计及三相短路故障的MMC光伏并网系统电压补偿方法

模块化多电平变流器(mudular multilevel converter,MMC)在主电路结构中可以有效提高光伏并网系统的光能利用率。提出了一种光伏电站直流并网方案,通过大功率高变比的有源箝位Boost全桥隔离变换器串联升压,然后经MMC实现光伏高压直流并网。针对光伏并网后交流侧电压的稳定性,提出了基于电压前馈的双闭环控制策略。在MATLAB/Simulink中搭建串联型光伏电站经MMC并网模型,并应用基于电压前馈的双闭环控制策略,验证所提拓扑方案的正确性以及控制策略的可行性。结果表明,控制策略可以在发生三相短路故障时及时补偿电压,提高交流系统侧的可靠性。     

污泥干燥及其与燃煤电站耦合的热力特性分析

如何对污泥进行低成本的无害化处理及资源化利用已经成为亟待解决的问题。首先基于传热传质的原理建立了污泥干燥过程的热力模型;其次提出了污泥干燥过程的综合评价指标,从热力学第二定律的角度对干燥过程所耗热量和功量进行综合评价;最后对污泥干燥过程与燃煤电站耦合的系统进行热力学分析。结果显示:与发电系统耦合时,在满足换热温差的前提下,风泥比为3.32时的干燥综合能耗为最低;相比案例机组,耦合系统的供电量和供电效率有较大的提升;此外敏感性分析发现,干燥机压损比例的增加会使干燥综合能耗有不同程度的增加;考虑因掺烧污泥导致锅炉效率降低时的供电效率有一定的下降。     

Automated sampling decision scheme for the AVM system

Reducing the sampling rate to as low as possible is a high priority for many factories to reduce production cost. Automatic virtual metrology based intelligent sampling decision (ISD) scheme had been previously developed for reducing the sampling rate and sustaining the virtual metrology (VM) accuracy. However, the desired sampling rate of the ISD scheme is fixed and set manually. Hence, whenever the VM accuracy gets worse, it cannot adaptively increase the default sampling rate in the ISD scheme. As a consequence, it would take more time to collect enough samples for improving the VM accuracy. Moreover, when the VM accuracy performs well all the time, it cannot automatically decrease the default sampling rate in ISD, which may result in unnecessary waste. Accordingly, this paper proposes an automated sampling decision (ASD) scheme to adaptively and automatically modify the sampling rate online and in real time for continuous improvement. The ASD scheme can monitor the VM accuracy online as well as update the VM models in real time for maintaining the VM accuracy when the VM accuracy becomes poor. Also, the ASD scheme can automatically reduce the sampling rate while the VM accuracy performs well.     

高介电常数、低介电损耗聚合物复合电介质材料研究进展

高介电常数聚合物电介质材料作为当今信息功能材料的研究热点,具有实际的应用价值和前景。综述了聚合物基复合电介质材料的分类及优缺点,以及从材料微观结构设计和填料界面修饰出发(如三元杂化或设计核壳和三明治结构),来获得高介电常数、低介电损耗聚合物复合电介质材料的研究状况和应用前景,以期对高介电、低损耗聚合物基电介质材料有一个更直观全面的了解,进一步拓展该类材料在电气和生物工程领域的研究和应用。     

Synthesis of Pt nanocrystals with different shapes using the same protocol to optimize their catalytic activity toward oxygen reduction

Engineering the shape and thus surface structure of Pt nanocrystals is an effective strategy for optimizing their catalytic activities toward various reactions. However, different protocols are typically used to produce Pt nanocrystals with distinctive shapes, making it difficult to directly compare their catalytic activities owing to the complication of surface contamination. Here we demonstrate that Pt nanocrystals with a variety of shapes, including those enclosed with low- or high-index facets, can be synthesized using the same protocol by simply adjusting the concentration of reducing agent and/or the reaction time. Specifically, when the reducing agent was used at a relatively low concentration, Pt truncated cubes, cuboctahedrons, truncated octahedrons, and octahedrons were produced sequentially upon the increase in reaction time. When 67% more reducing agent was used, Pt cubes and concave cubes were obtained consecutively as the reaction time was prolonged. Our quantitative analysis suggests that the diversity of shape and difference in size can be resulted from the difference in reduction kinetics. In evaluating their structure–activity relationship for oxygen reduction, it was established that the high-index facets on Pt concave cubes possessed a specific activity of 6.3 and 1.3 times greater than those of Pt cubes and octahedrons exposed by {1?0?0} and {1?1?1} facets, respectively. This work not only offers a general method for the synthesis of Pt nanocrystals having diverse shapes and thus different types of facets but also highlights the significance of reduction kinetics in controlling the structure evolution of other metal nanocrystals.     

