检索结果-内蒙古大学图书馆

arXiv 2021年

作者： Xu, Guangquan Liu, Yihua Xing, Jun Luo, Tao Gu, Yonghao Liu, Shaoying Zheng, Xi Vasilakos, Athanasios V. The Big Data School Qingdao Huanghai University Qingdao266427 China College of Intelligence and Computing Tianjin University Tianjin300350 China The International Engineering Institute Tianjin University Tianjin300350 China The College of Intelligence and Computing Tianjin University Tianjin Tianjin300350 China The school of Computer Science Beijing University of Posts and Telecommunications Beijing100876 China The Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia Beijing University of Posts and Telecommunications Beijing100876 China School of Informatics and Data Science Hiroshima University Japan The Department of Computing Macquarie University SydneyNSW2109 Australia The University of Technology Sydney Australia The Fuzhou University Fuzhou China Lulea University of Technology Lulea Sweden

The Internet of Vehicles (IoV) is an application of the Internet of things (IoT). It faces two main security problems: (1) the central server of the IoV may not be powerful enough to support the centralized authentication of the rapidly increasing connected vehicles, (2) the IoV itself may not be robust enough to single-node attacks. To solve these problems, this paper proposes SG-PBFT: a secure and highly efficient PBFT consensus algorithm for Internet of Vehicles, which is based on a distributed blockchain structure. The distributed structure can reduce the pressure on the central server and decrease the risk of single-node attacks. The SG-PBFT consensus algorithm improves the traditional PBFT consensus algorithm by using a score grouping mechanism to achieve a higher consensus efficiency. The experimental result shows that our method can greatly improve the consensus efficiency and prevent single-node attacks. Specifically, when the number of consensus nodes reaches 1000, the consensus time of our algorithm is only about 27% of what is required for the state-of-the-art consensus algorithm (PBFT). Our proposed SG-PBFT is versatile and can be used in other application scenarios which require high consensus efficiency. Copyright © 2021, The Authors. All rights reserved.

关键词： Internet of things

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Deep learning compact binary codes for fingerprint indexing

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Frontiers of Information Technology & Electronic engineering 2018年第9期19卷 1112-1123页

作者： Chao-chao BAI Wei-qiang WANG Tong ZHAO Ru-xin WANG Ming-qiang LI School of Computer and Control University of Chinese Academy of Science School of Mathematical Sciences University of Chinese Academy of Science Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Science

With the rapid growth in fingerprint databases, it has become necessary to develop excellent fingerprint indexing to achieve efficiency and accuracy. Fingerprint indexing has been widely studied with real-valued features,but few studies focus on binary feature representation, which is more suitable to identify fingerprints efficiently in large-scale fingerprint databases. In this study, we propose a deep compact binary minutia cylinder code（DCBMCC）as an effective and discriminative feature representation for fingerprint indexing. Specifically, the minutia cylinder code（MCC）, as the state-of-the-art fingerprint representation, is analyzed and its shortcomings are ***, we propose a novel fingerprint indexing method based on deep neural networks to learn *** novel network restricts the penultimate layer to directly output binary codes. Moreover, we incorporate independence, balance, quantization-loss-minimum, and similarity-preservation properties in this learning ***, a multi-index hashing（MIH） based fingerprint indexing scheme further speeds up the exact search in the Hamming space by building multiple hash tables on binary code substrings. Furthermore, numerous experiments on public databases show that the proposed approach is an outstanding fingerprint indexing method since it has an extremely small error rate with a very low penetration rate.

关键词： Fingerprint indexing Minutia cylinder code Deep neural network Multi-index hashing

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Emotion correlation mining through deep learning models on natural language text

arXiv

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arXiv 2020年

作者： Wang, Xinzhi Kou, Luyao Sugumaran, Vijayan Luo, Xiangfeng Zhang, Hui School of Computer Engineering and Science Shanghai University Shanghai200444 China Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai200444 China Institute of Public Safety Research Department of Engineering Physics Tsinghua University Beijing Beijing100084 China Key Laboratory of City Integrated EmergencyResponse Science Tsinghua University Beijing100084 China Department of Decision and Information Sciences Oakland University RochesterMI48309 United States Center for Data Science and Big Data Analytics Oakland University RochesterMI48309 United States

