检索结果-南通市图书馆

Applied Mathematics 2015年第9期6卷 1620-1629页

作者： Xiuhua Liang Guangming Deng Bin Yan College of Science Guilin University of Technology Guilin China

Utilizing principal component analysis (PCA) and cluster analysis, the standardization, dimension-reduction and de-correlation of multiple evaluation index system for fruit and vegetable nutrition are performed to assign principal component factor based on cluster analysis of loading matrix and combining with actual meaning and evaluation direction of index categories. To evaluate the richness of its nutrition according to the score of nutrition of fruit and vegetable, finally equivalent replacement suggestions are given in different seasons of vegetables & fruits according to the result of clustering. Studies show that principal component cluster method can not only carry on the reasonable classification of multivariate data effectively, but also make reasonable evaluation on the sample object, and provide powerful basis for evaluation of fruits and vegetables’ nutrition.

关键词： principal Component Analysis Cluster Analysis Multivariate Classification

Assessment of the Water Quality in the Ariake Sea Using principal Component Analysis

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Water Resource and Protection 2015年第1期7卷 41-49页

作者： Toshinori Tabata Kazuaki Hiramatsu Masayoshi Harada Department of Agro-Environmental Sciences Graduate School of Bioresource and Bioenvironmental Sciences Kyushu University Fukuoka Japan Department of Agro-Environmental Sciences Faculty of Agriculture Kyushu University Fukuoka Japan

The aim of this study is to investigate the condition of the Ariake Sea, Japan, which has been suffering from severe environmental issues for the past few decades. Water quality data have been generated from several points in this area for over 30 years by the Fukuoka, Saga, Kumamoto, and Nagasaki prefectures. In order to understand the characteristics of this sea, principal component analysis (PCA) was utilized using 11 water quality parameters;transparency, temperature, salinity, dissolved oxygen (DO), chemical oxygen demand (COD), dissolved inorganic nitrogen (DIN), ammonium-nitrogen (NH4+-N), nitrate-nitrogen (NO3--N), nitrite-nitrogen (NO2--N), phosphate-phosphorus, (PO43--P) and silica. PCA conveyed the amount of nutrients originating from the river, the organic pollution level, and seasonal changes. Subsequently, principal component scores were calculated for each point. It was concluded that the Ariake Sea environment has been affected by two main factors, which are the nutrients from the Chikugo River and anticlockwise tidal residual flow. These two factors must be considered for the environmental restoration of the Ariake Sea.

关键词： Ariake Sea principal Component Analysis Nutrients Anticlockwise Tidal Residual Flow

Operation Risk of Rural Cooperative Economic Organization Based on principal Component Analysis——A Case of Sichuan Province,China

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Asian Agricultural Research 2010年第3期2卷 22-26页

作者： JIA Xian-wei,FENG Ling School of Economics and Management,Sichuan Agricultural University,Yaan 625014,China

Based on the previous research,a total of 10 risk indices are selected according to the specific situation in Sichuan Province,such as natural risk,resource constraint risk,internal management risk,capital risk,technical input and output risk,market risk,contract credit risk,property risk and policy support risk.According to the questionnaire survey by cooperative economic organization of Sichuan Province,principal component analysis is used to analyze the importance and influence degree of the operation risk of rural cooperative economic organization.Result shows that in the operation of cooperative economic organization,management risk has greater influence degree and importance degree,which should be paid special attention to.Capital,property,price fluctuation,and quality of members have great degree of risk and are paid little attention to.The technical input and output risk and the contract credit risk have relatively low impact on the operation risk of cooperative economic organization and their importance is relatively low.Therefore,we should continue to maintain its advantages.Nature,resource constraint and policy support have relatively high risk awareness and low influence degree,belonging to the factors with priority attention which are controlled to some extent.Finally,suggestions are put forward,such as enhancing the management level of cooperative economic organizations,strengthening the farmer's risk awareness,and improving the risk aversion ability of cooperative economic organization.

关键词： Type of risk Influence degree principal component

Short-Term Financial Time Series Forecasting Integrating principal Component Analysis and Independent Component Analysis with Support Vector Regression

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Computer and Communications 2018年第3期6卷 51-67页

作者： Utpala Nanda Chowdhury Sanjoy Kumar Chakravarty Md. Tanvir Hossain Department of Computer Science and Engineering University of Rajshahi Rajshahi Bangladesh

Financial time series forecasting could be beneficial for individual as well as institutional investors. But, the high noise and complexity residing in the financial data make this job extremely challenging. Over the years, many researchers have used support vector regression (SVR) quite successfully to conquer this challenge. In this paper, an SVR based forecasting model is proposed which first uses the principal component analysis (PCA) to extract the low-dimensional and efficient feature information, and then uses the independent component analysis (ICA) to preprocess the extracted features to nullify the influence of noise in the features. Experiments were carried out based on 16 years’ historical data of three prominent stocks from three different sectors listed in Dhaka Stock Exchange (DSE), Bangladesh. The predictions were made for 1 to 4 days in advance targeting the short term prediction. For comparison, the integration of PCA with SVR (PCA-SVR), ICA with SVR (ICA-SVR) and single SVR approaches were applied to evaluate the prediction accuracy of the proposed approach. Experimental results show that the proposed model (PCA-ICA-SVR) outperforms the PCA-SVR, ICA-SVR and single SVR methods.

