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Purine alkaloids in tea plants:component,biosynthetic mechanism and genetic variation

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Beverage Plant Research 2022年第1期2卷 94-102页

作者： Shuran Zhang Jiqiang Jin Jiedan Chen Sezai Ercisli Liang Chen Key Laboratory of Biology Genetics and Breeding of Special Economic Animals and PlantsMinistry of Agriculture and Rural AffairsTea Research InstituteChinese Academy of Agricultural SciencesHangzhou 310008China Department of Horticulture Faculty of AgricultureAtaturk UniversityErzurum 25240Turke

The purine alkaloids,including caffeine,theobromine,and theacrine,are one of the most important quality and functional components of tea plants and commercial *** this paper,we review the component,biosynthetic mechanism and genetic variation of purine alkaloids in tea *** content of caffeine and theobromine in 403 accessions Chinese tea germplasms core collection preserved in the National Germplasm Hangzhou Tea Repository were analyzed using High Performance Liquid Chromatography(HPLC).The purine alkaloid profiles of different tea varieties,germplasm types,geographical origin,seasons,were *** naturally low caffeine or caffeine-free,high theobromine or high theacrine germplasms were identified and the possible biosynthetic mechanism of hypernormal purine alkaloid content in tea plants were partially *** Cleaved Amplified Polymorphic Sequences(CAPS)DNA markers were developed for the identification of purine alkaloid *** potential application of genetic engineering and DNA markers developed based on the low caffeine TCS1(tea caffeine synthesis gene)alleles for marker assisted selection(MAS),and cocoa tea and kucha in low caffeine/high theacrine tea cultivar breeding programs were also reviewed.

关键词： mechanism component urine

REANALYSIS OF PRINCIPAL component ANALYSIS TO RESUME PAST VEGETATION AND ENVIRONMENT——Taking Fenzhuang Profile in Beijing as an Example

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Chinese Geographical Science 1998年第3期8卷 34-40页

作者：张佳华

Ｉ．ＩＮＴＲＯＤＵＣＴＩＯＮＡｔｐｒｅｓｅｎｔ，ｇｌｏｂａｌｃｌｉｍａｔｅｃｈａｎｇｅｈａｖｅｂｅｅｎｗｉｄｅｌｙｐａｉｄａｔｅｎｔｉｏｎｔｏｉｎｔｅｒｎａｔｉｏｎａｌｙ．Ｃｌｉｍａｔｉｃａｎｄｅｎｖｉｒｏｎｍｅｎ... 详细信息

Ｉ．ＩＮＴＲＯＤＵＣＴＩＯＮＡｔｐｒｅｓｅｎｔ，ｇｌｏｂａｌｃｌｉｍａｔｅｃｈａｎｇｅｈａｖｅｂｅｅｎｗｉｄｅｌｙｐａｉｄａｔｅｎｔｉｏｎｔｏｉｎｔｅｒｎａｔｉｏｎａｌｙ．Ｃｌｉｍａｔｉｃａｎｄｅｎｖｉｒｏｎｍｅｎｔａｌｃｈａｎｇｅｓｆｒｏｍｌｏｃ...

关键词： Fenzhuang in Beijing past flora and environment principal component analysis spore pollen

Tool Health Condition Recognition Method for High Speed Milling of Titanium Alloy Based on Principal component Analysis (PCA) and Long Short Term Memory (LSTM)

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Journal of Donghua University(English Edition) 2019年第4期36卷 364-368页

作者： YANG Qirui XU Kaizhou ZHENG Xiaohu XIAO Lei BAO Jinsong College of Mechanical Engineering Donghua UniversityShanghai 201600China Shanghai Space Propulsion Technology Research Institute Shanghai 201100China

The healthy condition of the milling tool has a very high impact on the machining quality of the titanium ***,it is important to recognize the healthy condition of the tool and replace the damaged cutter at the right *** order to recognize the health condition of the milling cutter,a method based on the long short term memory(LSTM)was proposed to recognize tool health state in this *** various signals collected in the tool wear experiments were analyzed by time-domain statistics,and then the extracted data were generated by principal component analysis(PCA)*** preprocessed data extracted by PCA is transmitted to the LSTM model for *** with back propagation neural network(BPNN)and support vector machine(SVM),the proposed method can effectively utilize the time-domain regulation in the data to achieve higher recognition speed and accuracy.

关键词： health condition recognition milling tool principal component analysis(PCA) long short term memory(LSTM)

Estimation on principal component of multi-collinearity Gauss-Markov model based on minimum description length

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中国有色金属学会会刊：英文版 2005年第S1期15卷 153-155页

作者： SHI Yu-feng~(1, 2) (1. Shandong University of Technology, Zibo 255049, China 2. Key Laboratory of Geospace Environment and Geodesy, Ministry of Education, Wuhan University, Wuhan 430079, China)

Gauss-Markov model is frequently used in data analysis; the analysis and estimation of its parameters is always a hot issue. Based on the information theory and from the viewpoint of optimal information on description—minimum description length, this paper discusses a case: where there is multi-collinearity in the coefficient matrix, principal component estimation is used to estimate and select the original parameters, so as to reduce its multi-collinearity and improve its credibility. From the viewpoint of minimum description length, this paper discusses the approach of selecting principal components and uses this approach to solve a practical problem.