Emotion analysis has been attracting researchers’ attention. Most previous works in the artificial-intelligence field focus on recognizing emotion rather than mining the reason why emotions are not or wrongly recognized. The correlation among emotions contributes to the failure of emotion recognition. In this article, we try to fill the gap between emotion recognition and emotion correlation mining through natural language text from Web news. The correlation among emotions, expressed as the confusion and evolution of emotion, is primarily caused by human emotion cognitive bias. To mine emotion correlation from emotion recognition through text, three kinds of features and two deep neural-network models are presented. The emotion confusion law is extracted through an orthogonal basis. The emotion evolution law is evaluated from three perspectives: one-step shift, limited-step shifts, and shortest path transfer. The method is validated using three datasets: 1) the titles;2) the bodies;and 3) the comments of news articles, covering both objective and subjective texts in varying lengths (long and short). The experimental results show that in subjective comments, emotions are easily mistaken as anger. Comments tend to arouse emotion circulations of love–anger and sadness–anger. In objective news, it is easy to recognize text emotion as love and cause fear–joy circulation. These findings could provide insights for applications regarding affective interaction, such as network public sentiment, social media communication, and human–computer interaction. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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Tunable schiff-based networks with different bonding sites for enhanced photocatalytic activity under visible-light irradiation: The effects of steric hindrance

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Environmental research 2024年 246卷 118200页

作者： Han Wang Piao Xu Eydhah Almatrafi Ziwei Wang Chengyun Zhou Ting Xiong Hong Qin Yangzhuo He Hailan Yang Zhuotong Zeng Guangming Zeng School of Metallurgy and Environment Central South University Changsha 410083 China Department of Dermatology Second Xiangya Hospital Central South University Changsha 410011 China College of Environmental Science and Engineering and Key Laboratory of Environmental Biology and Pollution Control (Ministry of Education) Hunan University Changsha 410082 China Center of Research Excellence in Renewable Energy and Power Systems Center of Excellence in Desalination Technology Department of Mechanical Engineering King Abdulaziz University Jeddah 21589 Saudi Arabia. College of Environmental Science and Engineering and Key Laboratory of Environmental Biology and Pollution Control (Ministry of Education) Hunan University Changsha 410082 China. Institute of Big Data and Internet Innovation Hunan University of Technology and Business Changsha 410205 China. Center of Research Excellence in Renewable Energy and Power Systems Center of Excellence in Desalination Technology Department of Mechanical Engineering King Abdulaziz University Jeddah 21589 Saudi Arabia. Electronic address: zgming@***.

Organic polymers hold great potential in photocatalysis considering their low cost, structural tailorability, and well-controlled degree of conjugation for efficient electron transfer. Among the polymers, Schiff base networks (SNWs) with high nitrogen content have been noticed. Herein, a series of SNWs is synthesized based on the melamine units and dialdehydes with different bonding sites. The chemical and structural variation caused by steric hindrance as well as the related photoelectric properties of the SNW samples are investigated, along with the application exploration on photocatalytic degradation and energy production. The results demonstrate that only SNW-o based on o-phthalaldehyde responds to visible light, which extends to over 550 nm. SNW-o shows the highest tetracycline degradation rate of 0.02516 min, under 60-min visible light irradiation. Moreover, the HO production of SNW-o is 2.14 times higher than that of g-CN. The enhanced photocatalytic activity could be ascribed to the enlarged visible light adsorption and intramolecular electron transfer. This study indicates the possibility to regulate the optical and electrical properties of organic photocatalysts on a molecular level, providing an effective strategy for rational supramolecular engineering to the applications of organic materials in photocatalysis.

关键词： H(2)O(2) production Photocatalytic degradation Schiff-based networks Steric hindrance Structure tunability

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Unsupervised keyword Extraction Method Based on Chinese Patent Clustering

Unsupervised Keyword Extraction Method Based on Chinese Pate...

