scholarly journals Assessment of environmental risk from polluted organic wastewater in Long Thanh Industrial Park with the Nemerow index and Principal component Analysis

2021 ◽  
pp. 128-138
Keyword(s):  
Principal Component Analysis ◽  
High Risk ◽  
Environmental Risk ◽  
Principal Component ◽  
Component Analysis ◽  
The Other ◽  
Industrial Park ◽  
Other Hand ◽  
Industrial Parks ◽  
Pca Method

This study aims to assess environmental risk using the improved Nemerow index and the principal component analysis (PCA) method in Long Thanh's Industrial Park in Dong Nai Province. The study was implemented in five industrial parks of Long Thanh District in 2019. The result showed that Loc An - Binh Son industrial park was at extreme high risk of the level (6.7). Three industrial parks of Long Thanh, Go Dau and An Phuoc were high-risk (from 3 to 5) respectively. On the other hand, Long Duc Industrial Park has obtained no environmental risk.

scholarly journals VARIASI MORFOMETRIK DAN ALLOZYME CALON INDUK RAJUNGAN, Portunus pelagicus DARI BEBERAPA PERAIRAN DI INDONESIA

2006 ◽  
Vol 1 (2) ◽  
pp. 235
Author(s):  
Gusti Ngurah Permana ◽  
Sari Budi Moria ◽  
Haryanti Haryanti ◽  
Bambang Susanto
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Allozyme Variation ◽  
The Other ◽  
Width Ratio ◽  
Portunus Pelagicus ◽  
Central Java ◽  
South Sulawesi ◽  
Other Hand

Sampel diambil dari empat populasi rajungan yang berbeda yaitu Sulawesi Selatan, Jawa Timur, Jawa Tengah, dan Bali. Penelitian ini dilakukan untuk mengetahui variasi morfometrik dan allozyme dari calon induk rajungan. Hasil yang diperoleh yaitu variasi genetik rata-rata keempat populasi sangat rendah (0,0025). Rajungan dari Jawa Tengah dan Bali mempunyai nilai heterosigositas tertinggi yaitu 0,004 sedangkan populasi Sulawesi Selatan dan Jawa Timur (0,001). Jarak genetik populasi Jawa Timur dan Bali (0,0013), kemudian Jawa Tengah (0,0016), dan Sulawesi Selatan (0,002). Uji analisis komponen utama (Principal component analysis, PCA), menunjukkan bahwa secara morfometrik rajungan jantan dan betina yang berasal dari populasi Cilacap-Jawa Tengah dan P. Saugi-Sulawesi Selatan dapat membentuk satu sub populasi yang sama, sebaliknya populasi asal Negara-Bali membentuk sub populasi tersendiri. Korelasi yang erat antara nisbah panjang dan lebar karapas terhadap bobot tubuh ditemukan pada populasi P. Saugi-Sulawesi Selatan dan Cilacap-Jawa Tengah sebaliknya pada populasi Negara-Bali mempunyai korelasi yang rendah.Samples were collected from South Sulawesi, Central Java, East Java, and Bali. Genetic variation from allozyme was consistently low in all populations (0.0025) This research aimed to know morphometric and allozyme variation of Swimming Blue Crab, Portunus pelagicus from Indonesian waters. Population from Central Java and Bali had the highest heterozigosity value (0.004) compare to those from South Sulawesi and East Java (0.001). Sample cluster according to the pair’s genetic distance showe that East Java and Bali population has the smallest value (0.0013). By contrast, the largest value was observed in Central Java (0.0016) and South Sulawesi population (0.002). Principal Component Analysis showed that morphometrically male and female swimming blue crabs from Saugi and Cilacap population can build one identical subpopulation On the other hand population originated from Negara made a separate subpopulation There high correlation between carapace length and width ratio on population of P. Saugi-South Sulawesi and Cilacap-Central Java, on the other hand, Negara-Bali population had a low correlation.

scholarly journals Do Remittances Affect Politics in Bangladesh?

2021 ◽  
Author(s):  
Gazi Mainul Hassan ◽  
Shafiqur Rahman
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Structural Var ◽  
The Other ◽  
Alternative Measure ◽  
Important Impact ◽  
Other Hand ◽  
Permanent Effect ◽  
Political Variables

This paper examines how remittances contribute to the democratisation process in Bangladesh. The endogeneity issue between remittances and democracy is tackled by employing the Structural VAR (SVAR) approach. It is found that while remittances respond to innovations in the macro-political variables, remittances also have important impact on these variables. Our results build a synergy between two opposing findings in the politics literature where on one hand remittances flows stabilise autocracies, while on the other hand they foster the prospect for democratisation. In particular, we demonstrate that a shock in remittances flows will have a negative but transitory impact on democracy. Initially there will be a bout of autocratic episodes which will be eventually eliminated and democracy will be restored to its original level in three to five years. However, using an alternative measure for democracy with the aid of principal-component analysis, we find that after the fifth year following a shock in remittances flows, a small but positive permanent effect on democracy is observable that do not revert to zero at end of the ten period horizon.

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 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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