scholarly journals Regional Terrain Complexity Assessment Based on Principal Component Analysis and Geographic Information System: A Case of Jiangxi Province, China

2020 ◽  
Vol 9 (9) ◽  
pp. 539
Author(s):  
Faming Huang ◽  
Jianbo Yang ◽  
Biao Zhang ◽  
Yijing Li ◽  
Jinsong Huang ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Information System ◽  
Geographic Information System ◽  
Principal Component ◽  
Component Analysis ◽  
Geographic Information ◽  
Jiangxi Province ◽  
Topographic Map ◽  
Pca Method ◽  
Complexity Assessment

Regional terrain complexity assessment (TCA) is an important theoretical foundation for geological feature identification, hydrological information extraction and land resources utilization. However, the previous TCA models have many disadvantages; for example, comprehensive consideration and redundancy information analysis of terrain factors is lacking, and the terrain complexity index is difficult to quantify. To overcome these drawbacks, a TCA model based on principal component analysis (PCA) and a geographic information system (GIS) is proposed. Taking Jiangxi province of China as an example, firstly, ten terrain factors are extracted using a digital elevation model (DEM) in GIS software. Secondly, PCA is used to analyze the information redundancy of these terrain factors and deal with data compression. Then, the comprehensive evaluation of the compressed terrain factors is conducted to obtain quantitative terrain complexity indexes and a terrain complexity map (TCM). Finally, the TCM produced by the PCA method is compared with those produced by the slope-only, the variation coefficient and K-means clustering models based on the topographic map drawn by the Bureau of Land and Resources of Jiangxi province. Meanwhile, the TCM is also verified by the actual three-dimensional aerial images. Results show that the correlation coefficients between the TCMs produced by the PCA, slope-only, variable coefficient and K-means clustering models and the local topographic map are 0.894, 0.763, 0.816 and 0.788, respectively. It is concluded that the TCM of the PCA method matches well with the actual field terrain features, and the PCA method can reflect the regional terrain complexity characteristics more comprehensively and accurately when compared to the other three methods.

scholarly journals Metodología para la zonificación agroclimática de la región de La Mojana mediante el sistema de información geográfica ARC/Info

1998 ◽  
Vol 2 (2) ◽  
pp. 19
Author(s):  
Carlos Terán ◽  
Carlos Jimenez ◽  
Carlos González ◽  
Edgar Villaneda
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Information System ◽  
Geographic Information System ◽  
Temporal Variation ◽  
Principal Component ◽  
Geographic Information ◽  
Inverse System ◽  
Weighted Interpolation ◽  
El Sistema

<p>El estudio desarrolló una metodología objetiva de zonificación agroclimática mediante el uso del sistema de información geográfica (SIG) ARC/Info<sup>®</sup>. Se consideró la variación espacio-temporal de los elementos climáticos y espaciales del suelo y la vegetación pre­valecientes en la región de La Mojana (Colombia), para lo cual se empleó la información de 30 estaciones pluviométricas, una estación pluviográfica y 13 estaciones climáticas; para el procesa miento de los datos se combinaron técnicas de agrupamiento estadístico-matemáticas (análisis de Cluster y de Componentes Principales). Toda la información se desplegó en el sistema de información geográfica ARC/Info<sup>®</sup>, con celdas de 250 x 250 m<sup>2</sup> (6.25 ha), y se interpoló mediante el algoritmo denominado "distancia inversa ponderada" propuesto por Watson y Philip en 1985. La zonificación se efectuó teniendo en cuenta los excesos de precipitación derivados del balance hídrico y que se producen durante el período de lluvias; estos excesos se presentaron en las décadas 12 a 33. Con base en dos mapas, se creó una matriz geográfica en donde cada mapa representa la variación espacial de los excesos de precipitación en una década. A esta matriz se aplicó la técnica de Análisis de Componentes Principales (ACP), escogiéndose aquellos que presentaron la mayor variabilidad. Después, se aplicó el Análisis de Cluster usando el método de isocluster, para producir la zonificación final.</p><p> </p><p><strong>Methodology for agroclimatic classification at La Mojana Region in Colombia with the Geographic Information System ARC/Info</strong></p><p>A methodology for agroclimatic classification was developed in La Mojana region of Colombia using the Geographic lnformation System ARC/lnfo. Data considering the temporal variation of dimate at different locations, as well as temporal changes in soils and cover vegetation of La Mojana were used for this purpose. Processing of data was performed using both, Cluster Analysis and the principal component analysis (PCA). The information collected was incorporated into the ARC/Info program, by means of the algorithm named "inverse system weighted interpolation". Based on two leading maps which describe 87.2% of the variation in precipitation during two decades, a geographic matrix was developed applying the PCA technique, and selecting those components which exhibited the greatest variability. The final classification for the most representative regions of La Mojana was performed using Cluster Analysis.</p><p> </p>

