Research on Product Identity Design Based on Kansei Image in Mechanic Equipment

2013 ◽  
Vol 651 ◽  
pp. 569-574
Author(s):  
Peng Wang ◽  
Jian Ning Su ◽  
Chi Bing Hu ◽  
Shu Tao Zhang
Keyword(s):  
Testing Machine ◽  
Design Feature ◽  
Principal Component ◽  
Feature Space ◽  
Image Space ◽  
Analysis Method ◽  
Kansei Engineering ◽  
Design Elements ◽  
Product Identity ◽  
Kj Method

On the study of Kansei engineering and product identity, This paper presents the key methods and process of product identity design on Kansei image, including locating the Kansei image space, identifying the design feature space, mapping relation between Kansei image and design elements, mining product semantic. The research also establishes the Kansei image space model on the three cognitive dimensions of visual, quality and brand, and summarizes the design of processes and methods. In those aspects, some Kansei engineering methods are used to achieve industrial design, such as KJ method, SD method, Principal component analysis method, Quantification-I theory method. It is verified after the practical application of Hongshan testing machine that the method and process is reasonable and feasible.

Kansei Quantitative Analysis and its Application to Drum Washing Machine Apperence Design

2013 ◽  
Vol 401-403 ◽  
pp. 193-196 ◽  
Author(s):  
Sai He ◽  
Jian Ming Che
Keyword(s):  
Principal Component Analysis ◽  
Statistical Analysis ◽  
Quantitative Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Analysis Method ◽  
Kansei Engineering ◽  
Washing Machine ◽  
Design Elements ◽  
Statistical Analysis Method

Kansei Engineering (KE) refers to the translation of consumers' emotional requirements about a product into perceptual design elements. The Kansei Engineering is applied to the drum washing machine aided by a variety of engineering mean.Semantic differential (SD) is applied to extract the kansei tags.Multivariate statistical analysis method is also used for data mining.The data of design elements is processed by principal component analysis (PCA) and SPSS.

scholarly journals Principal Component Analysis based on data characteristics for dimensionality reduction of ECG recordings in arrhythmia classification

Open Physics ◽  
2019 ◽  
Vol 17 (1) ◽  
pp. 489-496 ◽  
Author(s):  
Agnieszka Wosiak
Keyword(s):  
Principal Component Analysis ◽  
Heart Diseases ◽  
Principal Component ◽  
Feature Space ◽  
Data Interpretation ◽  
Component Analysis ◽  
Feature Reduction ◽  
Analysis Method ◽  
Intrinsic Structure ◽  
Insight Into

Abstract Due to the growing problem of heart diseases, the computer improvement of their diagnostics becomes of great importance. One of the most common heart diseases is cardiac arrhythmia. It is usually diagnosed by measuring the heart activity using electrocardiograph (ECG) and collecting the data as multidimensional medical datasets. However, their storage, analysis and knowledge extraction become highly complex issues. Feature reduction not only enables saving storage and computing resources, but it primarily makes the process of data interpretation more comprehensive. In the paper the new igPCA (in-group Principal Component Analysis) method for feature reduction is proposed. We assume that the set of attributes can be split into subgroups of similar characteristic and then subjected to principal component analysis. The presented method transforms the feature space into a lower dimension and gives the insight into intrinsic structure of data. The method has been verified by experiments done on a dataset of ECG recordings. The obtained effects have been evaluated regarding the number of kept features and classification accuracy of arrhythmia types. Experiment results showed the advantage of the presented method compared to base PCA approach.

The style design of professional female vest based on kansei engineering

2019 ◽  
Vol 32 (1) ◽  
pp. 5-11 ◽  
Author(s):  
Daoling Chen ◽  
Pengpeng Cheng
Keyword(s):  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Analysis Method ◽  
Kansei Engineering ◽  
Content Type ◽  
Design Elements ◽  
Perceptual Evaluation ◽  
Correlation Analysis Method ◽  
Perceptual Assessment ◽  
Fifth Order