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IEEE International Conference on big knowledge (ICBK)

作者： Yuxin Xie Xuegang Hu Yuhong Zhang Shi Li Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Hefei China Intelligent Connected Vehicle Research Institute (JAC Technical Center) Anhui Jianghuai Automobile Group Corp.Ltd Hefei China

Recently, patent data analysis has attracted a lot of attention, and patent keyword extraction is a hot problem. Most existing methods for patent keyword extraction are based on the frequency of words without semantic information. In this paper, we propose an Unsupervised keyword Extraction Method (UKEM) based on Chinese patent clustering. More specifically, we use a Skip-gram model to train word embeddings based on a Chinese patent corpus. Then each patent is represented as a vector called patent vector. These patent vectors are clustered to obtain several cluster centroids. Next, the distance between each word vector in patent abstract and cluster centroid is computed to indicate the semantic importance of this word. The experimental results on several Chinese patent datasets show that the performance of our proposed method is better than several competitive methods.

关键词： Patents Semantics data mining knowledge engineering Neural networks big data Computer science

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Impact of DG on short circuit current detected by protection

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IOP Conference Series: Earth and Environmental Science 2021年第1期675卷

作者： Jing Gong School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing 100044 China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing 100044 China National Virtual Simulation Experimental Center for Smart City Education Beijing100044 China

DG (distributed generation) access causes the change of current detected by the protection, and then affects the sensitivity of the original protection. In order to solve the problem, the influences of DG capacity, DG access location and short-circuit location on short circuit current are analysed in this paper. When the DG capacity and grid-connected position remain unchanged, the unit current method is used to solve the short-circuit current by solving the transfer impedance. The relationship between the change of short circuit position and the impact degree of DG is discussed in detail, and the calculation formula of extreme point is derived. When the DG capacity and short circuit location remain unchanged, the impact of DG grid-connected position on the upstream and downstream current is analysed. The characteristic curve of the impact of DG position change on protection current detection is given, and the impact of DG grid-connected position on protection sensitivity is explained clearly. The conclusion provides an important reference for the selection of DG grid-connected position and the calculation of relay protection in distribution network with DG.

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A study on wavelet selection in power signal denoising

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IOP Conference Series: Earth and Environmental Science 2021年第1期675卷

In the wavelet threshold denoising of power signal, the selection of wavelet has an important influence on the denoising effect, and the wavelet generating function has diversity, if not selected properly, it will directly lead to the failure of denoising. Firstly, an operator is introduced to modify the threshold of each scale to better reflect the variation of wavelet coefficients of signal and noise with scale. Then a controllable threshold function is proposed to adapt to different soft and hard characteristics, and it is used to denoise the wavelet coefficients. Based on the study of the characteristics of wavelet, such as orthogonality, vanishing moment, support length and symmetry, four principles of wavelet selection in power signal denoising are proposed. The voltage sag and harmonics model are established, and db5, coif1 and sym2 wavelets are selected to decompose the signal to the fourth scale for denoising. The signal-to-noise ratio, mean square error and the detailed features of the reconstructed signal after denoising are compared. The experimental results show that the orthogonal wavelet db5 with high vanishing moment order and long support length has better denoising effect than coif1 and sym2, which proves the correctness of the wavelet selection principles proposed in this paper.

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Automatic knowledge Graph Construction: A Report on the 2019 ICDM/ICBK Contest

Automatic Knowledge Graph Construction: A Report on the 2019...

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IEEE International Conference on data Mining (ICDM)

作者： Xindong Wu Jia Wu Xiaoyi Fu Jiachen Li Peng Zhou Xu Jiang Mininglamp Academy of Sciences Mininglamp Technology Beijing China Key Laboratory of Knowledge Engineering with Big Data (Hefei Univ. of Technology) Ministry of Education Hefei China Department of Computing Macquarie University Sydney Australia School of Computer Science and Technology Anhui University Hefei China

Automatic knowledge graph construction seeks to build a knowledge graph from unstructured text in a specific domain or cross multiple domains, without human intervention. IEEE ICDM 2019 and ICBK 2019 invited teams from both degree-granting institutions and industrial labs to compete in the 2019 knowledge Graph Contest by automatically constructing knowledge graphs in at least two different domains. This article reports the outcomes of the Contest. The participants were expected to build a model to extract knowledge represented as triplets from text data and develop a web application to visualize the triplets. Awards were given to five teams. Their models and key techniques used to construct knowledge graphs are summarized.

