WATER MASS ANALYSIS OF THE INDONESIAN THROUGHFLOW BY MEANS OF PRINCIPLE COMPONENT ANALYSIS

The water masses in both routes of Indonesia Throughflow (ITF) from historical hydrographic data are examined by means of the Principal Component Analysis (PCA), a multivariate statistical technique, during the southeast monsoon and northwest monsoon, and compared with the TS diagrams. The temperature and dissolves oxygen always play in the same PC, which describeds a variability contribution of the water mass characters, while salinity in a different PC. The relationship of the water masses parameters may indicate the character of dissolved oxygen as a non-conservation tracer. The Principle Component Analysis may also be used to follow the trendds of core layer attenuation as verified by the salinity corresponds at the PC. It will be higher with S-max and S-min and more closely resemble the sources. This condition is shown in the waters close to the main sources in the Pacific, like Sulawesi, Malkuku and Halmahera Sea, where both the salinity extrema can still be observed. Conversely, in the Banda and Timor Sea, where S-max and S-min are greatly attenuated even completely remove, the correspondence of salinity in the water mass character variability becomes smaller. As seen on TS and TO diagrams, PCA graphics are also showed the dominant of the north Pacific water in the western route seas, the Sulawesi, Makasar Strait and the Florest Sea, but relatively salty water of South Pacific origin is observed in the Halmahera Sea, particularly in the northwest monsoon. The strong seasonal variablity of surface water in the Indonesian can also be observed in the PCA graphics. Keywords: Water Mass, Indonesian Throughflow, PCA.

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WATER MASSES CHARACTERISTICS AT THE SANGHIE TALAUD ENTRY PASSAGE OF INDONESIAN THROUGHFLOW USING INDEX SATAL DATA 2010

Jurnal Ilmu dan Teknologi Kelautan Tropis ◽

10.28930/jitkt.v6i2.9027 ◽

2015 ◽

Vol 6 (2) ◽

Author(s):

Ivonne M Radjawane ◽

Paundra P Hadipoetranto

Keyword(s):

North Pacific ◽

Water Mass ◽

Sea Water ◽

Core Layer ◽

South Pacific ◽

New Guinea ◽

Water Masses ◽

Coastal Current ◽

Indonesian Throughflow ◽

Pacific Water

ABSTRACT Measurement of ocean physical parameters using the CTD was conducted by deep water expedition INDEX-SATAL 2010 (Indonesian Expedition Sangihe-Talaud) in July-August 2010. The aim of this study was to determine the characteristics of water masses around the Sangihe Talaud Water where there was an entry passage of Indonesian throughflow (ITF) at the west pathway that passed through the primary pathway i.e., the Sulawesi Sea and Makassar Strait and the secondary pathway (east pathway) that passed through the Halmahera Sea. The analyses were performed by the method of the core layer and was processed with software Ocean Data View (ODV). The results showed that in the Sangihe Talaud waters there was a meeting water masses from the North Pacific and the South Pacific. The water mass characteristics in main pathway through the Sulawesi Sea was dominated by surface and intermediate North Pacific water masses and carried by the Mindanao Currents. While the Halmahera Sea water mass was dominated by surface and intermediate South Pacific water masses carried by the New Guinea Coastal Current that moved along the Papua New Guinea and Papua coast enters to the Halmahera Sea. Keywords: Index-Satal 2010, Northern Pacific Water Masses, Southern Pacific Water Masses, Sangihe Talaud

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A strategy for selecting diverse accessions using Principal Component Analysis from a large germplasm collection of soybean

Plant Genetic Resources ◽

10.1079/pgr200322 ◽

2003 ◽

Vol 1 (2-3) ◽

pp. 151-156 ◽

Cited By ~ 1

Author(s):

R. L Sapra ◽

S. K. Lal

Keyword(s):

Principal Component Analysis ◽

Sample Size ◽

Principle Component Analysis ◽

Quantitative Traits ◽

Germplasm Collection ◽

Principal Component ◽

Target Population ◽

Component Analysis ◽

Principle Component ◽

The Individual

AbstractWe suggest a diversity-dependent strategy, based on Principle Component Analysis, for selecting distinct accessions/parents for breeding from a soybean germplasm collection comprising of 463 lines, characterized and evaluated for 10 qualitative and eight quantitative traits. A sample size of six accessions included all the three states, namely low, medium and high of the individual quantitative traits, while a sample of 16–19 accessions included all the 60–64 distinct states of qualitative as well as quantitative traits. Under certain assumptions, the paper also develops an expression for estimating the size of a target population for capturing maximum variability in a sample three accessions.

