检索结果-内蒙古大学图书馆

IOP Conference Series: Earth and Environmental Science 2020年第1期615卷

作者： Fengbin Zheng Yu Xia Qiang Ge Kun Cai Henan Engineering Laboratory of Spatial Information Processing Henan University Henan 475004 PR China Henan Key Laboratory of Big Data Analysis and Processing Henan University Henan 475004 PR China School of Computer and Information Engineering Henan University Henan 475004 PR China

Satellite remote sensing will have certain errors in data retrieval accuracy, which requires further ground observation verification. This paper examines and verifies 2019 Jiangsu provincial Ground-level monitoring data and TROPOMI data to evaluate the applicability of TROPOMI tropospheric NO2 Vertical Column Density (VCD) data and compares the results to OMI data analysis verification results. The results show that the correlation coefficient r between the TROPOMI NO2 data and Ground-level monitoring data at the monthly mean scale is as high as 0.9, and consistent seasonal cyclic changes are observed. The correlation between Ground-level monitoring data and OMI NO2 data is lower than TROPOMI (r=0.78 < 0.9). Compared to OMI satellites, the TROPOMI attains a smaller deviation in tropospheric NO2 monitoring. Jiangsu province had the highest monthly average concentration in January, which were 38.67 ug/m3 (Ground-level), 17.35 × 1015 molec/cm2 (TROPOMI) and 20.04 × 1015 molec/cm2 (OMI). The lowest concentration in August was 13.39 ug/m3 (Ground-level), 5.2 × 1015 molec/cm2 (TROPOMI) and 7.03 × 1015 molec/cm2 (OMI).

关键词：

An extragradient algorithm for fixed point problems and pseudomonotone equilibrium problems

学校读者我要写书评

暂无评论

UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2020年第4期82卷 89-96页

作者： Yao, Zhangsong Liou, Yeong-Cheng Zhu, Li-Jun School of Information Engineering Nanjing Xiaozhuang University Nanjing211171 China Department of Healthcare Administration and Medical Informatics Research Center of Nonlinear Analysis and Optimization Kaohsiung Medical University Department of Medical Research Kaohsiung Medical University Hospital Kaohsiung807 Taiwan The Key Laboratory of Intelligent Information and Big Data Processing of NingXia Province and Health Big Data Research Institute Advanced Intelligent Perception and Control Technology Innovative Team of NingXia North Minzu University Yinchuan750021 China

Iterative methods for solving fixed point problems and pseudomonotone equilibrium problems have been considered. An extragradient algorithm has been presnted for finding a common element of the fixed points of pseudocontractive operators and the solutions of pseudomonotone equilibrium problems in Hilbert spaces. Weak convergence result of the suggested algorithm is proved. © 2020, Politechnica University of Bucharest. All rights reserved.

关键词： Iterative methods

Residual objectness for imbalance reduction

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Chen, Joya Liu, Dong Luo, Bin Peng, Xuezheng Xu, Tong Chen, Enhong Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Tencent TaiQ Team

For a long time, object detectors have suffered from extreme imbalance between foregrounds and backgrounds. While several sampling/reweighting schemes have been explored to alleviate the imbalance, they are usually heuristic and demand laborious hyper-parameters tuning, which is hard to achieve the optimality. In this paper, we first reveal that such the imbalance could be addressed in a learning-based manner. Guided by this illuminating observation, we propose a novel Residual Objectness (ResObj) mechanism that addresses the imbalance by end-to-end optimization, while no further hand-crafted sampling/reweighting is required. Specifically, by applying multiple cascaded objectness-related modules with residual connections, we formulate an elegant consecutive refinement procedure for distinguishing the foregrounds from backgrounds, thereby progressively addressing the imbalance. Extensive experiments present the effectiveness of our method, as well as its compatibility and adaptivity for both region-based and one-stage detectors, namely, the RetinaNet-ResObj, YOLOv3-ResObj and FasterRCNN-ResObj achieve relative 3.6%, 3.9%, 3.2% Average Precision (AP) improvements compared with their vanilla models on COCO, respectively. Copyright © 2019, The Authors. All rights reserved.

关键词： Object detection

Refining HTN methods via task insertion with preferences (ArXiv version)

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Xiao, Zhanhao Wan, Hai Zhuo, Hankui Hankz Herzig, Andreas Perrussel, Laurent Chen, Peilin School of Data and Computer Science Sun Yat-sen University Guangzhou China IRIT Toulouse France University of Toulouse Toulouse France Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Guangdong Province Key Laboratory of Big Data Analysis and Processing China

Hierarchical Task Network (HTN) planning is showing its power in real-world planning. Although domain experts have partial hierarchical domain knowledge, it is time-consuming to specify all HTN methods, leaving them incomplete. On the other hand, traditional HTN learning approaches focus only on declarative goals, omitting the hierarchical domain knowledge. In this paper, we propose a novel learning framework to refine HTN methods via task insertion with completely preserving the original methods. As it is difficult to identify incomplete methods without designating declarative goals for compound tasks, we introduce the notion of prioritized preference to capture the incompleteness possibility of methods. Specifically, the framework first computes the preferred completion profile w.r.t. the prioritized preference to refine the incomplete methods. Then it finds the minimal set of refined methods via a method substitution operation. Experimental analysis demonstrates that our approach is effective, especially in solving new HTN planning instances. Copyright © 2019, The Authors. All rights reserved.

