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Acta meteorologica Sinica 1991年第3期5卷 387-395页

作者：刘玉洁方宗义 Satellite Meteorology Center State Meteorological AdministrationBeijing

This paper summarizes the characteristics of Fengyun satellite and the properties of the data transmission, and also gives a brief introduction to the data acquisition and processing system. At last, it describes the ... 详细信息

关键词： meteorological satellite data processing application of products

The preliminary processing and analysis of LPR Channel-2B data from Chang'E-3

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Science China(Physics,Mechanics & Astronomy) 2014年第12期57卷 2346-2353页

作者： ZHAO Na ZHU Pei Min YANG Ke Si YUAN Yue Feng GUO Shi Li Institute of Geophysics and Geomatics China University of Geosciences Hubei Subsurface Multi-scale Imaging Lab China University of Geosciences College of Resources and Environmental Sciences Henan Institute of Engineering

The Lunar Penetrating Radar(LPR)carried by Chang’E-3 has imaged the shallow subsurface of the landing site at the northern Mare *** antenna B of the Channel-2 onboard the LPR(LPR Channel-2B)has collected more than 2000 traces of usable raw *** of the low resolution and noise of the raw data,only a few shallow geological structures are *** improve the resolution and the signal-to-noise ratio of the LPR data,we processed the LPR data including amplitude compensation,filtering,and deconvolution *** processing results reveal that the data processing in this study not only improves the signal-to-noise ratio of the LPR Channel-2B data but also makes the geological structures *** processing results will lay the foundation for the subsequent geological interpretation and physical property inversion of lunar materials.

关键词： data processing Lunar Penetrating Radar Chang'E-3 lunar subsurface Moon

APPLICATION OF GREY SYSTEM THEORY TO processing OF MEASURING data IN REVERSE ENGINEERING

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Transactions of Nanjing University of Aeronautics and Astronautics 2003年第1期20卷 36-41页

作者：平雪良周儒荣安鲁陵南京航空航天大学CAD/CAM工程研究中心

The processing of measuri ng data plays an important role in reverse engineering. Based on grey system the ory, we first propose some methods to the processing of measuring data in revers e engineering. The measured data usually have some abnormalities. When the abnor mal data are eliminated by filtering, blanks are created. The grey generation an d GM(1,1) are used to create new data for these blanks. For the uneven data sequ en ce created by measuring error, the mean generation is used to smooth it and then the stepwise and smooth generations are used to improve the data sequence.

关键词： reverse engineering gr ey system theory digitization data processing grey generation

An approach for automatic sleep stage scoring and apnea-hypopnea detection

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Frontiers of Computer Science 2012年第2期6卷 230-241页

作者： TimSCHLǔTER StefanCONRAD InstituteofComputerScience HeinrichHeineUniversity40225DtisseldorfGermany

In this article we present an application of data mining to the medical domain sleep research, an approach for automatic sleep stage scoring and apnea-hypopnea detec- tion. By several combined techniques （Fourier and wavelet transform, derivative dynamic time warping, and waveform recognition）, our approach extracts meaningful features （fre- quencies and special patterns like k-complexes and sleep spindles） from physiological recordings containing EEG, ECG, EOG and EMG data. Based on these pieces of in- formation, an ensemble of decision trees is constructed us- ing the principle of bagging, which classifies sleep epochs in their sleep stages according to the rules by Rechtschaf- fen and Kales and annotates occurrences of apnea-hypopnea （total or partial cessation of respiration）. After that, case- based reasoning is applied in order to improve quality. We tested and evaluated our approach on several large public databases from PhysioBank, which showed an overall accu- racy of 95.2% for sleep stage scoring and 94.5% for classify- ing minutes as apneic or non-apneic.