关键词： Financial Time Series Forecasting Support Vector Regression principal Component Analysis Independent Component Analysis Dhaka Stock Exchange

Examining Gait Patterns after Total Knee Arthroplasty Using Parameterization and principal Component Analysis

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Open Journal of Orthopedics 2013年第2期3卷 156-163页

作者： Kevin E. Roy Victoria L. Chester Chris A. McGibbon Andrew and Marjorie McCain Human Performance Laboratory Faculty of Kinesiology University of New Brunswick Fredericton Canada

The use of parameterization in assessing gait waveforms has been widely accepted, although it is recognized that this approach excludes the majority of information contained in the waveform. Waveform analysis techniques, such as principal component analysis (PCA), have gained popularity in recent years as a more effective approach to extracting important information from human movement waveforms, but are more challenging to interpret. Few studies have compared these two different approaches to determine which yields the most relevant information. This study compared the kinematic patterns during gait of six total knee arthroplasty (TKA) subjects (10 TKA knees), to a group of 10 age-matched asymptomatic control subjects (19 control knees). An eight-camera Vicon M-cam system was used to track movement and compute joint angles. Group differences in parameterization (max and min peaks) values and principal component scores were tested using one-way ANOVA and Kruskal-Wallis tests. Using parameterization, the TKA group was characterized by reduced hip extension, increased hip flexion, increased anterior pelvic tilt, increased trunk tilt, and reduced sagittal ankle angles compared to the control group. Waveform analysis, by means of PCA, showed-magnitude shifts in sagittal ankle waveforms between groups, rather than solely reporting differences in peaks. Waveform analysis also indicated a significant shift in the magnitude of the entire waveform for hip angles, pelvic tilt, and trunk tilt, indicating no change in range of motion between groups, but rather a change in the way in which range of motion is achieved at the hip. This study has identified several gait variables that were significantly different between the TKA and control groups. Our results suggest that waveform analysis is effective at identifying magnitude shifts as sources of variability between groups, which would not necessarily be analyzed using conventional parameterization techniques unless one knew a priori where the variability would exist.

关键词： Total Knee Arthroplasty (TKA) principal Component Analysis (PCA) Parameterization Gait Analysis

ArcGIS<sup>TM</sup>and principal Component Analysis of Probe Data to Micro-Map Minerals in Round Top Rare Earth Deposit

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Advances in Materials Physics and Chemistry 2020年第2期10卷 39-52页

作者： Lorraine M. Negrón Margaret Piranian Maria A. Amaya Daniel Gorski Nicholas E. Pingitore

Rare earth elements (REEs), especially heavy rare earth elements (HREEs), are in demand for their current and emerging applications in advanced technologies. Here we perform computer-driven micro-mapping at the millimeter scale of the minerals that comprise Round Top Mountain, in west Texas, USA. This large rhyolite deposit is enriched in HREEs and such other critical elements as Li, Be, and U. Electron probe microanalysis of 2 × 2 mm areas of thin sections of the rhyolite produced individual maps of 16 elements. These were superimposed to generate a 16-element composition at each pixel. principal components analysis of elements at each pixel identified the specific mineral at that site. The pixels were then relabeled as the appropriate minerals, thereby producing a single mineral map. The overall mineral composition of the 7 studied samples compared favorably with prior analyses of the Round Top deposit available in the literature. Likewise the range of porosity in the maps was consistent with that of previous direct measurements by water saturation. This new statistical and GIS-based technique provides a robust and unbiased approach to electron microprobe mapping. The study further showed that the high-value yttrofluorite grains exhibited little tendency to cluster with other late-stage trace minerals and that the samples extended the previously documented overall homogeneity of the deposit at field scale to this microscopic scale.

