scholarly journals Determination of quality and storage-time of dielectric mixtures using time domain spectroscopy

2005 ◽  
Vol 2 ◽  
pp. 1-5 ◽  
Author(s):  
O. Schimmer ◽  
F. Daschner ◽  
M. Kent ◽  
R. Knöchel
Keyword(s):  
Principal Components ◽  
Time Domain ◽  
Material Properties ◽  
Storage Time ◽  
Time Window ◽  
Principal Component Regression ◽  
Principal Component ◽  
Physical Models ◽  
Novel Approach ◽  
Fish Samples

Abstract. This paper presents a novel approach for determining certain material properties from permittivity measurements. It does not rely on physical models and mixture formulas. Instead, the reflection time domain response of the material to a step impulse is evaluated in a narrow time window near the steepest ascent of the pulse. A dedicated time domain spectrometer is introduced, which records the data. Principal components are derived directly from the time domain data. The most significant principal components are used to establish a principal component regression formula for prediction of the required material properties. The viability and accuracy of the method is demonstrated by applying it to measurements of the storage time of some chilled fish samples.

scholarly journals Principal component articial neural network calibration models for the simultaneous spectrophotometric estimation of mefenamic acid and paracetamol in tablets

2006 ◽  
Vol 71 (11) ◽  
pp. 1207-1218
Author(s):  
Dondeti Satyanarayana ◽  
Kamarajan Kannan ◽  
Rajappan Manavalan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Mefenamic Acid ◽  
Principal Component Regression ◽  
Principal Component ◽  
Simultaneous Estimation ◽  
Uv Spectrophotometry ◽  
Novel Approach ◽  
Marquardt Algorithm ◽  
Calibration Models

Simultaneous estimation of all drug components in a multicomponent analgesic dosage form with artificial neural networks calibration models using UV spectrophotometry is reported as a simple alternative to using separate models for each component. A novel approach for calibration using a compound spectral dataset derived from three spectra of each component is described. The spectra of mefenamic acid and paracetamol were recorded as several concentrations within their linear range and used to compute a calibration mixture between the wavelengths 220 to 340 nm. Neural networks trained by a Levenberg-Marquardt algorithm were used for building and optimizing the calibration models using MATALAB? Neural Network Toolbox and were compared with the principal component regression model. The calibration models were thoroughly evaluated at several concentration levels using 104 spectra obtained for 52 synthetic binary mixtures prepared using orthogonal designs. The optimized model showed sufficient robustness even when the calibration sets were constructed from a different set of pure spectra of the components. The simultaneous prediction of both components by a single neural network with the suggested calibration approach was successful. The model could accurately estimate the drugs, with satisfactory precision and accuracy, in tablet dosage with no interference from excipients as indicated by the results of a recovery study.

Effects of display curvature and hand length on smartphone usability

2017 ◽  
Vol 61 (1) ◽  
pp. 1054-1057 ◽  
Author(s):  
Jihhyeon Yi ◽  
Sungryul Park ◽  
Juah Im ◽  
Seonyeong Jeon ◽  
Gyouhyung Kyung
Keyword(s):  
Principal Components ◽  
Principal Component Regression ◽  
Principal Component ◽  
The Other ◽  
Side Edge ◽  
Hand Size ◽  
Size Groups ◽  
The Mean ◽  
Hand Length ◽  
Video Calling

The purpose of this study was to examine the effects of display curvature and hand length on smartphone usability, which was assessed in terms of grip comfort, immersive feeling, typing performance, and overall satisfaction. A total of 20 younger individuals with the mean (SD) age of 20.8 (2.4) yrs were divided into three hand-size groups (small: 8, medium: 6, large: 6). Two smartphones of the same size were used – one with a flat display and the other with a side-edge curved display. Three tasks (watching video, calling, and texting) were used to evaluate smartphone usability. The smartphones were used in a landscape mode for the first task, and in a portrait mode for the other two. The flat display smartphone provided higher grip comfort during calling (p = 0.008) and texting (p = 0.006) and higher overall satisfaction (p = 0.0002) than the curved display smartphone. The principal component regression (adjusted R2 = 0.49) of overall satisfaction on three principal components comprised of the remaining measures showed that the first principal component on grip comfort was more important than the other two on watching experience and texting performance. It is thus necessary to carefully consider the effect of display curvature on grip comfort when applying curved displays to hand-held devices such as smartphones.

Principal Component Regression for Mixture Resolution in Control Analysis by UV-Visible Spectrophotometry

1994 ◽  
Vol 48 (1) ◽  
pp. 37-43 ◽  
Author(s):  
M. Blanco ◽  
J. Coello ◽  
H. Iturriaga ◽  
S. Maspoch ◽  
M. Redon
Keyword(s):  
Principal Components ◽  
Principal Component Regression ◽  
Principal Component ◽  
Optimal Number ◽  
Control Analysis ◽  
Baseline Drift ◽  
Visible Spectrophotometry ◽  
Calibration Matrix ◽  
Uv Visible ◽  
Diode Array Spectrophotometer

The potential of principal component regression (PCR) for mixture resolution by UV-visible spectrophotometry was assessed. For this purpose, a set of binary mixtures with Gaussian bands was simulated, and the influence of spectral overlap on the precision of quantification was studied. Likewise, the results obtained in the resolution of a mixture of components with extensively overlapped spectra were investigated in terms of spectral noise and the criterion used to select the optimal number of principal components. The model was validated by cross-validation, and the number of significant principal components was determined on the basis of four different criteria. Three types of noise were considered: intrinsic instrumental noise, which was modeled from experimental data provided by an HP 8452A diode array spectrophotometer; constant baseline shifts; and baseline drift. Introducing artificial baseline alterations in some samples of the calibration matrix was found to increase the reliability of the proposed method in routine analysis. The method was applied to the analysis of mixtures of Ti, AI, and Fe by resolving the spectra of their 8-hydroxyquinoline complexes previously extracted into chloroform.

