Study of the influential inputs on the bagged denim fabric behaviors using the principal component analysis method

2015 ◽  
Vol 27 (6) ◽  
pp. 922-939 ◽  
Author(s):  
Mouna Gazzah ◽  
Boubaker Jaouachi ◽  
Laurence Schacher ◽  
Dominique Charles Adolphe ◽  
Faouzi Sakli

Purpose – The purpose of this paper is to predict the appearance of denim fabric after repetitive uses judging the denim cloth behavior and performance in viewpoint of bagging ability. Hence, it attempts to carry out the significant inputs and outputs that have an influence on the bagging behaviors using the Principal Component Analysis (PCA) technique. In this study, the Kawabata Evaluation System parameters such as the frictional characteristics, the bending, compression, tensile and shear parameters are investigated to propose a model highlighting and explaining their impacts on the different bagging properties. To improve the obtained results, the selected significant inputs are also analyzed within their bagging properties using Taguchi experimental design. The linear regressive models prove the effectiveness of the PCA method and the obtained findings. Design/methodology/approach – To investigate the mechanical properties and their contributions on the bagging characteristics, some denim fabrics were collected and measured thanks to the Kawabata evaluation systems (KES-FB1, KES-FB2, KES-FB3 and KES-FB4). These bagging properties were further analyzed applying the method of PCA to acquire factor patterns that indicate the most important fabric properties for characterizing the bagging behaviors of different studied denim fabric samples. An experimental design type Taguchi was, hence, applied to improve the results. Regarding the obtained results, it may be concluded that the PCA method remained a powerful and flawless technique to select the main influential inputs and significant outputs, able to define objectively the bagging phenomenon and which should be considered from the next researches. Findings – According to the results, there are good relationships between the Kawabata input parameters and the analyzed bagging properties of studied denim fabrics. Indeed, thanks to the PCA, it is probably easy to reduce the number of the influent parameters for three reasons. First, applying this technique of selection can help to select objectively the most influential inputs which affect enormously the bagged fabrics. Second, knowing these significant parameters, the prediction of denim fabric bagging seems fruitful and can undoubtedly help researchers explain widely this complex phenomenon. Third, regarding the findings mentioned, it seems that the prevention of this aesthetic phenomenon appearing in some specific zones of denim fabrics will be more and more accurate. Practical implications – This study is interesting for denim consumers and industrial applications during long and repetitive uses. Undoubtedly, the denim garments remained the largely used and consumed, hence, this particularity proves the necessity to study it in order to evaluate the bagging phenomenon which occurs as function of number of uses. Although it is fashionable to have bagging, the denim fabric remains, in contrast with the worsted ones, the most popular fabric to produce garments. Moreover, regarding this characteristic, the large uses and the acceptable value of denim fabrics, their aesthetic appearance behavior due to bagging phenomenon can be analyzed accurately because compared to worsted fabrics, they have a high value and the repetitive tests to investigate widely bagged zones may fall the industrial. The paper has practical implications in the clothing appearance and other textile industry, especially in the weaving process when friction forms (yarn-to-yarn, yarn-to-metal frictions) and stresses are drastic. This can help understanding why residual bagging behavior remained after garment uses due to the internal stress and excessive extensions. Regarding the selected influential inputs and outputs relative to bagging behaviors, there are some practical implications that have an impact on the industrial and researchers to study objectively the occurrence of this aesthetic phenomenon. Indeed, this study discusses the significance of the overall inputs; their contributions on the denim fabric bagged zones aims to prevent their ability to appear after uses. Moreover, the results obtained regarding the fabric mechanical properties can be useful to fabric and garment producers, designers and consumers in specifying and categorizing denim fabric products, insuring more denim cloth use and controlling fabric value. For applications where the subjective view of the consumer is of primary importance, the KES-FB system yields data that can be used for evaluating fabric properties objectively and prejudge the consumer satisfaction in viewpoint of the bagging ability. Therefore, this study shows that by measuring shear, tensile and frictional parameters of KES-FB, it may be possible to evaluate bagging properties. However, it highlights the importance and the significance of some inputs considered influential or the contrast (non-significant) in other researches. Originality/value – This work presents the first study analyzing the bagged denim fabric applying the PCA technique to remove the all input parameters which are not significant. Besides, it deals with the relationship developed between the mechanical fabric properties (tensile, shear and frictional stresses) and the bagging properties behavior. To improve these obtained relationships, for the first time, the regression technique and experimental design type Taguchi analysis were both applied. Moreover, it is notable to mention that the originality of this study is to let researchers and industrials investigate the most influential inputs only which have a bearing on the bagging phenomenon.

2020 ◽  
Vol 71 (05) ◽  
pp. 438-445
Author(s):  
Liliana Hristian ◽  
Maria Magdalena Ostafe ◽  
Ionut Dulgheru ◽  
Liliana Buhu ◽  
Adrian Buhu ◽  
...  