关键词： time series data processing signal processing,feature extraction pattern classification biomedical signalprocessing sleep

Low-field NMR inversion based on low-rank and sparsity restraint of relaxation spectra

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Petroleum Science 2022年第6期19卷 2741-2756页

作者： Si-Hui Luo Li-Zhi Xiao Yan Jin Jiang-Feng Guo Xiao-Bo Qu Zhang-Ren Tu Gang Luo Can Liang College of Artificial Intelligence and College of Petroleum Engineering China University of PetroleumBeijing102249China State Key Laboratory of Petroleum Resources and Prospecting China University of PetroleumBeijing102249China School of Electronic Science and Engineering(National Model Microelectronics College) Xiamen UniversityFujian361005China School of Electronic Science and Engineering National Model Microelectronics College Xiamen University Changzhou Institute of Technology ChangzhouJiangsu213000China

In this paper,we proposed a novel method for low-field nuclear magnetic resonance(NMR)inversion based on low-rank and sparsity restraint(LRSR)of relaxation spectra,with which high quality construction is made possible for one-and two-dimensional low-field and low signal to noise ratio NMR *** this method,the low-rank and sparsity restraints are introduced into the objective function instead of the smoothing *** low-rank features in relaxation spectra are extracted to ensure the local characteristics and morphology of *** sparsity and residual term are contributed to the resolution and precision of spectra,with the elimination of the redundant relaxation *** process of the objective function is designed with alternating direction method of multiples,in which the objective function is decomposed into three subproblems to be independently *** optimum solution can be obtained by alternating iteration and updating *** first,numerical simulations are conducted on synthetic echo data with different signal-to-noise ratios,to optimize the desirable regularization parameters and verify the feasibility and effectiveness of proposed ***,NMR experiments on solutions and artificial sandstone samples are conducted and analyzed,which validates the robustness and reliability of the proposed *** results from simulations and experiments have demonstrated that the suggested method has unique advantages for improving the resolution of relaxation spectra and enhancing the ability of fluid quantitative identification.

关键词： Low-field NMR Inversion method Low-rank and sparsity restraint Relaxation spectra data processing

Review on Security Defense Technology Research in Edge Computing Environment

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Chinese Journal of Electronics 2024年第1期33卷 1-18页

作者： Ke SHANG Weizhen HE Shuai ZHANG National Digital Switching System Engineering and Technological R&D Center

Edge computing, which achieves quick data processing by sinking data computing and storage to the network edge, has grown rapidly along with the Internet of things. The new network architecture of edge computing brings new security challenges. Based on this, this paper investigates the edge computing security literature published in recent years and summarizes and analyzes research work on edge computing security from different attack surfaces. We start with the definition and architecture of edge computing. From the attack surface between device and edge server, as well as on edge servers, the research describes the security threats and defense methods of edge computing. In addition, the cause of the attack and the pros and cons of defense methods is introduced. The challenges and future research directions of edge computing are given.

关键词： Cloud computing Systems architecture Computer architecture Network architecture data processing Security Servers

State of the art in applications of machine learning in steelmaking process modeling

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International Journal of Minerals,Metallurgy and Materials 2023年第11期30卷 2055-2075页

作者： Runhao Zhang Jian Yang State Key Laboratory of Advanced Special Steel School of Materials Science and EngineeringShanghai UniversityShanghai 200444China

With the development of automation and informatization in the steelmaking industry,the human brain gradually fails to cope with an increasing amount of data generated during the steelmaking *** learning technology provides a new method other than production experience and metallurgical principles in dealing with large amounts of *** application of machine learning in the steelmaking process has become a research hotspot in recent *** paper provides an overview of the applications of machine learning in the steelmaking process modeling involving hot metal pretreatment,primary steelmaking,secondary refining,and some other *** three most frequently used machine learning algorithms in steelmaking process modeling are the artificial neural network,support vector machine,and case-based reasoning,demonstrating proportions of 56%,14%,and 10%,*** data in the steelmaking plants are frequently ***,data processing,especially data cleaning,is crucially important to the performance of machine learning *** detection of variable importance can be used to optimize the process parameters and guide *** learning is used in hot metal pretreatment modeling mainly for endpoint S content *** predictions of the endpoints of element compositions and the process parameters are widely investigated in primary *** learning is used in secondary refining modeling mainly for ladle furnaces,Ruhrstahl–Heraeus,vacuum degassing,argon oxygen decarburization,and vacuum oxygen decarburization *** development of machine learning in the steelmaking process modeling can be realized through additional efforts in the construction of the data platform,the industrial transformation of the research achievements to the practical steelmaking process,and the improvement of the universality of the machine learning models.

