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| 1 | TDD-net: a tiny defect detection network for printed circuit boards显示文摘Tiny defect detection (TDD) which aims to perform the quality control of printed circuit boards (PCBs) is a basic and essential task in the production of most electronic products. Though significant progress has been made in PCB defect detection, traditional methods are still difficult to cope with the complex and diverse PCBs. To deal with these problems, this article proposes a tiny defect detection network (TDD-Net) to improve performance for PCB defect detection. In this method, the inherent multi-scale and pyramidal hierarchies of deep convolutional networks are exploited to construct feature pyramids. Compared with existing approaches, the TDD-Net has three novel changes. First, reasonable anchors are designed by using k-means clustering. Second, TDD-Net strengthens the relationship of feature maps from different levels and benefits from low-level structural information, which is suitable for tiny defect detection. Finally, considering the small and imbalance dataset, online hard example mining is adopted in the whole training phase in order to improve the quality of region-of-interest (ROI) proposals and make more effective use of data information. Quantitative results on the PCB defect dataset show that the proposed method has better portability and can achieve 98.90% mAP, which outperforms the state-of-arts. The code will be publicly available. | Runwei Ding Linhui Dai Guangpeng Li Hong Liu | 2019 | CAAI Transactions on Intelligence Technology2019，4，2： | 27 |
| 2 | Synthesis and Visible-light Photocatalytic Performance of C-doped Nb2O5 with High Surface Area显示文摘 | DING Shuang WANG Runwei ZHANG Panpan KANG Bonan ZHANG Daliang ZHANG Zongtao QIU Shilun | 2018 | Chemical Research in Chinese Universities2018，34，2： | 3 |
| 3 | Salient pairwise spatio-temporal interest points for real-time activity recognition显示文摘 | Mengyuan Liu Hong Liu Qianru Sun Tianwei Zhang Runwei Ding | 2016 | CAAI Transactions on Intelligence Technology2016，1，1： | 0 |
| 4 | Achieving a high magnetization in sub-nanostructured magnetite films by spin-flipping of tetrahedral Fe3. cations显示文摘Magnetite Fe304 (ferrite) has attracted considerable interest for its exceptional physical properties: It is predicted to be a semimetallic ferromagnetic with a high Curie temperature, it displays a metal-insulator transition, and has potential oxide-electronics applications. Here, we fabricate a high-magnetization (〉 1 Tesla) high-resistance (-0.1 Ωcm) sub-nanostructured (grain size 〈 3 nm) Fe304 film via grain-size control and nano-engineering. We report a new phenomenon of spin- flipping of the valence-spin tetrahedral FeB* in the sub-nanostructured Fe304 film, which produces the high magnetization. Using soft X-ray magnetic circular dichroism and soft X-ray absorption, both at the Fe L3,2- and O K-edges, and supported by first-principles and charge-transfer multiple calculations, we observe an anomalous enhancement of double exchange, accompanied by a suppression of the superexchange interactions because of the spin-flipping mechanism via oxygen at the grain boundaries. Our result may open avenues for developing spin- manipulated giant magnetic Fe304-based compounds via nano-grain size control. | Tun Seng Herng Wen Xiao Sock Mui Poh Feizhou He Ronny Sutarto Xiaojian Zhu Runwei Li Xinmao Yin Caozheng Diao Yang Yang Xuelian Huang Xiaojiang Yu Yuan Ping Feng AndrivoRusydi Jun Ding | 2015 | Nano Research2015，8，9： | 0 |
| 5 | Enhancing direct-path relative transfer function using deep neural network for robust sound source localization显示文摘This article proposes a deep neural network(DNN)-based direct-path relative transfer function(DP-RTF)enhancement method for robust direction of arrival(DOA)estimation in noisy and reverberant environments.The DP-RTF refers to the ratio between the directpath acoustic transfer functions of the two microphone channels.First,the complex-value DP-RTF is decomposed into the inter-channel intensity difference,and sinusoidal functions of the inter-channel phase difference in the time-frequency domain.Then,the decomposed DP-RTF features from a series of temporal context frames are utilized to train a DNN model,which maps the DP-RTF features contaminated by noise and reverberation to the clean ones,and meanwhile provides a time-frequency(TF)weight to indicate the reliability of the mapping.The DP-RTF enhancement network can help to enhance the DP-RTF against noise and reverberation.Finally,the DOA of a sound source can be estimated by integrating the weighted matching between the enhanced DP-RTF features and the DP-RTF templates.Experimental results on simulated data show the superiority of the proposed DP-RTF enhancement network for estimating the DOA of the sound source in the environments with various levels of noise and reverberation. | Bing Yang Runwei Ding Yutong Ban Xiaofei Li Hong Liu | 2022 | CAAI Transactions on Intelligence Technology2022，7，3： | 0 |
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