关键词： ArcGISTM Electron Probe Microanalysis principal Component Analy-sis Rare Earths Yttrofluorite X-Ray Map Analyzer Round Top

The principal Component Transform of Parametrized Functions

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Applied Mathematics 2017年第4期8卷 453-475页

作者： Ilia Zabrodskii Arcady Ponosov Department of Science and Technology Norwegian University of Life Sciences &Aring s Norway

Many advanced mathematical models of biochemical, biophysical and other processes in systems biology can be described by parametrized systems of nonlinear differential equations. Due to complexity of the models, a problem of their simplification has become of great importance. In particular, rather challengeable methods of estimation of parameters in these models may require such simplifications. The paper offers a practical way of constructing approximations of nonlinearly parametrized functions by linearly parametrized ones. As the idea of such approximations goes back to principal Component Analysis, we call the corresponding transformation principal Component Transform. We show that this transform possesses the best individual fit property, in the sense that the corresponding approximations preserve most information (in some sense) about the original function. It is also demonstrated how one can estimate the error between the given function and its approximations. In addition, we apply the theory of tensor products of compact operators in Hilbert spaces to justify our method for the case of the products of parametrized functions. Finally, we provide several examples, which are of relevance for systems biology.

关键词： principal Component Analysis Discretization of Functions Metamodeling Latent Parameters

Olive Mill Waste Water Management Study by Using principal Component Analysis

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

International Journal of Geosciences 2013年第2期4卷 444-453页

作者： Houda Sahnoun Mohamed Moncef Serbaji Boubaker Karray Khaled Medhioub Département des Sciences de la Terre Faculté des Sciences de Sfax Université de Sfax Sfax Tunisie Ecole Nationale d’Ingénieurs de Sfax-LARSEN Université de Sfax Sfax Tunisie Institut Préparatoire Aux Etudes d’Ingénieurs de Sfax Université de Sfax Sfax Tunisie Institution de la Recherche et l’Enseignement Supérieur Agricoles Sfax Tunisie

Olive mill waste water (OMWW) is a by-product issued after triturating olives. In Sfax, its management is different from urban to farming area. In this paper we treat it through a statistical analysis study during the season 2005-2006. principal Component Analysis (PCA) and Hierarchical Classification (HC) methods are carried out on this work. Applied to variables issued from an exhaustive questionnaire including 274 mills, four principal Components (PCs) are found to be significant, explaining 67% of the total variance. The coordinates of the 13 active variables retained by PCA were used to create a typology relative to the OMWW management and offered 7 groups of individuals which have the same characteristics, explaining 70% of the total inter-variance. This study showed that OMWW management in farming area could causes environmental problems because oleifactors haven’t controlled tanks and could evacuated OMWW on soil (causing oil deposit, waterproofing and possible asphyxia) or on public sewage network (causing corrosion, flow reduction). So, mills transfer from urban to farming areas in the form of agro-industrial complex is needed in the Sfax region.

关键词： Olive Mill Waste Water Management Sfax principal Component Analysis Hierarchical Classification

The Water Quality Evaluation in Balihe Lake Based on principal Component Analysis

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Geoscience and Environment Protection 2019年第8期7卷 38-48页

作者： Lei Zhang Meifang Zhong Yixin Xu Zhongyu Wang Hai Huang Research Center for Engineering Ecology and Nonlinear Science North China Electric Power University Beijing China

The water pollution situation in Balihe Lake, the biggest tributary of Shaying River Basin in Anhui Province, China, has brought a huge pressure on the improvement of water quality in Huai River. On October 16th, 2017, 11 major pollution indexes were observed at 15 sampling points in Balihe Lake. Based on the data experimentally measured, the water quality in Balihe Lake was analyzed utilizing the principal Component Analysis (PCA) of SPSS. The result suggested that the major components were oxygenated pollutants, water eutrophication pollutants and ammonia nitrogen, in which oxygenated pollutants played a dominant role. In addition, the upper part of Balihe Lake suffered serious situation and needed a focus on oxygenated pollutants.

关键词： Balihe Lake Water Quality principal Component Analysis Environmental Protection

Spousal concordance in academic achievements and IQ: A principal component analysis

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Open Journal of Psychiatry 2011年第2期1卷 15-19页

作者： Yue Pan Ke-Sheng Wang 不详

Assortative mating, the tendency for mate selection to occur on the basis of similar traits plays an essential role in understanding the genetic variation on academic achievements and intelligence (IQ), it is also an important mechanism explaining spousal concordance. We used a subset of The Collaborative Study on the Genetics of Alcoholism sample to study the mating patterns in 84 pairs of spouses from Caucasian families with their academic achievements (reading, spelling, arithmetic, vocabulary and comprehension) and IQ (verbal IQ, performance IQ and full scale IQ). Simple correlation analysis showed that 6 of these 8 traits revealed evidence of spousal correlation (P principal component (PC1) of husbands explains 73.61% for the variation in the eight variables, which has high loadings from reading, spelling, arithmetic, verbal IQ and full scale IQ while PC1 of wives explains 72.86% for the variation in the eight variables, which has high loadings from reading, spelling, verbal IQ and full scale IQ. There was highly significant positive correlation between spouses by PC1 (P < 0.0001). The new variable PC1 may be important in spousal concordance and mate selection in society and act upon achievements and intelligence levels.

关键词： Academic Achievements IQ Spousal Concordance principal Component Analysis