scholarly journals The Spatial Differences of Employment between the Settlements of Harghita County

2019 ◽  
Vol 15 (1) ◽  
pp. 65-82
Author(s):  
Szilárd Madaras
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Information System ◽  
Geographic Information System ◽  
System Analysis ◽  
Principal Component ◽  
Geographic Information ◽  
Spatial Differences ◽  
Geographic Information System Analysis ◽  
Spearman’S Correlation

Abstract This paper contains the analysis of employment in the settlements of Harghita County, using the GIS (Geographic Information System) analysis, Spearman’s correlation, principal component analysis, and the cluster analysis methods. Based on the database of a set of indicators which describe the demographic, employment, and enterprise dimensions, remarkable spatial differences were observed between the settlements. The principal objectives of the county development plan regarding the employment were analysed, and a discussion took place on the possibilities of employment development in Harghita County.

The Design of Index about Corporate Governance Based on PCA Method

2014 ◽  
Vol 926-930 ◽  
pp. 4085-4088
Author(s):  
Chuan Jun Li
Keyword(s):  
Principal Component Analysis ◽  
Corporate Governance ◽  
Principal Components ◽  
Principal Component ◽  
Component Analysis ◽  
Contribution Rate ◽  
Variance Contribution ◽  
Pca Method

This article uses the PCA method (Principal component analysis) to evaluate the level of corporate governance. PCA is used to analyze the correlation among 10 original indicators, and extract some principal components so that most of the information of the original indicators is extracted. The formulation of the index of corporate governance can be got by calculating the weight based on the variance contribution rate of the principal component, which can comprehensively evaluate corporate governance.

scholarly journals Patch-Based Principal Component Analysis for Face Recognition

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Tai-Xiang Jiang ◽  
Ting-Zhu Huang ◽  
Xi-Le Zhao ◽  
Tian-Hui Ma
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Nearest Neighbor ◽  
Spatial Information ◽  
Principal Component ◽  
Component Analysis ◽  
Two Dimensional ◽  
Face Images ◽  
Pca Method ◽  
Local Spatial Information

We have proposed a patch-based principal component analysis (PCA) method to deal with face recognition. Many PCA-based methods for face recognition utilize the correlation between pixels, columns, or rows. But the local spatial information is not utilized or not fully utilized in these methods. We believe that patches are more meaningful basic units for face recognition than pixels, columns, or rows, since faces are discerned by patches containing eyes and noses. To calculate the correlation between patches, face images are divided into patches and then these patches are converted to column vectors which would be combined into a new “image matrix.” By replacing the images with the new “image matrix” in the two-dimensional PCA framework, we directly calculate the correlation of the divided patches by computing the total scatter. By optimizing the total scatter of the projected samples, we obtain the projection matrix for feature extraction. Finally, we use the nearest neighbor classifier. Extensive experiments on the ORL and FERET face database are reported to illustrate the performance of the patch-based PCA. Our method promotes the accuracy compared to one-dimensional PCA, two-dimensional PCA, and two-directional two-dimensional PCA.

scholarly journals Deteksi Formalin Pada Buah Tomat (Lycopersicum esculentum Mill) Dengan Teknologi Hidung Elektronik (Electronic Nose)

