Spectral radiance reconstruction from trichromatic camera responses based on orthogonal test and regularized algorithm

Reconstruction of spectral information based on multi‐channel image system is a significant problem in color reproduction, detection, and recognition. A spectral radiance reconstruction from trichromatic digital camera responses is researched in this article. The mapping relationship between the trichromatic imaging system response and the incident spectral radiance is analyzed. Then, in order to remove the ill‐posedness of the problem, a regularized constraint solution model of spectral radiance reconstruction matrix is established. And the spectral radiance can be reconstructed by spectral radiance reconstruction matrices and trichromatic imaging system response. Finally, the spectral radiance reconstruction matrix is estimated by the system radiometric calibration experiment. The input radiance is offered by a LCD display. A 3‐factor and 9‐level orthogonal test is designed for the calibration experiment, and a test set of 24 colors is used for precision analysis. The results show that the average relative mean error of our method is 8.69%, it is lower than that of Wiener filtering method by 2.84%. The method can reconstruct spectral radiance information effectively.     

一起发电机定子接地故障的分析与处理

文章介绍了海外某大型火力发电厂发生的一起发电机定子接地故障的分析和处理过程,对发电厂类似故障的处理和海外发电厂的运行和维护提供了参考。     

Transmission line detection in aerial images: An instance segmentation approach based on multitask neural networks

Camera-based transmission line detection (TLD) is a fundamental and crucial task for automatically patrolling powerlines by aircraft. Motivated by instance segmentation, a TLD algorithm is proposed in this paper with a novel deep neural network, i.e., CableNet. The network structure is designed based on fully convolutional networks (FCNs) with two major improvements, considering the specific appearance characteristics of transmission lines. First, overlaying dilated convolutional layers and spatial convolutional layers are configured to better represent continuous long and thin cable shapes. Second, two branches of outputs are arranged to generate multidimensional feature maps for instance segmentation. Thus, cable pixels can be detected and assigned cable IDs simultaneously. Multiple experiments are conducted on aerial images, and the results show that the proposed algorithm obtains reliable detection performance and is superior to traditional TLD methods. Meanwhile, segmented pixels can be accurately identified as cable instances, contributing to line fitting for further applications.     

Reconstructed Water Oxidation Electrocatalysts: The Impact of Surface Dynamics on Intrinsic Activities

Electroreduction of small molecules such as H₂O, CO₂, and N₂ for producing clean fuels or valuable chemicals provides a sustainable approach to meet the increasing global energy demands and to alleviate the concern on climate change resulting from fossil fuel consumption. On the path to implement this purpose, however, several scientific hurdles remain, one of which is the low energy efficiency due to the sluggish kinetics of the paired oxygen evolution reaction (OER). In response, it is highly desirable to synthesize high-performance and cost-effective OER electrocatalysts. Recent advances have witnessed surface reconstruction engineering as a salient tool to significantly improve the catalytic performance of OER electrocatalysts. In this review, recent progress on the reconstructed OER electrocatalysts and future opportunities are discussed. A brief introduction of the fundamentals of OER and the experimental approaches for generating and characterizing the reconstructed active sites in OER nanocatalysts are given first, followed by an expanded discussion of recent advances on the reconstructed OER electrocatalysts with improved activities, with a particular emphasis on understanding the correlation between surface dynamics and activities. Finally, a prospect for clean future energy communities harnessing surface reconstruction-promoted electrochemical water oxidation will be provided.     

超宽带大功率合成器设计北大核心CSCD

在80 MHz~1 GHz频段,单个功率管输出功率能达到100 W以上,为研制输出功率400 W的功率放大器,文中设计了四路功率合成器。该合成器需要实现功率容量大、工作频带宽、体积小的设计目标。在功率容量方面,文中采用悬置带状线结构,其功率容量远远大于微带线结构;在工作频带方面,采用切比雪夫九节阻抗变换器,将工作带宽拓宽为80 MHz~1 GHz;在体积方面,文中合成器的功率合成部分采用Y型节级联实现四路功率合成,阻抗变换部分采用切比雪夫阻抗变换器进行阻抗变换,该结构相较于磁环巴伦功率合成器,不但具有损耗小、平坦度高的优点,而且通过将阻抗变换器设计成曲折的形状,进一步缩小了合成器体积。仿真与实测结果显示该合成器在80 MHz~1 GHz范围内还具有较高的平坦度,合成效率可达90%以上。     