scholarly journals Wavelet Scattering Transform for ECG Beat Classification

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Zhishuai Liu ◽  
Guihua Yao ◽  
Qing Zhang ◽  
Junpu Zhang ◽  
Xueying Zeng
Keyword(s):  
Neural Network ◽  
Time Window ◽  
Probabilistic Neural Network ◽  
Time Windows ◽  
Principal Component ◽  
Translation Invariant ◽  
Ecg Signals ◽  
Novel Approach ◽  
Scattering Transform ◽  
Reduction Methods

An electrocardiogram (ECG) records the electrical activity of the heart; it contains rich pathological information on cardiovascular diseases, such as arrhythmia. However, it is difficult to visually analyze ECG signals due to their complexity and nonlinearity. The wavelet scattering transform can generate translation-invariant and deformation-stable representations of ECG signals through cascades of wavelet convolutions with nonlinear modulus and averaging operators. We proposed a novel approach using wavelet scattering transform to automatically classify four categories of arrhythmia ECG heartbeats, namely, nonectopic (N), supraventricular ectopic (S), ventricular ectopic (V), and fusion (F) beats. In this study, the wavelet scattering transform extracted 8 time windows from each ECG heartbeat. Two dimensionality reduction methods, principal component analysis (PCA) and time window selection, were applied on the 8 time windows. These processed features were fed to the neural network (NN), probabilistic neural network (PNN), and k-nearest neighbour (KNN) classifiers for classification. The 4th time window in combination with KNN (k=4) has achieved the optimal performance with an averaged accuracy, positive predictive value, sensitivity, and specificity of 99.3%, 99.6%, 99.5%, and 98.8%, respectively, using tenfold cross-validation. Thus, our proposed model is capable of highly accurate arrhythmia classification and will provide assistance to physicians in ECG interpretation.

Spectral Mapping Using Kernel Principal Components Regression for Voice Conversion

2013 ◽  
Vol 38 (1) ◽  
pp. 39-45
Author(s):  
Peng Song ◽  
Li Zhao ◽  
Yongqiang Bao
Keyword(s):  
Principal Components ◽  
Median Filter ◽  
Principal Component Regression ◽  
Principal Component ◽  
Gaussian Mixture ◽  
Voice Conversion ◽  
Perceptual Quality ◽  
Posterior Probabilities ◽  
Spectral Mapping ◽  
The One

Abstract The Gaussian mixture model (GMM) method is popular and efficient for voice conversion (VC), but it is often subject to overfitting. In this paper, the principal component regression (PCR) method is adopted for the spectral mapping between source speech and target speech, and the numbers of principal components are adjusted properly to prevent the overfitting. Then, in order to better model the nonlinear relationships between the source speech and target speech, the kernel principal component regression (KPCR) method is also proposed. Moreover, a KPCR combined with GMM method is further proposed to improve the accuracy of conversion. In addition, the discontinuity and oversmoothing problems of the traditional GMM method are also addressed. On the one hand, in order to solve the discontinuity problem, the adaptive median filter is adopted to smooth the posterior probabilities. On the other hand, the two mixture components with higher posterior probabilities for each frame are chosen for VC to reduce the oversmoothing problem. Finally, the objective and subjective experiments are carried out, and the results demonstrate that the proposed approach shows greatly better performance than the GMM method. In the objective tests, the proposed method shows lower cepstral distances and higher identification rates than the GMM method. While in the subjective tests, the proposed method obtains higher scores of preference and perceptual quality.

scholarly journals Sparse principal component regression via singular value decomposition approach

2021 ◽  
Author(s):  
Shuichi Kawano
Keyword(s):  
Singular Value Decomposition ◽  
Principal Components ◽  
Principal Component Regression ◽  
Principal Component ◽  
Estimation Algorithm ◽  
Singular Value ◽  
Decomposition Approach ◽  
Response Variable ◽  
Explanatory Variables ◽  
Value Decomposition

AbstractPrincipal component regression (PCR) is a two-stage procedure: the first stage performs principal component analysis (PCA) and the second stage builds a regression model whose explanatory variables are the principal components obtained in the first stage. Since PCA is performed using only explanatory variables, the principal components have no information about the response variable. To address this problem, we present a one-stage procedure for PCR based on a singular value decomposition approach. Our approach is based upon two loss functions, which are a regression loss and a PCA loss from the singular value decomposition, with sparse regularization. The proposed method enables us to obtain principal component loadings that include information about both explanatory variables and a response variable. An estimation algorithm is developed by using the alternating direction method of multipliers. We conduct numerical studies to show the effectiveness of the proposed method.

scholarly journals Pemodelan Principal Component Regression dengan Software R

2012 ◽  
Vol 3 (1) ◽  
pp. 177
Author(s):  
Margaretha Ohyver
Keyword(s):  
Principal Components ◽  
Principal Component Regression ◽  
Principal Component ◽  
R Software

Principal Component Regression (PCR) is one method to handle multicollinear problems. PCR produces principal components that have a VIF less than ten. The purpose for this research is to obtained PCR model using R software. The result is a model of PCR with two principal components and determination coefficients R(square) = 97,27%.

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