关键词： machine learning steelmaking process modeling artificial neural network support vector machine case-based reasoning data processing

Machine Learning‑Enhanced Flexible Mechanical Sensing

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Nano-Micro Letters 2023年第4期15卷 190-222页

作者： Yuejiao Wang Mukhtar Lawan Adam Yunlong Zhao Weihao Zheng Libo Gao Zongyou Yin Haitao Zhao Applied Mechanics Laboratory Department of Engineering MechanicsTsinghua UniversityBeijing 100084People’s Republic of China Materials Interfaces Center Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055People’s Republic of China Department of Mechanical and Electrical Engineering Xiamen UniversityXiamen 361102People’s Republic of China School of Mechano‑Electronic Engineering Xidian UniversityXi’an 710071People’s Republic of China Research School of Chemistry Australian National UniversityCanberraACT 2601Australia

To realize a hyperconnected smart society with high productivity,advances in flexible sensing technology are highly ***,flexible sensing technology has witnessed improvements in both the hardware performances of sensor devices and the data processing capabilities of the device’s *** research efforts have been devoted to improving materials,sensing mechanism,and configurations of flexible sensing systems in a quest to fulfill the requirements of future ***,advanced data analysis methods are being developed to extract useful information from increasingly complicated data collected by a single sensor or network of *** learning(ML)as an important branch of artificial intelligence can efficiently handle such complex data,which can be multi-dimensional and multi-faceted,thus providing a powerful tool for easy interpretation of sensing *** this review,the fundamental working mechanisms and common types of flexible mechanical sensors are firstly *** how ML-assisted data interpretation improves the applications of flexible mechanical sensors and other closely-related sensors in various areas is elaborated,which includes health monitoring,human-machine interfaces,object/surface recognition,pressure prediction,and human posture/motion ***,the advantages,challenges,and future perspectives associated with the fusion of flexible mechanical sensing technology and ML algorithms are *** will give significant insights to enable the advancement of next-generation artificial flexible mechanical sensing.

关键词： Flexible mechanical sensors Machine learning Artificial intelligence data processing

Outlier screening for ironmaking data on blast furnaces

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International Journal of Minerals,Metallurgy and Materials 2021年第6期28卷 1001-1010页

作者： Jun Zhao Shao-fei Chen Xiao-jie Liu Xin Li Hong-yang Li Qing Lyu College of Metallurgy Northeastern UniversityShenyang 110819China Tangshan Branch of HBIS Group Co. Ltd.Tangshan 063020China College of Metallurgy and Energy North China University of TechnologyTangshan 063210China

Blast furnace data processing is prone to problems such as *** overcome these problems and identify an improved method for processing blast furnace data,we conducted an in-depth study of blast furnace *** on data samples from selected iron and steel companies,data types were classified according to different characteristics;then,appropriate methods were selected to process them in order to solve the deficiencies and outliers of the original blast furnace *** interpolation was used to fill in the divided continuation data,the Knearest neighbor(KNN)algorithm was used to fill in correlation data with the internal law,and periodic statistical data were filled by the *** error rate in the filling was low,and the fitting degree was over 85%.For the screening of outliers,corresponding indicator parameters were added according to the continuity,relevance,and periodicity of different ***,a variety of algorithms were used for *** the analysis of screening results,a large amount of efficient information in the data was retained,and ineffective outliers were *** processing of blast furnace big data as the basis of applied research on blast furnace big data can serve as an important means to improve data quality and retain data value.

关键词： blast furnace data missing outliers data processing data mining