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.

scholarly journals VARIACIONES NICTEMERALES EN LA COMUNIDAD DE ZOOPLANCTON DE LA BAHIA DE SANTA MARTA, MAR CARIBE COLOMBIANO

2016 ◽  
Vol 22 ◽  
Author(s):  
Alvaro Ramiro BernaI V. ◽  
Sven Zea
Keyword(s):  
Principal Components ◽  
Close Association ◽  
Incident Light ◽  
Sampling Site ◽  
Principal Component ◽  
Environmental Parameters ◽  
The Other ◽  
Surface Zone ◽  
The Mean ◽  
Components Analysis

Day-night and between-day variation in surficial zooplankton composition and its relationship to environmental parameters were analyzed in the Santa Marta Bay, Colombian Caribbean, by Principal Components Analysis. Sampling was carried out every four hours in three different days between August and October 1989. The greatest variation in abundance (first principal component) was due to an increase over the mean in most groups at nightfall and during night hours. This variation was inversely and significantly correlated with incident light intensity, and was interpreted as consequence of the vertical migration out of the surface zone. On the other hand, the second and third principal components showed differences amog days for groups and within groups; those that existed in the first sampling day with respect to the other two were highlighted by the second component,, and were negatively correlated with temperature and positively correlated with disolved nitrates. These results were interpreted as a consequence of movements of water masses with different physical-chemical characteristics and zooplankton composition over the sampling site. However, a case of close association between these parameters and the changes in migrating movements could not be ruled out.

On Cliff's Formula, the Kaiser-Guttman Rule, and the Number of Factors

1992 ◽  
Vol 74 (2) ◽  
pp. 595-598 ◽  
Author(s):  
Henry F. Kaiser
Keyword(s):  
Measurement Error ◽  
Internal Consistency ◽  
Principal Components ◽  
Principal Component ◽  
Internal Consistency Reliability ◽  
The Other ◽  
Factor Analytic ◽  
Number Of Factors ◽  
Physical Attributes ◽  
The Right

Cliff (1988) has presented a formula for the reliability of a principal component which is different from my long-known formula (Kaiser, 1957, 1991) for coefficient alpha of a principal component. Cliff claims that his approach is “correct” and mine “is the result of a misapplication of the formula for internal consistency reliability” Actually, both developments are correct but are based on different premises: Cliff considers measurement error within—but not between—attributes, while I consider measurement error between—but not within—attributes. The application of my formula to the knotty problem of the “number of factors”—the Kaiser-Guttman Rule—appears often to give the “right” result, when “right” means agreement with the subjective judgment of factor-analytic grandmasters. But when it fails it is approximately equally likely to overfactor as to underfactor. Cliff's formula, on the other hand, when used to establish the number of factors, almost invariably overfactors and, in the limit, as the within-attribute reliabilities all approach one (as with, say, physical attributes), nonsensically will declare all principal components perfectly reliable no matter how small their associated eigenvalues, yielding an absurd answer if used to establish the number of factors.

Chemical Structure-Odor Correlation in Series of Synthetic Methylene Interrupted n-Nonadien-1-ols

1996 ◽  
Vol 51 (11-12) ◽  
pp. 841-848 ◽  
Author(s):  
Yasunobu Sakoda ◽  
Kenji Matsui ◽  
Yoshihiko Akakabe ◽  
Jun Suzuki ◽  
Akikazu Hatanaka ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Double Bond ◽  
Principal Components ◽  
Chemical Structure ◽  
Principal Component ◽  
Component Analysis ◽  
The Other ◽  
Analysis Method ◽  
In Series ◽  
Natural Characteristic

Abstract Chemical structure-odor correlations in the isomers of n-C9-methylene interrupted dienols were explored using synthetic nine isomers of these alcohols. The synthetic dienols were purified by recrystallization or column chromatography of their 3,5-dinitrobenzoate de­ rivatives. Chemical structure-odor correlations in all the isomers of the purified n-nonadien-1-ols were analyzed by treating the data obtained statistically with the principal component analysis method (Sakoda et al., 1995; Cramer et al., 1988) in comparison with those of n-nonen-1-ols. The odor profiles of the n-nonadien-1-ols were attributable largely to the geometries of the isomers, compared with n-nonen -1-ols (Sakoda et al., 1995). With the principal component analysis, the odor profiles of the series of the dienols were successfully integrated into the first and the second principal components. The first component (PC 1) consisted of combined characteristics of fruity, fresh, sweet, herbal and oily-fatty, and the second component (PC 2) leaf or grassy and vegetable-like. Of the methylene interrupted dienol isomers, (2E ,6Z)-and (3Z,6Z)-nonadien-1-ols which are natural products and have (6Z) in the same, deviated markedly from the other isomers as seen in (6Z)-nonen -1-ol of n-nonen-1-ols. That suggests that the double bond of (ω3Z) was an important factor for natural characteristic odor.

scholarly journals On the Performance of Principal Component Liu-Type Estimator under the Mean Square Error Criterion

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Jibo Wu
Keyword(s):  
Principal Component Regression ◽  
Principal Component ◽  
Ordinary Least Squares ◽  
Mean Square ◽  
Ridge Estimator ◽  
Error Criterion ◽  
Ordinary Least Squares Estimator ◽  
The Mean ◽  
Mean Square Error Criterion ◽  
Theoretical Results

Wu (2013) proposed an estimator, principal component Liu-type estimator, to overcome multicollinearity. This estimator is a general estimator which includes ordinary least squares estimator, principal component regression estimator, ridge estimator, Liu estimator, Liu-type estimator,r-kclass estimator, andr-dclass estimator. In this paper, firstly we use a new method to propose the principal component Liu-type estimator; then we study the superior of the new estimator by using the scalar mean squares error criterion. Finally, we give a numerical example to show the theoretical results.

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.

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 A Simple Method for Limiting Disclosure in Continuous Microdata Based on Principal Component Analysis

2017 ◽  
Vol 33 (1) ◽  
pp. 15-41 ◽  
Author(s):  
Aida Calviño
Keyword(s):  
Principal Component Analysis ◽  
Principal Components ◽  
Synthetic Data ◽  
Principal Component ◽  
Component Analysis ◽  
Data Sets ◽  
Simple Method ◽  
Processing Times ◽  
The Mean ◽  
Mean Vector

Abstract In this article we propose a simple and versatile method for limiting disclosure in continuous microdata based on Principal Component Analysis (PCA). Instead of perturbing the original variables, we propose to alter the principal components, as they contain the same information but are uncorrelated, which permits working on each component separately, reducing processing times. The number and weight of the perturbed components determine the level of protection and distortion of the masked data. The method provides preservation of the mean vector and the variance-covariance matrix. Furthermore, depending on the technique chosen to perturb the principal components, the proposed method can provide masked, hybrid or fully synthetic data sets. Some examples of application and comparison with other methods previously proposed in the literature (in terms of disclosure risk and data utility) are also included.

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