On measuring network robustness for weighted networks

Knowledge and Information Systems - Network robustness measures how well network structure is strong and healthy when it is under attack, such as vertices joining and leaving. It has been widely...     

Saliency guided self-attention network for pedestrian attribute recognition in surveillance scenarios

Pedestrian attribute recognition is often considered as a multi-label image classification task. In order to make full use of attribute-related location information, a saliency guided sel-attention network ( SGSA-Net) was proposed to weakly supervise attribute localization, without annotations of attribute-related regions. Saliency priors were integrated into the spatial attention module ( SAM ). Meanwhile,channel-wise attention and spatial attention were introduced into the network. Moreover, a weighted binary cross-entropy loss ( WCEL) function was employed to handle the imbalance of training data. Extensive experiments on richly annotated pedestrian ( RAP) and pedestrian attribute ( PETA) datasets demonstrated that SGSA-Net outperformed other state-of-the-art methods.     

双脉冲激光诱导铝等离子体的双波长干涉诊断

双脉冲激光诱导等离子体在激光加工、元素检测、材料去除等领域有广阔的应用前景和发展空间,对其进行诊断具有重要意义。针对延迟双脉冲激光诱导铝等离子体的作用效果和影响机理,采用双波长干涉法对其时间演化规律展开研究。基于马赫-曾德尔干涉仪搭建了双波长干涉诊断系统,得到了双脉冲激光诱导等离子体干涉图。通过对干涉图的处理和分析,得到了等离子体电子密度随双脉冲激光延迟时间的变化规律。结果表明,随着双脉冲激光延迟时间的增加,第二束脉冲激光对等离子体电子密度的增强效果先加强后减弱。其中,双脉冲激光延迟时间为10 ns时,对等离子体电子密度的增强效果最强,在30 ns时刻,其中心区域平均电子密度可达6.49×1019 cm?3,相较于同等能量单脉冲激光诱导等离子体提升了26%。同时研究了延迟时间对第二束脉冲激光作用机制的影响。研究结果为双脉冲激光诱导等离子体的优化方向提供了参考。     

In Situ Management of Ions Migration to Control Hysteresis Effect for Planar Heterojunction Perovskite Solar Cells

As one of the most promising photovoltaic materials, the efficiency of inorganic–organic hybrid halide perovskite solar cells (PSCs) has reached 25.5% in 2020. However, the stability and hysteresis remain primary challenges before it can become a commercial photovoltaic technology. Therefore, those issues have drawn significant attention for photovoltaic applications. In this work, a study of the PSCs hysteresis improvement is presented based on a combination of first-principles simulations, scanning electron microscopy images, and time-dependent photocurrent measurements. It indicates the hysteresis led by the ion migration and accumulation is mainly localized at the two interfaces: one is between electron transport layer and active layer, and the other is between active layer and hole transport layer. Considering the massive defects at the grain boundaries (GBs), they lower the potential barriers significantly. The defect density at GBs is therefore reduced via the in situ passivation of PbI₂ crystals. The hysteresis index is decreased from 22.43% down to 1.04%, and results in an improvement in efficiency from 17.12% up to 20.10%. Following the understanding of defect-induced hysteresis, an approach to improve the hysteresis is provided, which can be integrated into the fabrication process and widely applied to enhance the performance of PSCs.     

n型单晶硅片的电阻率和扩散方阻对IBC太阳电池电性能的影响

叉指背接触式(IBC)太阳电池因正面没有金属栅线遮挡,具有较高的短路电流,且组件外观更加美观。但由于IBC太阳电池正负电极在背面交叉式分布,在制备过程中需要采用光刻掩模技术进行隔离,难以实现大规模生产。采用Quokka软件仿真模拟了电阻率和扩散方阻对n型IBC太阳电池效率的影响,并对不同电阻率和扩散方阻的电池片进行了实验验证,从n型单晶硅片电阻率的选择和扩散工艺优化方面为IBC太阳电池的规模化生产提供了理论基础。实验结果表明,电阻率为3~5Ω·cm、扩散方阻为70Ω/时,小批量生产的IBC太阳电池平均光电转换效率可达23.73%,开路电压为693 mV,短路电流密度为42.44 mA/cm²,填充因子为80.69%。     

智能牵引变电所网络化自投功能研究与应用

针对电气化铁路智能牵引变电所发生进线电源失压或牵引变压器故障,备用进线电源与备用牵引变压器自动投入问题,提出了基于GOOSE与SMV的网络化自投方案。将自投程序固化至2台既有主变测控装置,装置与装置之间、装置与智能组件之间通过GOOSE信号传递闭锁信号、开关分合闸信号或开关位置信号;合并单元通过SMV向装置传递电压、电流等模拟量。运行牵引变压器侧的主变测控装置作为自投的主用装置,另一台主变测控装置作为自投备用装置,确保自投功能顺利实现。现场试验结果验证了方法的有效性与正确性。     

高性能罗兰-C接收天线设计

为了提高罗兰-C磁天线的接收能力,设计了一种顺接串联多层线圈的磁天线。理论推导了顺接串联多层线圈磁天线的信噪比和灵敏度,最终设计的磁天线大小为150 mm×150 mm。远场测试接收到了较高信噪比的罗兰-C信号,在相同信号调理电路条件下,与单层单组线圈的磁天线相比,顺接串联多层线圈的磁天线接收到3.47 V的电压信号,且信噪比高达30.87 dB,灵敏度和信噪比分别提高了82.42%和63.20%。顺接串联多层线圈磁天线提高了罗兰-C磁天线的灵敏度和信噪比,对罗兰-C系统在定位方面的应用具有重要意义。     

Occluded Video Instance Segmentation: A Benchmark

International Journal of Computer Vision - Can our video understanding systems perceive objects when a heavy occlusion exists in a scene? To answer this question, we collect a large-scale dataset...     

Social-path embedding-based transformer for graduation development prediction

As the education of students attracts more and more attention, the task of graduation development prediction has gradually become a hot topic in academia and industry. The task of graduation development prediction aims to predict the employment category of students in advance via academic achievement data, which can help administrators understand students’ learning status and set up a reasonable learning plan. However, existing research ignores the potential impact of social relationships on students’ graduation development choices. To fully explore social relationships among students, we propose a Social-path Embedding-based Transformer Neural Network (SPE-TNN) for the task of graduation development prediction in this paper. Specifically, SPE-TNN is divided into the Social-path selection layer, the Social-path embedding layer, the Transformer layer, and the Multi-layer projection layer. Firstly, the Social-path selection layer is designed to find social relationships that impact graduation development and embed them into the student’s performance features through the Social-path embedding layer. Secondly, the Transformer layer is adopted to balance the weights of the students’ features. Finally, the Multi-layer projection layer is used to achieve the student graduation development prediction. Experimental results on the real-world datasets show that SPE-TNN outperforms the existing popular approaches.

     

A novel approach of data race detection based on CNN-BiLSTM hybrid neural network

Neural Computing and Applications - Existing data race detection approaches based on deep learning are suffering from the problems of unique feature extraction and low accuracy. To this end, this...