19,507 research outputs found
Pseudonocardia lutea sp. nov., a novel actinobacterium isolated from soil in Chad
No full textGao, Yuhang, Piao, Chenyu, Wang, Han, Shi, Linlin, Guo, Xiaowei, Song, Jia, Xiang, Wensheng, Zhao, Junwei, Wang, Xiangjing (2018): Pseudonocardia lutea sp. nov., a novel actinobacterium isolated from soil in Chad. International Journal of Systematic and Evolutionary Microbiology 68 (6): 1992-1997, DOI: 10.1099/ijsem.0.002780, URL: http://dx.doi.org/10.1099/ijsem.0.00278
Actinomadura harenae sp. nov., a novel actinomycete isolated from sea sand in Sanya
No full textHu, Jiangmeihui, Han, Chuanyu, Yu, Bing, Zhao, Junwei, Guo, Xiaowei, Shen, Yue, Wang, Xiangjing, Xiang, Wensheng (2020): Actinomadura harenae sp. nov., a novel actinomycete isolated from sea sand in Sanya. International Journal of Systematic and Evolutionary Microbiology 70 (2): 766-772, DOI: 10.1099/ijsem.0.00381
Remote Sensing Image Scene Classification: Benchmark and State of the Art
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Remote sensing image scene classification plays an important role in a wide range of applications and hence has been receiving remarkable attention. During the past years, significant efforts have been made to develop various data sets or present a variety of approaches for scene classification from remote sensing images. However, a systematic review of the literature concerning data sets and methods for scene classification is still lacking. In addition, almost all existing data sets have a number of limitations, including the small scale of scene classes and the image numbers, the lack of image variations and diversity, and the saturation of accuracy. These limitations severely limit the development of new approaches especially deep learning-based methods. This paper first provides a comprehensive review of the recent progress. Then, we propose a large-scale data set, termed "NWPU-RESISC45," which is a publicly available benchmark for REmote Sensing Image Scene Classification (RESISC), created by Northwestern Polytechnical University (NWPU). This data set contains 31 500 images, covering 45 scene classes with 700 images in each class. The proposed NWPU-RESISC45 1) is large-scale on the scene classes and the total image number; 2) holds big variations in translation, spatial resolution, viewpoint, object pose, illumination, background, and occlusion; and 3) has high within-class diversity and between-class similarity. The creation of this data set will enable the community to develop and evaluate various data-driven algorithms. Finally, several representative methods are evaluated using the proposed data set, and the results are reported as a useful baseline for future research.</p
Dataset to support the article "High-resolution 𝜙-OFDR using phase unwrap and nonlinearity suppression"
No full textThis dataset is used for realizing high resolution of phase-sensitive Optical Frequency Domain Reflectometer. It is associated with the research paper:
Guo Z, Yan J, Han G, Yu Y, Greenwood D and Marco J (2023) "High-Resolution φ-OFDR Using Phase Unwrap and Nonlinearity Suppression". Journal of Lightwave Technology, 41 (9), 2885-2891. (https://doi.org/10.1109/JLT.2023.3236775).
The data is presented as an excel file:
High_resolution_OFDR_using_phase_unwrap_and_nonlinearity_suppression.xlsx
This work was funded by High Value Manufacturing Catapult and the Engineer and Physical Sciences Research Council - EPSRC EP/V000624/1. The author Gaoce Han would like to acknowledge the China Scholarship Council for sponsoring.</span
Han Suyin (Chinese author) speaking at Dallas Brookes Hall.
No full textThis record was harvested from a previous catalogue system and will be withdrawn in 2025. Information in this record may be superseded or incomplete. Visit this record in UMA's new catalogue at: https://archives.library.unimelb.edu.au/nodes/view/276390Han Suyin (Chinese author) speaking at Dallas Brookes Hall.200056
Item: [1999.0081.00439] "Han Suyin (Chinese author) speaking at Dallas Brookes Hall.
