Principal Component Eigenvalues Research Articles

Effects of different ratios of organic and inorganic fertilizer on rice yield and soil physicochemical properties under conventional and controlled irrigation conditions

AbstractWater and fertilizer management practices are critical in rice fields. Excessive application of fertilizer adversely affects soil physicochemical properties, consequently decreasing yield. A greenhouse experiment was conducted to determine the effect of high (L1), medium (L2) and low organic fertilizer ratios (L3) as well as chemical fertilizer alone (L4) and no fertilization (L5) under conventional (C) and controlled irrigation (M) on rice yield and soil characteristics. Statistically, the regression equation was developed by using principal component analysis to clarify the influence of different soil quality factors on rice yield. The results showed that controlled irrigation could increase soil temperature (ST), electrical conductivity (EC) and nitrogen content while decreasing soil pH. The application of organic fertilizer enhanced ST and decreased EC. Under equal nitrogen application conditions, rice yield increased with organic fertilizer application, and it was highest under L1, which was 2.7% higher than L4 and 22% higher than L5. In the case of irrigation methods, controlled irrigation application increased yield by 6% compared to conventional irrigation. The multiple linear regression equation showed that the cumulative contribution of variance of the two principal component eigenvalues reached 75%, among which soil pH, total nitrogen (TN) content and nitrate nitrogen (NO3−‐N) contributed positively to yield, while ammonium nitrogen (NH4+‐N), soil organic carbon (SOC), EC and ST showed negative effects on rice yield. Thus, our study findings can provide a scientific basis for improving management practices.

Rapid registration method by using partial 3D point clouds

Due to the rapid development of three-dimensional laser technique, the registration algorithm attracts more attention in industrial application. In this paper, a rapid registration method of 3D point clouds is proposed to improve the efficiency of reverse engineering. The method is based on weighted principal analysis method and re-weighted iterative closest point algorithm. The pipeline consists three stages: coarse alignment, data simplification, and fine alignment. During the first phase, a weighted principal component analysis method is used to calculate original transformation matrices between model data and scene data. In the second part, principal component eigenvalue analysis scheme is implemented to filter the redundant points of datasets. This procedure could reduce the input number of fine registration and refine outliers. Finally, the fine registration is obtained by a weighted iterative closest point algorithm based on Cauchy's and Welsch's function using the partial points obtained in the second phase. The experimental results and comparison experiments are utilized to demonstrate the validity and effectiveness of the proposed matching algorithm.

Principal Component Analysis and Prediction of Students’ Physical Health Standard Test Results Based on Recurrent Convolution Neural Network

The recurrent convolutional neural network is an advanced neural network that integrates deep structure and convolution calculation. The feedforward neural network with convolution operation and deep structure is an important method of deep learning. In this paper, the convolutional neural network and the recurrent neural network are combined to establish a recurrent convolutional neural network model composed of anomalies, LSTM (Long Short‐Term Memory), and CNN. This study combines the principal component analysis method to predict and analyze the test results of students’ physical fitness standards. The innovation lies in the introduction of the function of the recurrent convolutional network and the use of principal component analysis to conduct qualitative research on seven evaluation indicators that reflect the three aspects of students’ physical health. The results of the study clearly show that there is a strong correlation between some indicators, such as standing long jump and sitting bends which may have a strong correlation. The first principal component eigenvalue has the highest contribution rate, which mainly reflects the five indicators of standing long jump, sitting forward bend, pull‐up, 50 m sprint, and 1000 m long‐distance running. This shows that the physical fitness indicators have a great impact on the physical health of students, which also reflects the current status of students’ physical fitness problems. The results of principal component analysis are scientific and reasonable.

The characteristics analysis of strain variation associated with Wenchuan earthquake using principal component analysis

Borehole strainmeters that are installed deeply into bedrock are capable of recording both continuous stress and strain measurements, and have consequently become an important tool for monitoring crustal deformation. A YRY-4 borehole strainmeter installed at the Guza Station recorded anomalous changes in borehole strain data preceding the Wenchuan earthquake on May 12, 2008 (UTC) (=8.0). We apply principal component analysis (PCA) to analyze borehole strain data from the Guza Station. The first principal component eigenvalues and eigenvectors are calculated. The fitted results of the cumulative number of anomalous eigenvalues demonstrate that an acceleration occurred approximately 4 months before the earthquake (from January 2008). The results of the combined eigenvalue and eigenvector analyses show that the spatial distribution of eigenvectors and accelerated occurrence of eigenvalue anomalies represents the stress evolution characteristics of the fault from a steady state to a sub-instability state in rock experiments. We tentatively infer that this process may also be linked to the preparation phase of a large earthquake.

Monitoring the effects of land consolidation on the ecological environmental quality based on remote sensing: A case study of Chaohu Lake Basin, China

As a kind of activity to break down the original land ecosystem, land consolidation causes the ecosystem to undergo a process of “stability-unstable-stable”. The Chaohu Lake Basin is a typical traditional agricultural planting area and extensive implementation of large-scale land consolidation projects. By monitoring the impact of land consolidation activities in a specific time period in the basin on the project areas and surrounding environment, the spatial and temporal impacts of large-scale land consolidation on regional eco-environmental quality are analyzed. This study mainly monitors crop growth, soil moisture and temperature changes in the land consolidation project area, and the corresponding changes in the surrounding environment. The response of ecological environment factors caused by land consolidation can be embodied in indicators such as humidity, greenness, heat and dryness. Four indicators of NDVI, WET, LST, and NDBSI were selected, using principal component analysis, monitoring the environmental quality changes in the land consolidation areas of Chaohu Lake Basin. Result shows that the ecological environmental quality of the study area exhibits long-term convergence and short-term divergence, and the correlation between the ecological indicators is not significant. The average contribution rate of the first principal component eigenvalue is 67.94 %. The implementation period of the land consolidation project will seriously interfere with the ecological environment, but it can be restored within 3–5 years after implementation, and the ecological environmental quality of the project area will continue to improve. Land consolidation not only can improve the quality of the ecological environment in the consolidation area, but also can promote the surrounding environment. The findings provide theoretical basis and decision guidance for the improvement of land consolidation planning and design (reconstruction) in China, and offer suggestions on strategy-oriented support and scientific basis for promoting the long-term development of land consolidation worldwide.