2019 ◽  
Vol 4 (2) ◽  
pp. 359-366
Author(s):  
Irfan Maibriadi ◽  
Ratna Ratna ◽  
Agus Arip Munawar
Keyword(s):  
Principal Component Analysis ◽  
Electronic Nose ◽  
Principal Component ◽  
Component Analysis ◽  
Lycopersicum Esculentum ◽  
Olfactory Ability ◽  
Pca Method ◽  
The Difference ◽  
Electronic Nose Technology

Abstrak,  Tujuan dari penelitian ini adalah mendeteksi kandungan dan kadar formalin pada buah tomat dengan menggunakan instrument berbasis teknologi Electronic nose. Penelitian ini menggunakan buah tomat yang telah direndam dengan formalin dengan kadar 0.5%, 1%, 2%, 3%, 4%, dan buah tomat tanpa perendaman dengan formalin (0%). Jumlah sampel yang digunakan pada penelitian ini adalah sebanyak 18 sampel. Pengukuran spektrum beras menggunakan sensor Piezoelectric Tranducer. Klasifikasi data spektrum buah tomat menggunakan metode Principal Component Analysis (PCA) dengan pretreatment nya adalah Gap Reduction. Hasil penelitian ini diperoleh yaitu: Hidung elektronik mulai merespon aroma formalin pada buah tomat pada detik ke-8.14, dan dapat mengklasifikasikan kandungan dan kadar formalin pada buah tomat pada detik ke 25.77. Hidung elektronik yang dikombinasikan dengan metode principal component analysis (PCA) telah berhasil mendeteksikandungan dan kadar formalin pada buah tomat dengan tingkat keberhasilan sebesar 99% (PC-1 sebesar 93% dan PC-2 sebesar 6%). Perbedaan kadar formalin menjadi faktor utama yang menyebabkan Elektronik nose mampu membedakan sampel buah tomat yang diuji, karena semakin tinggi kadar formalin pada buah tomat maka aroma khas dari buah tomat pun semakin menghilang, sehingga Electronic nose yang berbasis kemampuan penciuman dapat membedakannya.Detect Formaldehyde on Tomato (Lycopersicum esculentum Mill) With Electronic Nose TechnologyAbstract, The purpose of this study is to detect the contents and levels of formalin in tomatoes by using instruments based on Electronic nose technology. This study used tomatoes that have been soaked in formalin with a concentration of 0.5%, 1%, 2%, 3%, 4%, 5% and tomatoes without soaking with formalin (0%). The samples in this study were 18 samples. The measurements of the intensity on tomatoes aroma were using Piezoelectric Transducer sensors. The classification of tomato spectrum data was using the Principal Component Analysis (PCA) method with Gap Reduction pretreatment. The results of this study were obtained: the Electronic nose began to respond the smell of formalin on tomatoes at 8.14 seconds, and it could classify the content and formalin levels in tomatoes at 25.77 seconds. Electronic nose combined with the principal component analysis (PCA) method have successfully detected the content and levels of formalin in tomatoes with a success rate at 99% (PC-1 of 93% and PC-2 of 6%). The difference of grade formalin levels is the main factor that causes Electronic nose to be able to distinguish the tomato samples tested, because the higher of formalin content in tomatoes, the distinctive of tomatoes aroma is increasingly disappearing. Thereby, the Electronic nose based on  the olfactory ability can distinguish them. 

ГЕОИНФОРМАЦИОННАЯ БАЗА ДАННЫХ ПО ОЦЕНКЕ МЕЛИОРАТИВНОГО СОСТОЯНИЯ ОРОШАЕМЫХ И ОСУШАЕМЫХ ЗЕМЕЛЬ ЮГА ЕВРОПЕЙСКОЙ ЧАСТИ РОССИИ

2020 ◽  
pp. 4
Author(s):  
A.A. Buber ◽  
E.L. Ratkovich ◽  
Y.A. Homutov
Keyword(s):  
Information System ◽  
Geographic Information System ◽  
Open Source ◽  
Water Use ◽  
Russian Federation ◽  
Geographic Information ◽  
Hydraulic Structures ◽  
Topographic Map ◽  
The Russian Federation

