sites for the material removal process over SiO₂ films. The optimal oxide-CMP performances in terms of removal efficiency and surface quality were achieved by the 40% Sm-doped composite abrasives. It might be attributed to the high Ce³⁺ and V_O concentrations and the enhancement of tribochemical reactivity between CeO₂SiO₂ interfaces. Furthermore, the relationship between the surface chemistry, polishing performance as well as the actual role in oxide-CMP of the D-mSiO₂/Sm_xCe_1?xO_2?δ abrasives were also discussed.     

Numerical study on laminar burning velocity of ammonia flame with methanol addition

The combustion characteristics of ammonia/methanol mixtures were investigated numerically in this study. Methanol has a dramatic promotive effect on the laminar burning velocity (LBV) of ammonia. Three mechanisms from literature and another four self-developed mechanisms constructed in this study were evaluated using the measured laminar burning velocities of ammonia/methanol mixtures from Wang et al. (Combust.Flame. 2021). Generally, none of the selected mechanisms can precisely predict the measured laminar burning velocities at all conditions. Aiming to develop a simplified and reliable mechanism for ammonia/methanol mixtures, the constructed mechanism utilized NUI Galway mechanism (Combust.Flame. 2016) as methanol sub-mechanism and the Otomo mechanism (Int. J. Hydrogen. Energy. 2018) as ammonia sub-mechanism was optimized and reduced. The reduced mechanism entitled ‘DNO-NH3’, can accurately reproduce the measured laminar burning velocities of ammonia/methanol mixtures under all conditions. A reaction path analysis of the ammonia/methanol mixtures based on the DNO-NH3 mechanism shows that methanol is not directly involved in ammonia oxidation, instead, the produced methyl radicals from methanol oxidization contribute to the dehydrogenation of ammonia. Besides, NO_x emission analysis demonstrates that 60% methanol addition results in the highest NO_x emissions. The most important reactions dominating the NO_x consumption and production are identified in this study.     

疫情下电磁场理论课程的网络教学

本文主要总结了新冠疫情期间作者的电磁场理论课程在线教学经验。对比分析了录播和直播的优缺点后,选择录播教学方式。基于超星网络教学平台,展示了录播网络教学的具体措施,包括网上答疑和学习效果检查以及在线批改作业等。给出了网络教学可以为线下教学继续使用的方法和手段,为疫情结束后的正常教学提供了新的网络教学补充措施。     

5G核心网业务模型的智能化预测研究

摘 要：核心网业务模型的建立是5G网络容量规划和网络建设的基础，通过现有方法得到的理论业务模型是静态不可变的且与实际网络存在偏离。为了克服现有5G核心网业务模型与现网模型适配性较差以及规划设备无法满足用户实际业务需求的问题，提出了一种长短期记忆（long short-term memory，LSTM）网络与卷积LSTM （convolution LSTM，ConvLSTM）网络双通道融合的 5G 核心网业务模型预测方法。该方法基于人工智能（artificial intelligence，AI）技术以实现高质量的核心网业务模型的智能预测，形成数据反馈闭环，实现网络自优化调整，助力网络智能化建设。     

价值网络语境下企业设计价值创新路径研究

目的 在新的视角下，提出一种价值创新路径模型，对设计驱动型企业的价值实现方式进行有效补充。方法 通过文献研究，论述了企业关系由价值链到价值网转变的历史趋势;通过比较研究，对价值实现过程中主客体之间的4种关系进行了类比分析，提出了不同主客体关系状态下设计实践的具体表现方式。结果 在价值网络语境下，以价值论为理论依据，从主客体之间的关系入手，构建设计价值创新路径模型。结论 通过分析证明:“主客体统一状态”是企业价值创造的理想状态;主客体关系一直处于动态的变化过程中;客体对主体的“意义”也一直处于动态变化中。在这种动态变化的关系网络中，设计驱动方式已经成为企业创新的重要模式，企业如何捕获并诠释“意义”，成为价值实现的关键所在。