Automatic text summarization based on latent semantic indexing

Automatic summarization is a topic of common concern in computational linguistics and information science, since a computer system of text summarization is considered to be an effective means of processing information resources. A method of text summarization based on latent semantic indexing (LSI), which uses semantic indexing to calculate the sentence similarity, is proposed in this article. It improves the accuracy of sentence similarity calculations and subject delineation, and helps the abstracts generated to cover the documents comprehensively as well as reducing redundancies. The effectiveness of the method is proved by the experimental results. Compared with the traditional keyword-based vector space model method of automatic text summarization, the quality of the abstracts generated was significantly improved.     

Geostatistical downscaling of AMSR2 precipitation with COMS infrared observations

This article presents a geostatistical approach for downscaling precipitation products from passive microwave satellites with geostationary Meteorological Satellite observations. More precisely, the Advanced Microwave Scanning Radiometer 2 (AMSR2) precipitation (daily level 3 product) with 0.25° spatial resolution and the Communication, Ocean and Meteorological Satellite (COMS) infrared (IR) data with 5 km spatial resolution were used for the downscaling experiment over the Korean peninsula. Brightness temperature data observed at COMS IR 1 and water vapour channels were incorporated for downscaling via area-to-point residual Kriging with non-linear regression. The evaluation results with densely sampled Automatic Weather Station data revealed that incorporating the COMS IR observations with the AMSR2 precipitation showed similar error statistics to those of the AMSR2 precipitation because of the limitations of IR observations themselves and the inherent errors of the AMSR2 precipitation product over land. However, the area-based evaluation using information entropy indicated that incorporating the COMS observations resulted in more detailed spatial variation in the final product than direct downscaling of the AMSR2 precipitation. In addition, local precipitation patterns could be captured when there were regions with missing precipitation values in the AMSR2 product. Consequently, the downscaling result is useful for understanding the local precipitation patterns with an accuracy similar to that provided by microwave satellite observations. It is also suggested that the spatial variability in the downscaling result and errors in input low-resolution data should be considered when downscaling coarse resolution data using fine resolution auxiliary variables.     

目标提取与哈希机制的多标签图像检索

目的 哈希是大规模图像检索的有效方法。为提高检索精度,哈希码应保留语义信息。图像之间越相似,其哈希码也应越接近。现有方法首先提取描述图像整体的特征,然后生成哈希码。这种方法不能精确地描述图像包含的多个目标,限制了多标签图像检索的精度。为此提出一种基于卷积神经网络和目标提取的哈希生成方法。方法 首先提取图像中可能包含目标的一系列区域,然后用深度卷积神经网络提取每个区域的特征并进行融合,通过生成一组特征来刻画图像中的每个目标,最后再产生整幅图像的哈希码。采用Triplet Loss的训练方法,使得哈希码尽可能保留语义信息。结果 在VOC2012、Flickr25K和NUSWIDE数据集上进行多标签图像检索。在NDCG(normalized discounted cumulative gain)性能指标上,当返回图像数量为 1 000时,对于VOC2012,本文方法相对于DSRH(deep semantic ranking hashing)方法提高24个百分点,相对于ITQ-CCA(iterative quantization-canonical correlation analysis)方法能提高36个百分点;对于Flickr25,本文方法比DSRH方法能提高2个左右的百分点;对于NUSWIDE,本文方法相对于DSRH方法能提高4个左右的百分点。对于平均检索准确度,本文方法在NUSWIDE和Flickr25上能提高25个百分点。根据多项评价指标可以看出,本文方法能以更细粒度来精确地描述图像,显著提高了多标签图像检索的性能。结论 本文新的特征学习模型,对图像进行细粒度特征编码是一种可行的方法,能够有效提高数据集的检索性能。     

Maxi-min margin machine: learning large margin classifiers locally and globally.

In this paper, we propose a novel large margin classifier, called the maxi-min margin machine M(4). This model learns the decision boundary both locally and globally. In comparison, other large margin classifiers construct separating hyperplanes only either locally or globally. For example, a state-of-the-art large margin classifier, the support vector machine (SVM), considers data only locally, while another significant model, the minimax probability machine (MPM), focuses on building the decision hyperplane exclusively based on the global information. As a major contribution, we show that SVM yields the same solution as M(4) when data satisfy certain conditions, and MPM can be regarded as a relaxation model of M(4). Moreover, based on our proposed local and global view of data, another popular model, the linear discriminant analysis, can easily be interpreted and extended as well. We describe the M(4) model definition, provide a geometrical interpretation, present theoretical justifications, and propose a practical sequential conic programming method to solve the optimization problem. We also show how to exploit Mercer kernels to extend M(4) for nonlinear classifications. Furthermore, we perform a series of evaluations on both synthetic data sets and real-world benchmark data sets. Comparison with SVM and MPM demonstrates the advantages of our new model.