Development of small-scale unmanned-aerial-vehicle helicopter systems

Ph.DDOCTOR OF PHILOSOPH

Fault Diagnosis for Power Electronics Converters based on Deep Feedforward Network and Wavelet Compression

A fault diagnosis method for power electronics converters based on deep feedforward network and wavelet compression is proposed in this paper. The transient historical data after wavelet compression are used to realize the training of fault diagnosis classifier. Firstly, the correlation analysis of the voltage or current data running in various fault states is performed to remove the redundant features and the sampling point. Secondly, the wavelet transform is used to remove the redundant data of the features, and then the training sample data is greatly compressed. The deep feedforward network is trained by the low frequency component of the features, while the training speed is greatly accelerated. The average accuracy of fault diagnosis classifier can reach over 97%. Finally, the fault diagnosis classifier is tested, and final diagnosis result is determined by multiple-groups transient data, by which the reliability of diagnosis results is improved. The experimental result proves that the classifier has strong generalization ability and can accurately locate the open-circuit faults in IGBTs.Comment: Electric Power Systems Researc

Paying for Knowledge: Why People Paying for Live Broadcasts in Online Knowledge Sharing Community?

Powered by the proliferation of social computing and user-generated content, new knowledge sharing platforms in China, including Q&A communities and live broadcasting, were launched and received widely attentions recently. This research is motivated by the tremendous growth of an online knowledge sharing platform, Zhihu Live (www.zhihu.com/lives). Built upon Zhihu community, the usability and functionality of Zhihu Live makes it easy for user to create their own broadcasting lives that can be shared in the community to a wide range of audiences, making this an attractive platform to content creators (speakers) and knowledge consumers (audiences). We therefore propose a two-phase model to investigate the daily sales of Zhihu lives. Hierarchical Linear model was employed to test our hypotheses. Our preliminary results suggest that number of “like” positively affects daily sales before a live starts (phase 1), whereas “like” number, audience review score, and interactions between speakers and audiences during the broadcasting process have significant effects on live’s daily sales after the live starts (phase 2). Implications are discussed and limitations are noted

Paying for Live Broadcast: Predicting Internet Knowledge Product Sharing

Despite researcher’s attempts on examining knowledge sharing behavior, the impact of purchasing behavior on sales of knowledge products remains largely unknown in the existing literature. To fill this void, using the data collected from Zhihu.com, we develop a two-phase framework to assess the impact of factors of live (i.e., price), factors of other audiences (i.e., review scores) and factors of speaker (i.e., reputation) on sales. Moreover, with start date of a live as a dividing point, our study examines the difference of impact of these factors on sales between two sales stages (before a live start VS. after a live starts). Results and implications are analyzed and discussed

Big Data Analytics in Online Structural Health Monitoring

This manuscript explores the application of big data analytics in online structural health monitoring. As smart sensor technology is making progress and low cost online monitoring is increasingly possible, large quantities of highly heterogeneous data can be acquired during the monitoring, thus exceeding the capacity of traditional data analytics techniques. This paper investigates big data techniques to handle the highvolume data obtained in structural health monitoring. In particular, we investigate the analysis of infrared thermal images for structural damage diagnosis. We explore the MapReduce technique to parallelize the data analytics and efficiently handle the high volume, high velocity and high variety of information. In our study, MapReduce is implemented with the Spark platform, and image processing functions such as uniform filter and Sobel filter are wrapped in the mappers. The methodology is illustrated with concrete slabs, using actual experimental data with induced damag

EgoVM: Achieving Precise Ego-Localization using Lightweight Vectorized Maps

Accurate and reliable ego-localization is critical for autonomous driving. In this paper, we present EgoVM, an end-to-end localization network that achieves comparable localization accuracy to prior state-of-the-art methods, but uses lightweight vectorized maps instead of heavy point-based maps. To begin with, we extract BEV features from online multi-view images and LiDAR point cloud. Then, we employ a set of learnable semantic embeddings to encode the semantic types of map elements and supervise them with semantic segmentation, to make their feature representation consistent with BEV features. After that, we feed map queries, composed of learnable semantic embeddings and coordinates of map elements, into a transformer decoder to perform cross-modality matching with BEV features. Finally, we adopt a robust histogram-based pose solver to estimate the optimal pose by searching exhaustively over candidate poses. We comprehensively validate the effectiveness of our method using both the nuScenes dataset and a newly collected dataset. The experimental results show that our method achieves centimeter-level localization accuracy, and outperforms existing methods using vectorized maps by a large margin. Furthermore, our model has been extensively tested in a large fleet of autonomous vehicles under various challenging urban scenes.Comment: 8 page

Transcriptome profiling reveals the crucial biological pathways involved in cold response in Moso bamboo (Phyllostachys edulis).

Most bamboo species including Moso bamboo (Phyllostachys edulis) are tropical or subtropical plants that greatly contribute to human well-being. Low temperature is one of the main environmental factors restricting bamboo growth and geographic distribution. Our knowledge of the molecular changes during bamboo adaption to cold stress remains limited. Here, we provided a general overview of the cold-responsive transcriptional profiles in Moso bamboo by systematically analyzing its transcriptomic response under cold stress. Our results showed that low temperature induced strong morphological and biochemical alternations in Moso bamboo. To examine the global gene expression changes in response to cold, 12 libraries (non-treated, cold-treated 0.5, 1 and 24 h at -2 °C) were sequenced using an Illumina sequencing platform. Only a few differentially expressed genes (DEGs) were identified at early stage, while a large number of DEGs were identified at late stage in this study, suggesting that the majority of cold response genes in bamboo are late-responsive genes. A total of 222 transcription factors from 24 different families were differentially expressed during 24-h cold treatment, and the expressions of several well-known C-repeat/dehydration responsive element-binding factor negative regulators were significantly upregulated in response to cold, indicating the existence of special cold response networks. Our data also revealed that the expression of genes related to cell wall and the biosynthesis of fatty acids were altered in response to cold stress, indicating their potential roles in the acquisition of bamboo cold tolerance. In summary, our studies showed that both plant kingdom-conserved and species-specific cold response pathways exist in Moso bamboo, which lays the foundation for studying the regulatory mechanisms underlying bamboo cold stress response and provides useful gene resources for the construction of cold-tolerant bamboo through genetic engineering in the future