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Support vector regression is an improvement for principle component analysis

Journal of University of Shanghai for Science and Technology ◽

10.51201/jusst/21/06462 ◽

2021 ◽

Vol 23 (06) ◽

pp. 1699-1715

Author(s):

Mohamed, A. M. ◽

◽

Abdel Latif, S. H ◽

Alwan, A. S. ◽

◽

...

Keyword(s):

Principal Component Analysis ◽

Support Vector Regression ◽

Principle Component Analysis ◽

Mean Squared Error ◽

Principal Component ◽

Component Analysis ◽

Kernel Functions ◽

Coefficient Of Determination ◽

Support Vector ◽

Principle Component

The principle component analysis is used more frequently as a variables reduction technique. And recently, an evolving group of studies makes use of machine learning regression algorithms to improve the estimation of empirical models. One of the most frequently used machines learning regression models is support vector regression with various kernel functions. However, an ensemble of support vector regression and principal component analysis is also possible. So, this paper aims to investigate the competence of support vector regression techniques after performing principal component analysis to explore the possibility of reducing data and having more accurate estimations. Some new proposals are introduced and the behavior of two different models 𝜀𝜀-SVR and 𝑣𝑣-SVR are compared through an extensive simulation study under four different kernel functions; linear, radial, polynomial, and sigmoid kernel functions, with different sample sizes, ranges from small, moderate to large. The models are compared with their counterparts in terms of coefficient of determination (𝑅𝑅2 ) and root mean squared error (RMSE). The comparative results show that applying SVR after PCA models improve the results in terms of SV numbers between 30% and 60% on average and it can be applied with real data. In addition, the linear kernel function gave the best values rather than other kernel functions and the sigmoid kernel gave the worst values. Under 𝜀𝜀-SVR the results improved which did not happen with 𝑣𝑣-SVR. It is also drawn that, RMSE values decreased with increasing sample size.

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Principle Component Analysis Based on New Symmetric Similarity Measures for Heavy-Tailed Data

Fluctuation and Noise Letters ◽

10.1142/s0219477518500293 ◽

2018 ◽

Vol 17 (04) ◽

pp. 1850029

Author(s):

Mohammad Seidpisheh ◽

Adel Mohammadpour

Keyword(s):

Principal Component Analysis ◽

Similarity Measure ◽

Principle Component Analysis ◽

Similarity Measures ◽

Principal Component ◽

Component Analysis ◽

Principle Component ◽

Heavy Tailed Distributions ◽

Heavy Tailed ◽

Artificial Datasets

We consider the principal component analysis (PCA) for the heavy-tailed distributions. A traditional measure for the classical PCA is the covariance measure. Due to the non-existence of variance of many heavy-tailed distributions, this measure cannot be used for them. We will clarify how to perform PCA in heavy-tailed data by extending a similarity measure based on covariance. We introduce similarity measures based on a new dependence coefficient of heavy-tailed distributions. Using real and artificial datasets, the performance of the proposed PCA is evaluated and compared with the classical one.