Для визуального отображения сведений о государственных гидромелиоративных системах, их составе, показателях, отдельно расположенных гидротехнических сооружениях и информации по использованию воды и сбросу загрязняющих веществ в исследуемых регионах, была использована общедоступная географическая информационная система с открытым кодом QGIS 3.4. ГИС-проект содержит: топографическую карту с нанесенными границами областей и векторные слои, включающие данные о гидромелиоративных системах, составе гидротехнических сооружений, показателям орошаемых и осушаемых земель и водопользованию по субъектам РФ 1,2,3.The open-source geographic information system QGIS 3.4 was used to visually display information about state hydro-reclamation systems, their composition, indicators, separately located hydraulic structures, and information on water use and discharge of pollutants in the study regions. The GIS project contains: a topographic map with the applied regions borders and vector layers, including data on hydro-reclamation systems, hydraulic structures composition, indicators of irrigated and drained lands and water use for the subjects of the Russian Federation.

scholarly journals Kernel Principal Component Analysis of Coil Compression in Parallel Imaging

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Yuchou Chang ◽  
Haifeng Wang
Keyword(s):  
Principal Component Analysis ◽  
Parallel Imaging ◽  
Principal Component ◽  
Component Analysis ◽  
Kernel Principal Component Analysis ◽  
Computational Time ◽  
Parallel Mri ◽  
Compression Technique ◽  
Virtual Channels ◽  
Pca Method

A phased array with many coil elements has been widely used in parallel MRI for imaging acceleration. On the other hand, it results in increased memory usage and large computational costs for reconstructing the missing data from such a large number of channels. A number of techniques have been developed to linearly combine physical channels to produce fewer compressed virtual channels for reconstruction. A new channel compression technique via kernel principal component analysis (KPCA) is proposed. The proposed KPCA method uses a nonlinear combination of all physical channels to produce a set of compressed virtual channels. This method not only reduces the computational time but also improves the reconstruction quality of all channels when used. Taking the traditional GRAPPA algorithm as an example, it is shown that the proposed KPCA method can achieve better quality than both PCA and all channels, and at the same time the calculation time is almost the same as the existing PCA method.

CORRIDOR-SCENE CLASSIFYING METHODS FOR MOBILE ROBOT BASED ON MULTI-SONAR-SENSOR INFORMATION FUSION

2007 ◽  
Vol 04 (01) ◽  
pp. 15-26 ◽  
Author(s):  
XIUQING WANG ◽  
ZENG-GUANG HOU ◽  
LONG CHENG ◽  
MIN TAN ◽  
FEI ZHU
Keyword(s):  
Principal Component Analysis ◽  
Mobile Robot ◽  
Principal Component ◽  
Component Analysis ◽  
Experimental Results ◽  
Scene Analysis ◽  
Kernel Principal Component Analysis ◽  
Kernel Pca ◽  
Sonar Sensor ◽  
Pca Method

The ability of cognition and recognition for complex environment is very important for a real autonomous robot. A new scene analysis method using kernel principal component analysis (kernel-PCA) for mobile robot based on multi-sonar-ranger data fusion is put forward. The principle of classification by principal component analysis (PCA), kernel-PCA, and the BP neural network (NN) approach to extract the eigenvectors which have the largest k eigenvalues are introduced briefly. Next the details of PCA, kernel-PCA and the BP NN method applied in the corridor scene analysis and classification for the mobile robots based on sonar data are discussed and the experimental results of those methods are given. In addition, a corridor-scene-classifier based on BP NN is discussed. The experimental results using PCA, kernel-PCA and the methods based on BP neural networks (NNs) are compared and the robustness of those methods are also analyzed. Such conclusions are drawn: in corridor scene classification, the kernel-PCA method has advantage over the ordinary PCA, and the approaches based on BP NNs can also get satisfactory results. The robustness of kernel-PCA is better than that of the methods based on BP NNs.

Identification of the isomers using principal component analysis (PCA) method

2016 ◽  
Author(s):  
Abdullah Kepceoğlu ◽  
Yasemin Gündoğdu ◽  
Kenneth William David Ledingham ◽  
Hamdi Sukur Kilic
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Pca Method
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