A Study on the mathematics textbooks in the era of the Great Han Empire
No full text이 글은 갑오경장(1894)과 경술 국치(1910) 사이에 간행된 산학(수학) 교재류의 목록을 확인하고, 각 텍스트의 출판과 관련된 사항, 소장처, 이본 등의 서지적 정보와 함께 이 시기 산학 교재류의 국어사 자료로서의 의의를 언어 사용 상의 측면에 초점을 두어 정리하는 것을 목적으로 한다. 이는 현대 한국어 태동기의 분과 학문의 도입 양상에 대한 연구의 일환인 한편, 학술 용어의 번역과 정착을 중심으로 이 시기의 한국어의 어휘 확장 양상을 확인하는 데에 필요한 기초 자료를 정리하는 작업의 한 부분이다. 본 연구에 앞선 산학(수학) 교재류에 대한 연구로는 산학 교재류의 서지 사항에 대해 기술한 강윤호(1973:187-199), 김봉희(1992:247-253), 한길준(2009), 오채환 외(2010) 등이 있고, 한국 수학사를 기술하면서 교재류를 함께 다룬 것으로 김용운·김용국(1982)와 이상구(2013)이 있다.This paper aims to make a whole list of the mathematics textbooks in the era of the Great Han Empire and summerize bibliographical data and linguistic characteristics in view of Korean history. In chapter 1, the author reviewed former studies which deals with the mathematics textbooks in the era of the Great Han Empire. In chapter 2, the author summerized bibliographical data of 45 volumes of 32 kinds textbooks. In chapter 3, the author described linguistic characteristics of the textbooks, especially focusing on writing systems, the use of Arabic numerals, horizontal writing, and presence of index or glossary
Also By The Same Author: AKTiveAuthor, a Citation Graph Approach to Name Disambiguation
No full textThe desire for definitive data and the semantic web drive for inference over heterogeneous data sources requires co-reference resolution to be performed on those data. In particular, name disambiguation is required to allow accurate publication lists, citation counts and impact measures to be determined. This paper describes a graph-based approach to author disambiguation on large-scale citation networks. Using self-citation, co-authorship and document source analyses, AKTiveAuthor clusters papers, achieving precision of 0.997 and recall of 0.818 over a test group of eight surname clusters
Unsupervised 3D Local Feature Learning by Circle Convolutional Restricted Boltzmann Machine
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Extracting local features from 3D shapes is an important and challenging task that usually requires carefully designed 3D shape descriptors. However, these descriptors are hand-crafted and require intensive human intervention with prior knowledge. To tackle this issue, we propose a novel deep learning model, namely circle convolutional restricted Boltzmann machine (CCRBM), for unsupervised 3D local feature learning. CCRBM is specially designed to learn from raw 3D representations. It effectively overcomes obstacles such as irregular vertex topology, orientation ambiguity on the 3D surface, and rigid or slightly non-rigid transformation invariance in the hierarchical learning of 3D data that cannot be resolved by the existing deep learning models. Specifically, by introducing the novel circle convolution, CCRBM holds a novel ring-like multi-layer structure to learn 3D local features in a structure preserving manner. Circle convolution convolves across 3D local regions via rotating a novel circular sector convolution window in a consistent circular direction. In the process of circle convolution, extra points are sampled in each 3D local region and projected onto the tangent plane of the center of the region. In this way, the projection distances in each sector window are employed to constitute a novel local raw 3D representation called projection distance distribution (PDD). In addition, to eliminate the initial location ambiguity of a sector window, the Fourier transform modulus is used to transform the PDD into the Fourier domain, which is then conveyed to CCRBM. Experiments using the learned local features are conducted on three aspects: global shape retrieval, partial shape retrieval, and shape correspondence. The experimental results show that the learned local features outperform other state-of-the-art 3D shape descriptors.</p
Dataset to support the article "High Sensing Accuracy Realisation with Millimetre/sub-Millimetre Resolution in Optical Frequency Domain Reflectometer"
No full textThis dataset is used for realizing high sensing accuracy and sub-millimetre resolution of Optical Frequency Domain Reflectometer. It is associated with the research paper "High Sensing Accuracy Realisation with Millimetre sub-Millimetre Resolution in Optical Frequency Domain Reflectometer" in Journal: Journal of Lightwave Technology.
This work was funded by High Value Manufacturing Catapult, grant reference, 160080 CORE (WMG), titled ‘Smart Sensing for Future Batteries’ and the EPSRC (Engineering and Physical Sciences Research Council), grant reference EP/R004927/1, titled ‘Prosperity Partnership’. The author Gaoce Han would like to acknowledge the China Scholarship Council for sponsoring.</span
Bilateral K - Means algorithm for fast co-clustering
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With the development of the information technology, the amount of data, e.g. text, image and video, has been increased rapidly. Efficiently clustering those large scale data sets is a challenge. To address this problem, this paper proposes a novel co-clustering method named bilateral k-means algorithm (BKM) for fast co-clustering. Different from traditional k-means algorithms, the proposed method has two indicator matrices P and Q and a diagonal matrix S to be solved, which represent the cluster memberships of samples and features, and the co-cluster centres, respectively. Therefore, it could implement different clustering tasks on the samples and features simultaneously. We also introduce an effective approach to solve the proposed method, which involves less multiplication. The computational complexity is analyzed. Extensive experiments on various types of data sets are conducted. Compared with the state-of-the-art clustering methods, the proposed BKM not only has faster computational speed, but also achieves promising clustering results. Copyright © 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.</p
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