关键词： minimum description length Gauss-Markov model multi-collinearity principal component estimation

A Kernel Time Structure Independent component Analysis Method for Nonlinear Process Monitoring

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Chinese Journal of Chemical Engineering 2014年第Z1期22卷 1243-1253页

作者：蔡连芳田学民张妮 College of Information and Control Engineering China University of Petroleum

Kernel independent component analysis(KICA) is a newly emerging nonlinear process monitoring method,which can extract mutually independent latent variables called independent components(ICs) from process variables. However, when more than one IC have Gaussian distribution, it cannot extract the IC feature effectively and thus its monitoring performance will be degraded drastically. To solve such a problem, a kernel time structure independent component analysis(KTSICA) method is proposed for monitoring nonlinear process in this paper. The original process data are mapped into a feature space nonlinearly and then the whitened data are calculated in the feature space by the kernel trick. Subsequently, a time structure independent component analysis algorithm, which has no requirement for the distribution of ICs, is proposed to extract the IC ***, two monitoring statistics are built to detect process faults. When some fault is detected, a nonlinear fault identification method is developed to identify fault variables based on sensitivity analysis. The proposed monitoring method is applied in the Tennessee Eastman benchmark process. Applications demonstrate the superiority of KTSICA over KICA.

关键词： Process monitoring Independent component analysis Kernel trick Time structure Fault identification

Spatial Correlation Anomalies of the Diurnal Variation of the Geomagnetic Vertical component before the Yingjiang MS 6.1 and Ludian MS 6.5 Earthquakes

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Earthquake Research in China 2018年第4期32卷 521-532页

作者： Dai Yong Feng Zhisheng Yang Yanming Gegen Earthquake Agency of Inner Mongolia Autonomous Region Hohhot 010010China Jiangsu Earthquake Agency Nanjing 210014China

Significant anomalies were observed at the geomagnetic stations in the southwest region of China before the Yingjiang M_S6. 1 earthquake and the Ludian M_S6. 5 earthquake in *** processed the geomagnetic vertical component diurnal variation data by the spatial correlation method. The results show that during the period from April 1 to May 20,2014,there existed quasi-synchronous decrease changes in the coefficient curves between the five geomagnetic stations of Guiyang,Hechi,Nanshan,Muli,Yongning and Xinyi and Hongshan stations. Furthermore,there was a high gradient zone in the normalized correlation coefficient contour map with background values removed. The epicenters of the Yingjiang M_S6. 1 earthquake and the Ludian M_S6. 5 earthquake are located in the gradient zone or near the gradient zone.

关键词： Yingjiang MS6.1 earthquake Ludian MS6.5 earthquake Geomagnetism Vertical component Abnormal variation

A Distributed Web GIS Application Based on component Technology and Fractal Image Compression

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武汉理工大学学报 2006年第S3期28卷 924-928页

作者： HAN Jie State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering,HoHai University,Nanjing 210098,China, State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering HoHai UniversityNanjing 210098China

Geographic information system(GIS)technology is a combination of computer’s graphic and database to store and process spatial *** to the users’ demands,GIS exports the exact geographic information and related in- formation for users with map and description through associating geographic place and related *** on the existing pop- ular technology,this paper presents a distributed web GIS application based on component technology and fractal image compres- *** presents the basic framework of the proposed system at first,and then discusses the key technology of implementing this sys- tem;finally it designs a three-layer WEB GIS instance using VC++ATL based on Geo *** example suggests the proposed design is correct,feasible and valid.

关键词： geographic information system web technology component technology fractal image compression

Fault Isolation by Partial Dynamic Principal component Analysis in Dynamic Process

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Chinese Journal of Chemical Engineering 2006年第4期14卷 486-493页

作者：李荣雨荣冈 National Key Laboratory of Industrial Control Technology Institute of Advanced Process Control Zhejiang Uni-versity Hangzhou 310027 China

Principal component analysis (PCA) is a useful tool in process fault detection, but offers little support on fault isolation. In this article, structured residual with strong isolation property is introduced. Although it is easy to get the residual by transformation matrix in static process, unfortunately, it becomes hard in dynamic process under control loop. Therefore, partial dynamic PCA(PDPCA) is proposed to obtain structured residual and enhance the isolation ability of dynamic process monitoring, and a compound statistic is introduced to resolve the problem resulting from independent variables in every variable subset. Simulations on continuous stirred tank reactor (CSTR) show the effectiveness of the proposed method.

关键词： fault isolation structured residual dynamic principal component analysis partial principal component

component system regression testing method based on CTAM

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Journal of Southeast University(English Edition) 2005年第3期21卷 282-286页

作者：解凯徐宝文聂长海史亮章晓芳东南大学计算机科学与工程系南京210096

The emphasis of component system regression testing is retesting of the event interaction between updated components and other components in a system.A component system regression testing method based on a new component testing association model （CTAM） is ***,the modification-affected component groups are identified by the impact analysis on CTAM,and each component in this group is assigned with an influence ***,previous test cases are selected according to the influence degree,to generate the minimal regression test *** with traditional methods,CTAM is derived from the statistic on the interactive events that occurred in previous test executions,and focuses on the complicated relationship between components,which is more applicable to the component system regression testing.

关键词： component system testing regression testing test case selection