基于改进残差特征蒸馏的轻量级超分辨率网络

基于深度学习的图像超分辨率算法通常采用递归的方式或参数共享的策略来减少网络参数，这将增加网络的深度，使得运行网络花费大量的时间，从而很难将模型部署到现实生活中。为了解决上述问题，本文设计一种轻量级超分辨率网络，对中间特征的关联性及重要性进行学习，且在重建部分结合高分辨率图像的特征信息。首先，引入层间注意力模块，通过考虑层与层之间的相关性，自适应地分配重要层次特征的权重。其次，使用增强重建模块提取高分辨率图像中更精细的特征信息，以此得到更加清晰的重建图片。通过大量的对比实验表明，本文设计的网络与其他轻量级模型相比，有更小的网络参数量，并且在重建精度和视觉效果上都有一定的提升。     

Effect of support and reduction temperature in the hydrogenation of CO2 over the Cu–Pd bimetallic catalyst with high Cu/Pd ratio

Metal-support interaction and catalyst pretreatment are important for industrial catalysis. This work investigated the effect of supports (SiO₂, CeO₂, TiO₂ and ZrO₂) for Cu–Pd catalyst with high Cu/Pd ratio (Cu/Pd = 33.5) regarding catalyst cost, and the reduction temperatures of 350 °C and 550 °C were compared. The activity based on catalyst weight follows the order of Si > Ce > Zr > Ti when reduced at 350 °C. The reduction temperature leads to the surface reconstruction over the SiO₂, CeO₂ and TiO₂ catalysts, while results in phase transition over Cu–Pd/ZrO₂. The effect of reduction temperature on catalytic performance is prominent for the SiO₂ and ZrO₂ supported catalysts but not for the CeO₂ and TiO₂ ones. Among the investigated catalysts, Zr-350 exhibits the highest methanol yield. This work reveals the importance of the supports and pretreatment conditions on the physical-chemical properties and the catalytic performance of the Cu–Pd bimetallic catalysts.     

基于纸浆纤维的贵重物品内包装塑造系统研究

为解决贵重物品与其内包装“契合度”不高的问题，提出基于纸浆纤维的贵重物品内包装塑造系统。系统通过三维扫描获取待包装物品的点云信息，经主控计算机进行数据处理，以下位机为执行中枢，辅之其他外部设备，利用纸浆纤维为贵重物品塑造“量身定制”的内包装，提升内包装契合度，增强保护效果，绿色环保，安全稳定。     

基于VMD和BSA-KELM的高陡边坡位移预测模型研究

边坡位移的时间序列曲线存在复杂的非线性特性,传统的预测模型精度不足以满足预测要求。为此提出了基于变分模态分解的鸟群优化-核极限学习机的预测模型,并用于河北省某水泥厂的边坡位移预测。该方法首先采用VMD把边坡位移序列分解为一系列的有限带宽的子序列,再对各子序列分别采用相空间重构并用核极限学习机预测,采用鸟群算法优化相空间重构的嵌入维度和KELM中惩罚系数和核参数三个数值,以取得最优预测模型。最后将各个子序列预测值叠加,得到边坡位移的最终预测值。结果表明：和KELM、BSA-KELM、EEMD-BSA-KELM模型相比,基于VMD的BSA-KELM预测精度更高,为边坡位移的预测提供一种有效的方法。     

基于RGB颜色信息聚类的光谱反射率重建

针对光谱反射率研究中因训练样本数据量大造成的冗杂等问题，提出了一种基于RGB信息进行聚类的样本分类方法。首先对颜色进行聚类并确定聚类个数，后对每一类光谱反射率使用BP神经网络分别进行重建。对于实验结果，使用ΔE₀₀、均方根误差（RMSE）以及适应度系数等标准进行评价，同时与主成分分析算法进行对比。从实验分析可得出，在聚类数目为7时光谱反射率重建效果最好，这时的平均CIE2000的色差为0.836，平均RMSE为0.0149，平均适应度系数为99.82%，并且，在最后对重建色差较大的色块进行了优化处理。实验证明，颜色聚类方法可以很好的应用于光谱反射率重建。