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Fault Diagnosis Method Based on Information Entropy and Relative Principal Component Analysis

Journal of Control Science and Engineering ◽

10.1155/2017/2697297 ◽

2017 ◽

Vol 2017 ◽

pp. 1-8 ◽

Cited By ~ 1

Author(s):

Xiaoming Xu ◽

Chenglin Wen

Keyword(s):

Fault Diagnosis ◽

Principal Components ◽

Information Entropy ◽

Principle Component Analysis ◽

Information Gain ◽

Principal Component ◽

Component Analysis ◽

Principle Component ◽

Original Dataset ◽

Diagnosis Method

In traditional principle component analysis (PCA), because of the neglect of the dimensions influence between different variables in the system, the selected principal components (PCs) often fail to be representative. While the relative transformation PCA is able to solve the above problem, it is not easy to calculate the weight for each characteristic variable. In order to solve it, this paper proposes a kind of fault diagnosis method based on information entropy and Relative Principle Component Analysis. Firstly, the algorithm calculates the information entropy for each characteristic variable in the original dataset based on the information gain algorithm. Secondly, it standardizes every variable’s dimension in the dataset. And, then, according to the information entropy, it allocates the weight for each standardized characteristic variable. Finally, it utilizes the relative-principal-components model established for fault diagnosis. Furthermore, the simulation experiments based on Tennessee Eastman process and Wine datasets demonstrate the feasibility and effectiveness of the new method.

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Aplikasi Principle Component Analysis dan Directed Principal Component untuk Pemetaan Alterasi Hidrotermal menggunakan Citra ASTER di Kecamatan Kokap, Kulon Progo

Jurnal Geologi dan Sumberdaya Mineral ◽

10.33332/jgsm.geologi.v20i3.437 ◽

2019 ◽

Vol 20 (3) ◽

pp. 119

Author(s):

Bayu Raharja ◽

Agung Setianto ◽

Anastasia Dewi Titisari

Keyword(s):

Principle Component Analysis ◽

Near Infrared ◽

Confusion Matrix ◽

Principal Component ◽

Component Analysis ◽

Principle Component ◽

Shortwave Infrared

Endapan emas di daerah Kokap, Kabupaten Kulon Progo yang berasosiasi dengan endapan tipe epitermal terbentuk akibat adanya proses alterasi hidrotermal. Pemanfaatan data penginderaan jauh untuk pemetaan alterasi telah berhasil dilakukan diberbagai lokasi. Penelitian ini bertujuan untuk mengkaji kemampuan citra ASTER saluran visible near infrared (VNIR) dan saluran shortwave infrared (SWIR) dalam memetakan jenis alterasi menggunakan metode Principle Component Analysis (PCA) dan Directed Principal Component (DPC). Klasifikasi multispektral selanjutnya dilakukan untuk membedakan tipe alterasi hidrotermal menggunakan kombinasi saluran hasil PCA dan DPC. Keberhasilan pemetaan alterasi hidrotermal dievaluasi secara statistik menggunakan confusion matrix. Tingkat akurasi yang dapat diterima adalah sebesar 85% dengan koefisien kappa lebih besar dari 0.8. Hasil penelitian menunjukkan metode DPC memiliki akurasi lebih baik dari PCA dalam memetakan tipe alterasi meskipun tingkat akurasi keduanya dibawah batas yang ditentukan. Saluran ASTER yang paling baik digunakan untuk memetakan tipe alterasi hidrotermal di lokasi penelitian adalah 1, 2, 4, dan 6.

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Object Classification via PCANet and Color Constancy Model

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.635-637.997 ◽

2014 ◽

Vol 635-637 ◽

pp. 997-1000 ◽

Cited By ~ 1

Author(s):

De Kun Hu ◽

Li Zhang ◽

Wei Dong Zhao ◽

Tao Yan

Keyword(s):

Principal Component Analysis ◽

Data Processing ◽

Functional Properties ◽

Principle Component Analysis ◽

Color Constancy ◽

Principal Component ◽

Object Classification ◽

Component Analysis ◽

Color Images ◽

Principle Component

In order to classify the objects in nature images, a model with color constancy and principle component analysis network (PCANet) is proposed. The new color constancy model imitates the functional properties of the HVS from the retina to the double-opponent cells in V1. PCANet can be designed and learned extremely, which comprises only the very basic data processing components: cascaded principal component analysis (PCA), binary hashing, and block-wise histograms. At last, a SVM is trained to classify the object in the image. The results of experiments demonstrate the potential of the model for object classification in wild color images.