Purpose The purpose of this paper is to study the style design methods of professional female vests that meet the emotional needs of consumers. Design/methodology/approach Using the theory of kansei engineering as a guide to screen representative samples of female professional vests and relevant emotional vocabularies of styles, through morphological analysis, style design elements of female professional vests are extracted, the fifth-order semantic difference questionnaire was used to establish the perceptual assessment matrix for design elements, the correlation analysis method and multiple linear regression analysis were used to analyze the results of the perceptual evaluation of the sample, find out the relationship between the perceptual vocabulary and design elements of professional female vest styles, and establish a regression model, finally, it is verified by random samples of the market, so as to guide the development of new products. Findings The seven design elements extracted from professional female vest styles have an impact on consumer perception, by using a linear analysis method, the correspondence between perceptual perception of consumers and style design elements can be quantified and a model can be established to accurately predict consumers’ perceptual intentions. Originality/value The application of perceptual engineering in the style design of professional female vests provides a new idea for the design of clothing styles. It helps garment companies and designers to determine the development direction of professional woman’s vest styles, while the research results provide design reference for other products.

scholarly journals Emotional augmented reality-based mobile learning design elements: a kansei engineering approach

2019 ◽  
Vol 14 (1) ◽  
pp. 413 ◽  
Author(s):  
Fauziah Redzuan ◽  
An-Nur Atiqah Khairuddin ◽  
Nor Aziah Daud
Keyword(s):  
Augmented Reality ◽  
Mobile Learning ◽  
Principal Component ◽  
Semantic Space ◽  
Learning Material ◽  
Kansei Engineering ◽  
Learning Materials ◽  
Study Objective ◽  
Design Elements ◽  
Mobile Ar

<span>In recent times, various studies have shown that Augmented Reality (AR) will be the next wave of online learning. This is because of the advent of powerful smartphones that has changed user experiences, thereby able to increase the capability of AR. There has been much concentration in previous studies on cognition towards the use of AR in education, in which little consideration has been given to emotions which is also an important aspect in learning. Based on this, the present research aims to identify salient connections between emotions and design elements of AR-based mobile learning material through the application of the Kansei Engineering (KE) approach. In order to achieve this study objective, the use of a human heart in relation to the mobile AR application of the KE approach was adopted in this research as a case study, in which seven specimens of the mobile AR application were evaluated including 55 emotions of Kansei Words (KW). Additionally, the kansei evaluation experiment of this study was carried out by 28 students from one of the public universities, after which the data were analysed using Factor and Principal Component Analysis. The results of this study show the important pillars of emotions or Kansei semantic space of emotions for AR-based mobile learning materials. Based on Factor Analysis, it revealed four main pillars; <em>professional-motivated</em>, <em>confused</em>, <em>wandering-thrilled</em>, <em>challenging</em> and one additional pillar; <em>trustable</em>. Besides that, this research also described design elements of AR-based mobile learning material that might evoke specific emotions based on the identified pillars. Finally, the findings of this research are hoped to be applicable as a guide in design during preparation of AR-based mobile learning materials with affective elements in the future.</span>

PENELITIAN DAN PENGEMBANGAN SENSOR AROMATIK SEBAGAI ALAT DETEKSI ALKOHOL PADA PARFUM DAN WEWANGIAN UNTUK AUTENTIKASI PRODUK PARFUM HALAL

2017 ◽  
Vol 18 (2) ◽  
pp. 302-322
Author(s):  
Fajar Hardoyono
Keyword(s):  
Sensor Array ◽  
Principal Component ◽  
Metal Oxide Semiconductor ◽  
Oxide Semiconductor ◽  
Response Patterns ◽  
Data Recording ◽  
Analysis Method ◽  
Sample Group ◽  
Array Pattern ◽  
Group Data

Abstract: The development of aromatic sensor array instrument for the detection of alcohol in perfume. The research was conducted by developing the sensor array using 8 sensors made of metal oxide semiconductor. The sensor types used in this study consisted of TGS 813, TGS 822, TGS 2600, TGS 826, TGS 2611, TGS 2620, TGS 2612 and TGS 2602. Response patterns of 8 sensors formed a sensor array pattern used to detect the aroma of 2 groups of samples perfume made from the essential oil of ginger. The first sample group is pure ginger atsiri oil without mixed alcohol. The second sample group was made from the ginger atsiri oil mixed with alcohol with a level of 0.02 M. The results of the data recording show that the developed instrument is able to dissect the first sample group with the second sample group. Data analysis using principal component analysis method (PCA shows that the instrument is able to distinguish the contaminated alcohol perfume group 0.2 M with the alcohol-free perfume group with 100% accuracy. Keywords: Sensor Aroma, Perfume.

