A Fuzzy-Statistics-Based Principal Component Analysis (FS-PCA) Method for Multispectral Image Enhancement and Display

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.

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Patch-Based Principal Component Analysis for Face Recognition

Computational Intelligence and Neuroscience ◽

10.1155/2017/5317850 ◽

2017 ◽

Vol 2017 ◽

pp. 1-9 ◽

Cited By ~ 10

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.

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

Jurnal Ilmiah Mahasiswa Pertanian ◽

10.17969/jimfp.v4i2.10935 ◽

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.

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Face Image Enhancement via Principal Component Analysis

AI 2009: Advances in Artificial Intelligence - Lecture Notes in Computer Science ◽

10.1007/978-3-642-10439-8_20 ◽

2009 ◽

pp. 190-198

Author(s):

Deqiang Yang ◽

Tianwei Xu ◽

Rongfang Yang ◽

Wanquan Liu

Keyword(s):

Principal Component Analysis ◽

Image Enhancement ◽

Principal Component ◽

Component Analysis ◽

Face Image

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Kernel Principal Component Analysis of Coil Compression in Parallel Imaging

Computational and Mathematical Methods in Medicine ◽

10.1155/2018/4254189 ◽

2018 ◽

Vol 2018 ◽

pp. 1-9 ◽

Cited By ~ 1

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.

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

International Journal of Information Acquisition ◽

10.1142/s0219878907001125 ◽

2007 ◽

Vol 04 (01) ◽

pp. 15-26 ◽

Cited By ~ 3

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.

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