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Principle Component Analysis

Computational Intelligence and its Applications - Biometric Image Discrimination Technologies ◽

10.4018/978-1-59140-830-7.ch002 ◽

2006 ◽

pp. 21-40

Author(s):

David Zhang ◽

Xiao-Yuan Jing ◽

Jian Yang

Keyword(s):

Principle Component Analysis ◽

Component Analysis ◽

Statistical Technique ◽

Principle Component ◽

Non Linear ◽

Basic Concepts

In this chapter, we first describe some basic concepts of PCA, a useful statistical technique that can be used in many fields, such as face patterns and other biometrics. Then, we introduce PCA definitions and related technologies. Following, we discuss non-linear PCA technologies. Finally, some useful conclusions are summarized.

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Topography of Clonidine-induced Electroencephalographic Changes Evaluated by Principal Component Analysis

Anesthesiology ◽

10.1097/00000542-200006000-00010 ◽

2000 ◽

Vol 92 (6) ◽

pp. 1545-1552 ◽

Cited By ~ 16

Author(s):

Petra Bischoff ◽

Eckehard Scharein ◽

Gunter N. Schmidt ◽

Georg von Knobelsdorff ◽

Burkhart Bromm ◽

...

Keyword(s):

Principal Component Analysis ◽

Frequency Band ◽

Principle Component Analysis ◽

Principal Component ◽

Component Analysis ◽

Fast Fourier Transformation ◽

Complex Data ◽

Multivariate Statistical ◽

Double Blind ◽

Principle Component

Background Principal component analysis is a multivariate statistical technique to facilitate the evaluation of complex data dimensions. In this study, principle component analysis was used to reduce the large number of variables from multichannel electroencephalographic recordings to a few components describing changes of spatial brain electric activity after intravenous clonidine. Methods Seven healthy volunteers (age, 26 +/- 3 [SD] yr) were included in a double-blind crossover study with intravenous clonidine (1.5 and 3.0 microg/kg). A spontaneous electroencephalogram was recorded by 26 leads and quantified by standard fast Fourier transformation in the delta, theta, alpha, and beta bands. Principle component analysis derived from a correlation matrix calculated between all electroencephalographic leads (26 x 26 leads) separately within each classic frequency band. The basic application level of principle component analysis resulted in components representing clusters of electrodes positions that were differently affected by clonidine. Subjective criteria of drowsiness and anxiety were rated by visual analog scales. Results Topography of clonidine-induced electroencephalographic changes could be attributed to two independent spatial components in each classic frequency band, explaining at least 85% of total variance. The most prominent effects of clonidine were increases in the delta band over centroparietooiccipital areas and decreases in the alpha band over parietooccipital regions. Clonidine administration resulted in subjective drowsiness. Conclusions Data from the current study supported the fact that spatial principle component analysis is a useful multivariate statistical procedure to evaluate significant signal changes from multichannel electroencephalographic recordings and to describe the topography of the effects. The clonidine-related changes seen here were most probably results of its sedative effects.

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The continuity of water masses along the western boundary of the Tasman and Coral Seas

Marine and Freshwater Research ◽

10.1071/mf9680077 ◽

1968 ◽

Vol 19 (2) ◽

pp. 77 ◽

Cited By ~ 13

Author(s):

DJ Rochford

Keyword(s):

Water Mass ◽

New Caledonia ◽

Core Layer ◽

Water Masses ◽

Western Boundary ◽

Intermediate Water ◽

North West ◽

The North ◽

Coral Sea ◽

Intermediate Water Mass

Hydrological data of the Umitaka Maru (December 1967) and of H.M.A.S. Gascoyne (November-December 1965) have been used to show continuity of selected water masses from the north-west Coral Sea to the continental margin off New South Wales. The core layer properties of these water masses (salinity, temperature, oxygen) indicate that these water masses of the north-west Coral Sea are formed by the inflow from the east of the South Equatorial water mass (0 m), the upper salinity maximum water mass (150-200 m) of the central South Pacific, and of the Antarctic Intermediate water mass (800-1000 m). The inflow of the first two occurs immediately south of the Solomon Is. whilst that of the third occurs between New Caledonia and the New Hebrides. Continuity of the upper oxygen maximum of the 200-800 m layer was not examined because of doubts as to its existence as a separate water mass.

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