Multi-harmonic sources location based on sparse component analysis and complex independent component analysis

2020 ◽  
Vol 14 (19) ◽  
pp. 4195-4206
Author(s):  
Guangui Wang ◽  
Xiaoyang Ma ◽  
Weikang Wang ◽  
Honggeng Yang ◽  
Chang Chen ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Sparse Component Analysis

INDEPENDENT COMPONENT ANALYSIS (ICA) DAN SPARSE COMPONENT ANALYSIS (SCA) DALAM PEMISAHAN VOKAL DAN INSTRUMEN PADA SENI GEGUNTANGAN

2019 ◽  
Vol 8 (1) ◽  
pp. 105
Author(s):  
Angga Pramana Putra ◽  
Ni Wayan Wiantari ◽  
Putu Mira Novita Dewi ◽  
I Dewa Made Bayu Atmaja Darmawan
Keyword(s):  
Independent Component Analysis ◽  
Mean Square Error ◽  
Blind Source Separation ◽  
Component Analysis ◽  
Independent Component ◽  
Signal Separation ◽  
Blind Signal Separation ◽  
Mean Square ◽  
Blind Signal ◽  
Sparse Component Analysis

Geguntangan adalah pesantian dalam upacara keagamaan yang diiringi dengan gamelan. Indra  pendengaran manusia cenderung memiliki keterbatasan, yang menyebabkan tidak semua vokal yang  tercampur dengan gamelan bisa didengar jelas. Oleh karena itu diperlukan suatu sistem yang dapat digunakan untuk memisahkan vokal dengan gamelan pada geguntangan. Pemisahan sumber suara ini dikategorikan sebagai Blind Source Separation (BSS) atau disebut juga Blind Signal Separation yang  artinya sumber tidak dikenal. Algoritma yang digunakan untuk menangani BSS adalah algoritma Independent Component Analysis (ICA) dan Sparse Component Analysis (SCA) dengan berfokus  pada pemisahan sinyal suara pada file suara berformat *.wav. Algoritma SCA dan ICA digunakan  untuk proses pemisahan suara dengan parameter nilai yang digunakan adalah Mean Square Error (MSE) dan Signalto Interference Ratio(SIR). Dari hasil simulasi menunjukkan Hasil perhitungan MSE dan SIR dengan dengan menggunakan mixing matriks [0.3816, 0.8678], [0.8534, -0.5853] didapatkan untuk metode ICA nilai MSE sebesar 4.169380402433175 x 10-6 untuk instrumennya dan 2.884749383815846 x 10-5 untuk vokalnya dan didapatkan nilai SIR sebesar 53.79928479270223 untuk instrumennya dan 45.39891910741724 untuk vokalnya. Selanjutnya untuk metode SCA, nilai MSE sebesar 3.382207103335018 x 10-5 untuk instrumennya dan 3.099942460987607 x 10-5 untuk vokalnya dan didapatkan nilai SIR sebesar 44.707998026869014 untuk instrumennya dan 45.08646367168143 untuk vokalnya.

scholarly journals Multi-Harmonic Source Localization Based on Sparse Component Analysis and Minimum Conditional Entropy

Entropy ◽  
2020 ◽  
Vol 22 (1) ◽  
pp. 65
Author(s):  
Du ◽  
Yang ◽  
Ma
Keyword(s):  
Independent Component Analysis ◽  
Distribution System ◽  
Conditional Entropy ◽  
Component Analysis ◽  
Independent Component ◽  
Minimum Condition ◽  
Harmonic Current ◽  
Sparse Component Analysis ◽  
System Node ◽  
Harmonic Source

Aiming at the fact that the independent component analysis algorithm requires more measurement points and cannot solve the problem of harmonic source location under underdetermined conditions, a new method based on sparse component analysis and minimum conditional entropy for identifying multiple harmonic source locations in a distribution system is proposed. Under the condition that the network impedance is unknown and the number of harmonic sources is undetermined, the measurement node configuration algorithm selects the node position to make the separated harmonic current more accurate. Then, using the harmonic voltage data of the selected node as the input, the sparse component analysis is used to solve the harmonic current waveform under underdetermination. Finally, the conditional entropy between the harmonic current and the system node is calculated, and the node corresponding to the minimum condition entropy is the location of the harmonic source. In order to verify the effectiveness and accuracy of the proposed method, the simulation was performed in an IEEE 14-node system. Moreover, compared with the results of independent component analysis algorithms. Simulation results verify the correctness and effectiveness of the proposed algorithm.

Skin Chromophore Estimation from Mobile Selfie Images using Constrained Independent Component Analysis

2020 ◽  
Vol 2020 (14) ◽  
pp. 357-1-357-6
Author(s):  
Luisa F. Polanía ◽  
Raja Bala ◽  
Ankur Purwar ◽  
Paul Matts ◽  
Martin Maltz
Keyword(s):  
Independent Component Analysis ◽  
Spectral Reflectance ◽  
Source Separation ◽  
Principal Component ◽  
Component Analysis ◽  
Previous Method ◽  
Independent Component ◽  
Light Transport ◽  
Camera Model ◽  
Constrained Independent Component Analysis

Human skin is made up of two primary chromophores: melanin, the pigment in the epidermis giving skin its color; and hemoglobin, the pigment in the red blood cells of the vascular network within the dermis. The relative concentrations of these chromophores provide a vital indicator for skin health and appearance. We present a technique to automatically estimate chromophore maps from RGB images of human faces captured with mobile devices such as smartphones. The ultimate goal is to provide a diagnostic aid for individuals to monitor and improve the quality of their facial skin. A previous method approaches the problem as one of blind source separation, and applies Independent Component Analysis (ICA) in camera RGB space to estimate the chromophores. We extend this technique in two important ways. First we observe that models for light transport in skin call for source separation to be performed in log spectral reflectance coordinates rather than in RGB. Thus we transform camera RGB to a spectral reflectance space prior to applying ICA. This process involves the use of a linear camera model and Principal Component Analysis to represent skin spectral reflectance as a lowdimensional manifold. The camera model requires knowledge of the incident illuminant, which we obtain via a novel technique that uses the human lip as a calibration object. Second, we address an inherent limitation with ICA that the ordering of the separated signals is random and ambiguous. We incorporate a domain-specific prior model for human chromophore spectra as a constraint in solving ICA. Results on a dataset of mobile camera images show high quality and unambiguous recovery of chromophores.

Exploring Independent Component Analysis for GPR Signal Processing

PIERS Online ◽  
2005 ◽  
Vol 1 (6) ◽  
pp. 750-753 ◽  
Author(s):  
Anxing Zhao ◽  
Yansheng Jiang ◽  
Wenbing Wang
Keyword(s):  
Signal Processing ◽  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component
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