scholarly journals Habitat and Distribution Modeling of Prehistoric Hippos (Hippopotamus sivalensis spp.) During Pleistocene in Java Island

2021 ◽  
Vol 6 (1) ◽  
pp. 93-101
Author(s):  
Andriwibowo Andriwibowo ◽  
Adi Basukriadi ◽  
Erwin Nurdin
Keyword(s):  
Principal Component Analysis ◽  
Fossil Record ◽  
Principal Component ◽  
Habitat Modeling ◽  
Asian Continent ◽  
Distribution Modeling ◽  
Submerged Aquatic Plants ◽  
Extant Species ◽  
Hippopotamus Amphibius ◽  
Pca Method

Currently, there are only 2 extant species of hippos including common (Hippopotamus amphibius) and pygmy hippos (Choeropsis liberiensis) . But in prehistoric times, there were several species. During  Pleistocene these species were known to migrate to Java Island from Asian Continent and the species was Hippopotamus sivalensis spp. In this regard, this study aimed to model the habitat of H. sivalensis spp., ecology, and biodiversity of Hippopotamus sivalensis spp. based on the fossil record. The model was developed based on the Principal Component Analysis (PCA) method using the R statistical package. The results showed that there were 7 populations of H. sivalensis spp. that lived at various altitudes with an average of 177 m above sea level (95% CI : 123-232 m). According to PCA, there were at least 2 separate populations of H. sivalensis spp. One population occupies the forest while another occupies a habitat close to the coast. Currently the habitat for H. sivalensis spp. already changed. Based on habitat modeling, H. sivalensis spp. inhabit streams with submerged aquatic plants and shrubs and trees growing along river banks.

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. 

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

scholarly journals Novel Distance Metric for Touch less Footprint Based Identification Technique

2020 ◽  
Vol 9 (3) ◽  
pp. 1011-1016
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Personal Identification ◽  
Shape Features ◽  
Distance Metric ◽  
Mahalanobis Distances ◽  
Behavioral Attribute ◽  
Identification Technique ◽  
Pca Method ◽  
Personal Recognition

In this fast-paced technology-driven today's era, biometrics is not the new buzzword in the information security domain. Biometrics uses any physiological or/ and behavioral attribute/s of an individual for personal identification and/or verification. In biometrics, so many traits, like a fingerprint, face, palm, retina, iris, ECG, gait, voice, and signature, etc., have been used from ages to uniquely identify a human being. Biometrics based on Footprints is the latest practice for personal identification. Like fingerprints and palmprints, footprints of individuals carry uniqueness; hence can be used in biometrics for personal recognition. This work investigates the powerfulness of footprints by extracting texture and shape features using Principal Component Analysis (PCA) method based upon Eigenfeet and introduces a new distance metric during the matching phase. Experimental results show that the new distance metric shows better results in comparison to the Euclidean, Manhattan and Mahalanobis distances.

scholarly journals Analisis Kandungan N-Nitrogen (Amonia, Nitrit, Nitrat) dan Fosfat di Perairan Teluk Pandan Provinsi Lampung

2019 ◽  
Vol 8 (1) ◽  
pp. 57-66
Author(s):  
Reza Iklima AS ◽  
Gusti Diansyah ◽  
Andi Agussalim ◽  
Citra Mulia
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Data Analysis ◽  
Sampling Method ◽  
Principal Component ◽  
Pearl Oyster ◽  
Water Sampling ◽  
Cage Culture ◽  
Water Sampler ◽  
Pca Method

Iklima AS et al, 2019. Analysis of N-Nitrogen (Ammonia, Nitrate, and Nitric) and Phosphate at Teluk Pandan’s water territorial, Lampung Province. JLSO 8(1):57-66.Teluk Pandan’s water territorial was known to aquaculture activity such as prawn, pearl oyster and cage culture by community that lived in the area. It activities could makes water quality to be polluted.This research was purposed to known the content of nutrient (Ammonia, Nitrate, Nitric, and Phosphate) and to studied nutrient that related to other’s water quality parametric at Teluk Pandan water territorial. Sampling was determinate by 15station using purposive sampling method. Data analysis was used to studied relation between water quality’s parametric using Principal Component Analysis (PCA). Water sampling was taken at surface using water sampler. It was analyze in Oceanography and Instrumentation Laboratory, Department of Marine Science, Universitas Sriwijaya. Result of this research showing that rate of content nutrient at Teluk Pandan’s water territory ranging from 0.0007-0.0087 mg/L NO3-N, nitric ranging from 0.0001-0.0062 mg/L NO2-N, and phosphate ranging form 0,0012 – 0,0091 mg/L PO4-P. Based on result Teluk Pandan’s water territory still can be used for water’s ecosystem. Result using PCA method showing that correlation between parametric are directly proportional and inversely. Correlation that directly proportional showing by parametric group quadrant I (Temperature, Salinity, Velocity, and Abundance of Phytoplankton), quadrant II (DO, pH and nitrate) and quadrant III (Ammonia, nitric and phosphate). Inversely showing by parametric group quadrant I to parametric group quadrant III.

scholarly journals Quantifying the Wind Direction Climate over the Baltic States using Principal Component Analysis

2021 ◽  
Author(s):  
Tija Sile ◽  
Maksims Pogumirskis ◽  
Juris Seņņikovs ◽  
Uldis Bethers
Keyword(s):  
Principal Component Analysis ◽  
Wind Direction ◽  
Surface Wind ◽  
Principal Component ◽  
Component Analysis ◽  
Baltic States ◽  
Spatial Coverage ◽  
Prevailing Wind Direction ◽  
Pca Method ◽  
The Baltic

<p>Wind direction is an important meteorological parameter, however, its analysis is made difficult by it being a circular variable that cannot easily be averaged. The goal of this study was to identify the main features of wind direction climate over the Baltic States in a methodical way. We used Principal Component Analysis (PCA) for this purpose.</p><p>Two data sets were used: UERRA re-analysis with 11 km horizontal resolution and surface wind direction observations from Latvian stations. We used PCA on both of these datasets and analyzed the results together. Such an approach enabled comparison of the wind direction climate of the reanalysis with the observations. However, preliminary results suggested applying PCA also on the subset of UERRA data that corresponds to observation stations. This eliminates effects caused by differences in spatial coverage between  gridded and station datasets.</p><p>To verify the quality of the reanalysis independently of the PCA method, Earth Mover’s Distance (EMD) was used to directly compare wind direction distributions at the station grid points with observations.</p><p>Results show good correspondence overall between the reanalysis data and the observations. The PCA method identifies SW as the prevailing wind direction, in good agreement with the expectations. The PCA results enable identification of the main wind direction features of the region, such as increased frequency of northern winds during the summer and increased frequency of southern winds during the winter that can be explained by synoptic scale processes. Additionally, the PCA method identifies coast parallel flows created by mesoscale interaction between the Baltic Sea and the dry land, and wind deflection around terrain (hills up to 300 m above sea level).</p><p>This approach could be generalized to other regions and help create a more systematic understanding about wind direction climate, as well as assist in quantifying the performance of reanalysis and identify meteorological processes that need to be investigated further.</p><p>Corresponding author is grateful to the project “Mathematical modelling of weather processes - development of methodology and applications for Latvia (1.1.1.2/VIAA/2/18/261)” for financial support.</p>

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