关键词： Domain Knowledge

Online Multiple Object Tracking with Reid Feature Extraction Network and Similarity Matrix Network

学校读者我要写书评

暂无评论

Journal of Physics: Conference Series 2020年第1期1544卷

作者： Qingge Ji Haoqiang Yu School of Data and Computer Science Sun Yat-sen University Guangzhou 510006 China Guangdong Province Key Laboratory of Big Data Analysis and Processing Guangzhou 510006 China

In multiple object tracking(MOT), data association is a crucial part. By constructing similarity loss matrix for trajectories and detections, they can be matched correspondingly by using Hungarian matching algorithm. However, the similarity loss is often obtained by calculating the Euclidean distance or other handcraft distance metrics of features extracted between objects, which may not be robust enough, resulting in matching inaccuracy. In this paper, we propose a novel MOT method with applying deep learning to feature extraction and data association. We firstly design the appearance feature extraction network(AFN) to learn effective features by training it on a large-scale person re-identification dataset(reid). Then, we propose the similarity matrix estimation network (SMN) to obtain reliable similarity by training it on the public MOT Challenge dataset, MOT17. Additionally, the similarity matrix output of SMN includes the dummy objects, which are used to deal with the association problems of object missing and object appearance between frames. In the end, our proposed MOT method is evaluated on MOT15, MOT17 and ablation study is carried out.

关键词：

Application of Big data Computing Model Based on Semantic data analysis Technology

学校读者我要写书评

暂无评论

IOP Conference Series: Materials Science and Engineering 2020年第1期750卷

作者： Ganglong Fan Jian Shen Henan key Laboratory for Big Data Processing & Analytics of Electronic Commerce Luoyang Normal University Luoyang China Higher Education Admissions Office of HENAN Province Zhengzhou China

Big data computing model refers to various high-level abstract models extracted and established from the diverse big data computing problems and requirements according to big data's different data characteristics and computational characteristics. In order to improve the data quality, a method of adding semantics to the data is proposed to help users identify big data's pattern. In this way, we can make use of the semantic value of the data, detect big data, and propose a big data computing model through the semantic analysis of the data. The paper presents research on big data computing model based on semantic data analysis technology.

关键词：

A novel cloud game framework in dual Gigabit Network

学校读者我要写书评

暂无评论

Journal of Physics: Conference Series 2020年第1期1684卷

作者： Kai Sun Tao Xu Shanghai Urban Construction Vocational College Shanghai 201415 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng 475004 China. School of Computer and Information Engineering Henan University Kaifeng 475004 China.

With the development of 5G, cloud computing, and artificial intelligence (AI) technology, cloud game has become a hotspot about application and innovation of Gigabit bandwidth. This paper, firstly, analyses the present situation and development of cloud game to clarify the key business and key technology of cloud game, then, proposes an operational framework of cloud game, finally, the feasibility of the proposed framework is discussed and several suggestions about the operation of cloud game are given.

关键词：

Research on the Brain-inspired Cross-modal Neural Cognitive Computing Framework

学校读者我要写书评

暂无评论

arXiv 2018年

作者： Liu, Yang Key Laboratory of Big Data Analysis and Processing of Henan province Intelligent Technology and Application Engineering Research Centre of Henan province College of Computer Science and Information Engineering Henan University Kaifeng475004 China

To address modeling problems of brain-inspired intelligence, this thesis is focused on researching in the semantic-oriented framework design for multimedia and multimodal information. The Multimedia Neural Cognitive Computing (MNCC) model was designed based on the nervous mechanism and cognitive architecture. Furthermore, the semantic-oriented hierarchical Cross-modal Neural Cognitive Computing (CNCC) framework was proposed based on MNCC model, and formal description and analysis for CNCC framework was given. It would effectively improve the performance of semantic processing for multimedia and cross-modal information, and has far-reaching significance for exploration and realization brain-inspired computing. Copyright © 2018, The Authors. All rights reserved.

关键词： Semantics

AI-skin: Skin disease recognition based on self-learning and wide data collection through a closed loop framework

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Chen, Min Zhou, Ping Wu, Di Hu, Long Hassan, Mohammad Mehedi Alamri, Atif School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Data and Computer Science Sun Yat-sen University Guangzhou China Guangdong Province Key Laboratory of Big Data Analysis and Processing Guangzhou China Chair of Pervasive and Mobile Computing Information Systems Department College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia Chair of Pervasive and Mobile Computing Software Engineering Department CCIS King Saud University Riyadh11543 Saudi Arabia