Extracting borehole strain precursors associated with the Lushan earthquake through principal component analysis

A YRY-4 borehole strainmeter installed at the Guza Station recorded anomalous changes in borehole strain data preceding the Lushan earthquake on April 20, 2013 (UTC) ( =7.0). To identify earthquake-induced abnormal strain changes, we apply principal component analysis (PCA) for the first time to analyse the borehole strain data from the Guza Station. The first principal component eigenvalues and eigenvectors demonstrate that the anomalous days are mainly concentrated within two time periods:1) October 25-December 30, 2012, and 2) April 15-19,2013. A combined eigenvalue and eigenvector analysis reveals that the abnormal days exhibit a clustered distribution that is aggregated in the same location for both periods, intuitively indicating that there is a forceful correlation between the two anomalies. We infer that a similar process contributed to the formation of both anomalies and that these two anomalies are both earthquake precursors associated with the Lushan earthquake. These findings also indicate that the PCA approach exhibits potential for the extraction of earthquake precursor anomalies.

Automatic Detection and Classification of Pole-Like Objects for Urban Cartography Using Mobile Laser Scanning Data.

Mobile laser scanning (MLS) is a modern and powerful technology capable of obtaining massive point clouds of objects in a short period of time. Although this technology is nowadays being widely applied in urban cartography and 3D city modelling, it has some drawbacks that need to be avoided in order to strengthen it. One of the most important shortcomings of MLS data is concerned with the fact that it provides an unstructured dataset whose processing is very time-consuming. Consequently, there is a growing interest in developing algorithms for the automatic extraction of useful information from MLS point clouds. This work is focused on establishing a methodology and developing an algorithm to detect pole-like objects and classify them into several categories using MLS datasets. The developed procedure starts with the discretization of the point cloud by means of a voxelization, in order to simplify and reduce the processing time in the segmentation process. In turn, a heuristic segmentation algorithm was developed to detect pole-like objects in the MLS point cloud. Finally, two supervised classification algorithms, linear discriminant analysis and support vector machines, were used to distinguish between the different types of poles in the point cloud. The predictors are the principal component eigenvalues obtained from the Cartesian coordinates of the laser points, the range of the Z coordinate, and some shape-related indexes. The performance of the method was tested in an urban area with 123 poles of different categories. Very encouraging results were obtained, since the accuracy rate was over 90%.

An eigen-analysis of the relationships between model structure, discrete data, measurement error and resulting parameter identification distributions

Practical rather than structural identifiability is often the determining factor whether effective parameter identification is possible in a physiological model. This paper presents analysis into relationships between the population outcomes, and the original model and data properties as part of ongoing research into a deterministic approach to evaluate a-priori identifiability.Data size, output noise variance and true parameter values were varied for a simple 2-parameter model with a linear regression equation Ax = b for discrete data points. Principal Component Analysis of a Monte Carlo simulation was compared to these varied properties and the eigendecomposition of ATA.Principal component vectors were found to be parallel with ATA eigenvectors and the eigenvalues were inversely related. Principal component eigenvalues decreased in inverse proportion to data size, were scaled by the sum of squared parameter values and noise variance. ATA eigenvalues on the other hand were unchanged by output noise and parameter value, but increased in linear, rather than inverse proportion, to data size. The ratio of principal component eigenvalues to each other was affected by data size and some parameter values, while the ATA eigenvalue ratio was affected by data size only.Deterministic relationships have been found between population parameter identification outcomes, model properties and data. If all of the factors determining principle components can be calculated then population variance can be estimated from a single set of data, facilitating confidence of individual outcomes and evaluation of practical identifiability.

A principal component analysis of the U.S. News & World Report tier rankings of colleges and universities

This paper utilizes principal component regression analysis to examine the relative contributions of 11 ranking criteria used to construct the U.S. News & World Report (USNWR) tier rankings of national universities. The main finding of this study is that the actual contributions of the 11 ranking criteria examined differ substantially from the explicit USNWR weighting scheme because of severe and pervasive multicollinearity among the ranking criteria. USNWR assigns the greatest weight to academic reputation. However, generated first principal component eigenvalues of tier rankings indicate that the most significant ranking criterion is the average SAT scores of enrolled students. This result is significant since admission requirements are policy variables that indirectly affect, for example, admission applications, yields, enrollment, retention, tuition-based revenues, and alumni contributions.

The efficient cross-validation of principal components applied to principal component regression

The cross-validation of principal components is a problem that occurs in many applications of statistics. The naive approach of omitting each observation in turn and repeating the principal component calculations is computationally costly. In this paper we present an efficient approach to leave-one-out cross-validation of principal components. This approach exploits the regular nature of leave-one-out principal component eigenvalue downdating. We derive influence statistics and consider the application to principal component regression.