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Novel ansatz for charge radii in density functional theories

arXiv

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arXiv 2020年

作者： An, Rong Geng, Li-Sheng Zhang, Shi-Sheng School of Physics Beihang University Beijing100191 China Beijing Key Laboratory of Advanced Nuclear Materials and Physics Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Medicine and Engineering Beihang University Beijing100191 School of Physics and Microelectronics Zhengzhou University ZhengzhouHenan450001 China

Charge radii are one of the most fundamental properties of atomic nuclei characterizing their charge distributions. Though the general trend as a function of the mass number is well described by the A1/3 rule, some fine structures, such as the evolution along the calcium isotopic chain and the corresponding odd-even staggerings, are notoriously difficult to describe both in density functional theories and ab initio methods. In this letter, we propose a novel ansatz to describe the charge radii of calcium isotopes, by adding a correction term, proportional to the number of Cooper pairs, and determined by the BCS amplitudes and a single parameter, to the charge radii calculated in the relativistic mean field model with the pairing interaction treated with the BCS method. The new ansatz yields results consistent with data not only for calcium isotopes, but also for ten other isotopic chains, including oxygen, neon, magnesium, chromium, nickel, germanium, zirconium, cadmium, tin, and lead. It is remarkable that this ansatz with a single parameter can describe nuclear charge radii throughout the periodic table, particularly the odd-even staggerings and parabolic behavior. We hope that the present study can stimulate more discussions about its nature and relation with other effects proposed to explain the odd-even staggerings of charge radii. Copyright © 2020, The Authors. All rights reserved.

关键词： Isotopes

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A differentiability-based processes and parameters learning hydrologic model for advancing runoff prediction and process understanding

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Journal of Hydrology 2025年 661卷

作者： Chunxiao Zhang Heng Li Yuqian Hu Dingtao Shen Bingli Xu Min Chen Wenhao Chu Rongrong Li School of Information Engineering China University of Geosciences Beijing Beijing China Hebei Key Laboratory of Geospatial Digital Twin and Collaborative Optimization China University of Geosciences Beijing Beijing China Chinese Academy of Surveying and Mapping Beijing China Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province Central China Normal University Wuhan China College of Urban and Environmental Sciences Central China Normal University Wuhan China Department of Information and Communication Academy of Army Armored Forces Beijing China Key Laboratory of Virtual Geographic Environment (Ministry of Education of PRC) Nanjing Normal University Nanjing Jiangsu China International Research Center of Big Data for Sustainable Development Goals Beijing China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing Jiangsu China Institute of Space and Earth Information Science The Chinese University of Hong Kong Shatin New Territories Hong Kong China

Differentiable parameterized learning (dPL) represents a cutting-edge advancement in synergizing process-based models (PBMs) and machine learning to improve both the model prediction and interpretability. However, dPL mainly focuses on refining parameters within a predefined process-based structure, which limits its performance and process understanding to the constraints of existing PBM frameworks. To address this limitation, we propose an extended framework: Differentiable Process and Parameter Learnings (dP2Ls). dP2Ls incorporate regionalized neural networks (NNs) to simultaneously improve both the modeling of process variables and parameter learnings. Within dP2Ls, the EXP-HYDRO model serves as a differentiable physical backbone, where the potential evapotranspiration (PET) process is modeled using a regionalized long short-term memory (LSTM), while the dPL strategy is independently applied to learning hydrological parameters. Experiments with runoff prediction in ungauged catchments across the contiguous United States revealed that: (1) The median Nash-Sutcliffe efficiency (NSE) of regionalized dP2Ls in ungauged catchments surpassed that of all models using NNs for either process learning or parameter learning and approached the LSTM model (the median NSE differs by only 0.021); (2) Regionalized dP2Ls enhanced the interpretability of some intermediate variables by jointly learning the process variables and parameters, resulting in the correlation coefficients of snowpack and ET fitting the estimation products increased by 0.022 and 0.026, respectively; (3) The embedded regionalized LSTM outputs more interpretable PET than EXP-HYDRO, demonstrating the capability of NNs to improve traditional process understanding. In summary, this study demonstrates the advantages of dP2Ls in regionalized modeling, overcoming the limitations of traditional process-based structures through flexible NN configurations, and providing a reliable pathway for the future in-depth diagno

关键词： Catchments

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