There are a lot of hidden dangers in the change of human skin conditions, such as the sunburn caused by long-time exposure to ultraviolet radiation, which not only has aesthetic impact causing psychological depression and lack of selfconfidence, but also may even be life-threatening due to skin canceration. Current skin disease researches adopt the auto-classification system for improving the accuracy rate of skin disease classification. However, the excessive dependence on the image sample database is unable to provide individualized diagnosis service for different population groups. To overcome this problem, a medical AI framework based on data width evolution and self-learning is put forward in this paper to provide skin disease medical service meeting the requirement of real time, extendibility and individualization. First, the wide collection of data in the close-loop information flow of user and remote medical data center is discussed. Next, a data set filter algorithm based on information entropy is given, to lighten the load of edge node and meanwhile improve the learning ability of remote cloud analysis model. In addition, the framework provides an external algorithm load module, which can be compatible with the application requirements according to the model selected. Three kinds of deep learning model, i.e. LeNet-5, AlexNet and VGG16, are loaded and compared, which have verified the universality of the algorithm load module. The experiment platform for the proposed real-time, individualized and extensible skin disease recognition system is built. And the system's computation and communication delay under the interaction scenario between tester and remote data center are analyzed. It is demonstrated that the system we put forward is reliable and effective. Copyright © 2019, The Authors. All rights reserved.

关键词： data acquisition

Community-developed checklists for publishing images and image analyses

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Schmied, Christopher Nelson, Michael S. Avilov, Sergiy Bakker, Gert-Jan Bertocchi, Cristina Bischof, Johanna Boehm, Ulrike Brocher, Jan Carvalho, Mariana Chiritescu, Catalin Christopher, Jana Cimini, Beth A. Conde-Sousa, Eduardo Ebner, Michael Ecker, Rupert Eliceiri, Kevin Fernandez-Rodriguez, Julia Gaudreault, Nathalie Gelman, Laurent Grunwald, David Gu, Tingting Halidi, Nadia Hammer, Mathias Hartley, Matthew Held, Marie Jug, Florian Kapoor, Varun Koksoy, Ayse Aslihan Lacoste, Judith Le Dévédec, Sylvia Le Guyader, Sylvie Liu, Penghuan Martins, Gabriel G. Mathur, Aastha Miura, Kota Llopis, Paula Montero Nitschke, Roland North, Alison Parslow, Adam C. Payne-Dwyer, Alex Plantard, Laure Ali, Rizwan Schroth-Diez, Britta Schütz, Lucas Scott, Ryan T. Seitz, Arne Selchow, Olaf Sharma, Ved P. Spitaler, Martin Srinivasan, Sathya Strambio-De-Castillia, Caterina Taatjes, Douglas Tischer, Christian Jambor, HelenaKlara Fondazione Human Technopole Viale Rita Levi-Montalcini 1 Milano20157 Italy Department of Biomedical Engineering University of Wisconsin-Madison MadisonWI53706 United States Max Planck Institute of Immunobiology and Epigenetics Freiburg79108 Germany Medical BioSciences department Radboud University Medical Centre Nijmegen Netherlands Laboratory for Molecular mechanics of cell adhesions Pontificia Universidad Católica de Chile Santiago Chile Osaka University Graduate School of Engineering Science Japan Euro-BioImaging ERIC Bio-Hub Meyerhofstr. 1 Heidelberg69117 Germany Carl Zeiss AG Carl-Zeiss-Straße 22 Oberkochen73447 Germany BioVoxxel Scientific Image Processing and Analysis Eugen-Roth-Strasse 8 Ludwigshafen67071 Germany Nanophotonics and BioImaging Facility at INL International Iberian Nanotechnology Laboratory 4715-330 Portugal Phi Optics Inc. 1800 S. Oak St Ste 106 ChampaignIL61820 United States Biochemistry Center Heidelberg Heidelberg University Germany Imaging Platform Broad Institute CambridgeMA02142 United States i3S Instituto de Investigação e Inovação Em Saúde and INEB Instituto de Engenharia Biomédica Universidade do Porto Porto Portugal Translational Research Institute Queensland University of Technology 37 Kent Street WoolloongabbaQLD4102 Australia School of Biomedical Sciences Faculty of Health Queensland University of Technology BrisbaneQLD4059 Australia TissueGnostics GmbH Vienna1020 Austria Department of Medical Physics and Biomedical Engineering University of Wisconsin-Madison MadisonWI53706 United States Centre for Cellular Imaging Core Facility Sahlgrenska Academy University of Gothenburg Sweden Allen Institute for Cell Science SeattleWA United States Friedrich Miescher Institute for Biomedical Research Basel Switzerland RNA Therapeutics Institute University of Massachusetts Chan Medical School WorcesterMA01605 United States University of Oklahoma NormanOK United States Advanced Light Microscopy Unit Ce

Images document scientific discoveries and are prevalent in modern biomedical research. Microscopy imaging in particular is currently undergoing rapid technological advancements. However for scientists wishing to publish the obtained images and image analyses results, there are to date no unified guidelines. Consequently, microscopy images and image data in publications may be unclear or difficult to interpret. Here we present community-developed checklists for preparing light microscopy images and image analysis for publications. These checklists offer authors, readers, and publishers key recommendations for image formatting and annotation, color selection, data availability, and for reporting image analysis workflows. The goal of our guidelines is to increase the clarity and reproducibility of image figures and thereby heighten the quality and explanatory power of microscopy data is in publications. © 2023, CC BY-SA.

关键词： Image analysis