scholarly journals Modeling, comparison and evaluation of one-DOF six-bar bioinspired jumping leg mechanisms

2021 ◽  
Vol 13 (2) ◽  
pp. 168781402199295
Author(s):  
Ziqiang Zhang ◽  
Qi Yang ◽  
Xingkun Liu ◽  
Chuanzhong Zhang ◽  
Jinnong Liao
Keyword(s):  
Center Of Mass ◽  
Principal Component ◽  
Analysis Method ◽  
Index Distribution ◽  
Inertial Moment ◽  
Structure Index ◽  
Mean And Variance ◽  
Research Objects ◽  
Leg Mechanisms ◽  
Selection Of

One degree-of-freedom (DOF) jumping leg has the advantages of simple control and high stiffness, and it has been widely used in bioinspired jumping robots. Compared with four-bar jumping leg, six-bar jumping leg mechanism can make the robot achieve more abundant motion rules. However, the differences among different configurations have not been analyzed, and the choice of configurations lacks basis. In this study, five Watt-type six-bar jumping leg mechanisms were selected as research objects according to the different selection of equivalent tibia, femur and trunk link, and a method for determining the dimension of the jumping leg was proposed based on the movement law of jumping leg of locust in take-off phase. On this basis, kinematics indices (sensitivity of take-off direction angle and trunk attitude angle), dynamics indices (velocity loss, acceleration fluctuation, and mean and variance of total inertial moment) and structure index (distribution of center of mass) were established, and the differences of different configurations were compared and analyzed in detail. Finally, according to the principal component analysis method, the optimal selection method for different configurations was proposed. This study provides a reference for the design of one DOF bioinspired mechanism.

Anatomization of the systems of dimension relaxation for facial recognition

2020 ◽  
pp. 1-11
Author(s):  
Mayamin Hamid Raha ◽  
Tonmoay Deb ◽  
Mahieyin Rahmun ◽  
Tim Chen
Keyword(s):  
Face Recognition ◽  
Main Idea ◽  
Principal Component ◽  
Feature Space ◽  
Projection Methods ◽  
Data Representation ◽  
Linear Discriminant ◽  
Class Differences ◽  
Underlying Space ◽  
Maximum Variance

Face recognition is the most efficient image analysis application, and the reduction of dimensionality is an essential requirement. The curse of dimensionality occurs with the increase in dimensionality, the sample density decreases exponentially. Dimensionality Reduction is the process of taking into account the dimensionality of the feature space by obtaining a set of principal features. The purpose of this manuscript is to demonstrate a comparative study of Principal Component Analysis and Linear Discriminant Analysis methods which are two of the highly popular appearance-based face recognition projection methods. PCA creates a flat dimensional data representation that describes as much data variance as possible, while LDA finds the vectors that best discriminate between classes in the underlying space. The main idea of PCA is to transform high dimensional input space into the function space that displays the maximum variance. Traditional LDA feature selection is obtained by maximizing class differences and minimizing class distance.

Study on the Effect Factors of Non-Perormance Loan Ratio of Chinese Commerical Banks

2011 ◽  
Vol 50-51 ◽  
pp. 728-732
Author(s):  
Ping Li ◽  
Ming Ying Zhuo ◽  
Li Chao Feng ◽  
Rui Zhang
Keyword(s):  
Principal Component Analysis ◽  
Commercial Banks ◽  
Principal Component ◽  
Component Analysis ◽  
Analysis Method ◽  
Analysis Model ◽  
Principal Component Analysis Model ◽  
Effect Factors ◽  
Financial Indicator ◽  
Commerical Banks

Non-performance loan ratio is one of the important assessment criteria of the security of credit assets. It is also an important financial indicator to evaluate the general strength of commercial banks. Using principal component analysis method and statistical software SPSS16.0 and based on the non-performance loan ratio and relative data of some commercial banks in China in 2007, this paper provided a principal component analysis model for the non-performance loan ratio of China’s commercial banks. The factors that affect the non-performance loan ratio were refined in this paper. Finally, the characteristics of effect factors of each bank were analyzed and compared in detail.

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