Percutaneous Injury Analysis: Consistent Categorization, Effective Reduction Methods, and Future Strategies

1995 ◽  
Vol 16 (10) ◽  
pp. 582-589 ◽  
Author(s):  
Donna J. Haiduven ◽  
Esther S. Phillips ◽  
Karl V. Clemons ◽  
David A. Stevens
Keyword(s):  
Effective Reduction ◽  
Reduction Methods ◽  
Injury Analysis

Percutaneous Injury Analysis: Consistent Categorization, Effective Reduction Methods, and Future Strategies

1995 ◽  
Vol 16 (10) ◽  
pp. 582-589 ◽  
Author(s):  
Donna J. Haiduven ◽  
Esther S. Phillips ◽  
Karl V. Clemons ◽  
David A. Stevens
Keyword(s):  
Effective Reduction ◽  
Reduction Methods ◽  
Injury Analysis

Measuring Reduction Methods for VR Sickness in Virtual Environments

2017 ◽  
Vol 7 (2) ◽  
pp. 27-43 ◽  
Author(s):  
Takurou Magaki ◽  
Michael Vallance
Keyword(s):  
Virtual Environments ◽  
Quantitative Data ◽  
Negative Symptoms ◽  
Reduction Method ◽  
Physiological Data ◽  
Simulator Sickness ◽  
Biological Responses ◽  
Effective Reduction ◽  
Reduction Methods ◽  
Simulator Sickness Questionnaire

Recently, virtual reality (VR) technologies have developed remarkably. However, some users have negative symptoms during VR experiences or post-experiences. Consequently, alleviating VR sickness is a major challenge, but an effective reduction method has not yet been discovered. The purpose of this article is to compare and evaluate VR sickness in two virtual environments (VE). Current known methods of reducing VR sickness were implemented. To measure VR sickness a validated simulator sickness questionnaire (SSQ) was undertaken by the subjects (n=21). In addition, subjects wore a customized biological sensor in order to evaluate their physiological data by measuring responses in three kinds of natural states and two kinds of VR experience states. This quantitative data, as objective evaluations according to the biological responses, is analyzed and considered alongside subjective qualitative evaluations according to the SSQ. The outcomes and limitations of the reduction methods and data collection are discussed.

Speech Emotion Recognition Based on Sparse Representation

2013 ◽  
Vol 38 (4) ◽  
pp. 465-470 ◽  
Author(s):  
Jingjie Yan ◽  
Xiaolan Wang ◽  
Weiyi Gu ◽  
LiLi Ma
Keyword(s):  
Dimensionality Reduction ◽  
Emotion Recognition ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Speech Emotion Recognition ◽  
Least Squares Regression ◽  
Computer Science Pedagogy ◽  
Reduction Methods ◽  
Analysis Computer

Abstract Speech emotion recognition is deemed to be a meaningful and intractable issue among a number of do- mains comprising sentiment analysis, computer science, pedagogy, and so on. In this study, we investigate speech emotion recognition based on sparse partial least squares regression (SPLSR) approach in depth. We make use of the sparse partial least squares regression method to implement the feature selection and dimensionality reduction on the whole acquired speech emotion features. By the means of exploiting the SPLSR method, the component parts of those redundant and meaningless speech emotion features are lessened to zero while those serviceable and informative speech emotion features are maintained and selected to the following classification step. A number of tests on Berlin database reveal that the recogni- tion rate of the SPLSR method can reach up to 79.23% and is superior to other compared dimensionality reduction methods.

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