The paper aims to highlight the correlations between the physical-mechanical properties of woven fabrics made of combed wool yarn with different fibrous compositions, using the Principal Component Analysis (PCA) method. Based on the information on each fabric/assortment group, it is found that four main factors were extracted: durability, comfort, production price and fabric design. The extracted components explain 92% of the variability of the twelve initial inter-correlated variables, so that we can considerably reduce the complexity of the data configured using these components, with only 8% loss of information. This technique that follows the controlled reduction of the number of variables can be used subsequently for modelling the physical-mechanical properties of the fabrics and for selecting the most suitable fabrics to satisfy the requirements of a particular field of use


2017 ◽  
Vol 44 (6) ◽  
pp. 715-731 ◽  
Author(s):  
Ivy Drafor

Purpose The purpose of this paper is to analyse the spatial disparity between rural and urban areas in Ghana using the Ghana Living Standards Survey’s (GLSS) rounds 5 and 6 data to advance the assertion that an endowed rural sector is necessary to promote agricultural development in Ghana. This analysis helps us to know the factors that contribute to the depravity of the rural sectors to inform policy towards development targeting. Design/methodology/approach A multivariate principal component analysis (PCA) and hierarchical cluster analysis were applied to data from the GLSS-5 and GLSS-6 to determine the characteristics of the rural-urban divide in Ghana. Findings The findings reveal that the rural poor also spend 60.3 per cent of their income on food, while the urban dwellers spend 49 per cent, which is an indication of food production capacity. They have low access to information technology facilities, have larger household sizes and lower levels of education. Rural areas depend a lot on firewood for cooking and use solar/dry cell energies and kerosene for lighting which have implications for conserving the environment. Practical implications Developing the rural areas to strengthen agricultural growth and productivity is a necessary condition for eliminating spatial disparities and promoting overall economic development in Ghana. Addressing rural deprivation is important for conserving the environment due to its increased use of fuelwood for cooking. Absence of alternatives to the use of fuelwood weakens the efforts to reduce deforestation. Originality/value The application of PCA to show the factors that contribute to spatial inequality in Ghana using the GLSS-5 and GLSS-6 data is unique. The study provides insights into redefining the framework for national poverty reduction efforts.


2014 ◽  
Vol 926-930 ◽  
pp. 4085-4088
Author(s):  
Chuan Jun Li

This article uses the PCA method (Principal component analysis) to evaluate the level of corporate governance. PCA is used to analyze the correlation among 10 original indicators, and extract some principal components so that most of the information of the original indicators is extracted. The formulation of the index of corporate governance can be got by calculating the weight based on the variance contribution rate of the principal component, which can comprehensively evaluate corporate governance.


2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Tai-Xiang Jiang ◽  
Ting-Zhu Huang ◽  
Xi-Le Zhao ◽  
Tian-Hui Ma

We have proposed a patch-based principal component analysis (PCA) method to deal with face recognition. Many PCA-based methods for face recognition utilize the correlation between pixels, columns, or rows. But the local spatial information is not utilized or not fully utilized in these methods. We believe that patches are more meaningful basic units for face recognition than pixels, columns, or rows, since faces are discerned by patches containing eyes and noses. To calculate the correlation between patches, face images are divided into patches and then these patches are converted to column vectors which would be combined into a new “image matrix.” By replacing the images with the new “image matrix” in the two-dimensional PCA framework, we directly calculate the correlation of the divided patches by computing the total scatter. By optimizing the total scatter of the projected samples, we obtain the projection matrix for feature extraction. Finally, we use the nearest neighbor classifier. Extensive experiments on the ORL and FERET face database are reported to illustrate the performance of the patch-based PCA. Our method promotes the accuracy compared to one-dimensional PCA, two-dimensional PCA, and two-directional two-dimensional PCA.


2019 ◽  
Vol 4 (2) ◽  
pp. 359-366
Author(s):  
Irfan Maibriadi ◽  
Ratna Ratna ◽  
Agus Arip Munawar

Abstrak,  Tujuan dari penelitian ini adalah mendeteksi kandungan dan kadar formalin pada buah tomat dengan menggunakan instrument berbasis teknologi Electronic nose. Penelitian ini menggunakan buah tomat yang telah direndam dengan formalin dengan kadar 0.5%, 1%, 2%, 3%, 4%, dan buah tomat tanpa perendaman dengan formalin (0%). Jumlah sampel yang digunakan pada penelitian ini adalah sebanyak 18 sampel. Pengukuran spektrum beras menggunakan sensor Piezoelectric Tranducer. Klasifikasi data spektrum buah tomat menggunakan metode Principal Component Analysis (PCA) dengan pretreatment nya adalah Gap Reduction. Hasil penelitian ini diperoleh yaitu: Hidung elektronik mulai merespon aroma formalin pada buah tomat pada detik ke-8.14, dan dapat mengklasifikasikan kandungan dan kadar formalin pada buah tomat pada detik ke 25.77. Hidung elektronik yang dikombinasikan dengan metode principal component analysis (PCA) telah berhasil mendeteksikandungan dan kadar formalin pada buah tomat dengan tingkat keberhasilan sebesar 99% (PC-1 sebesar 93% dan PC-2 sebesar 6%). Perbedaan kadar formalin menjadi faktor utama yang menyebabkan Elektronik nose mampu membedakan sampel buah tomat yang diuji, karena semakin tinggi kadar formalin pada buah tomat maka aroma khas dari buah tomat pun semakin menghilang, sehingga Electronic nose yang berbasis kemampuan penciuman dapat membedakannya.Detect Formaldehyde on Tomato (Lycopersicum esculentum Mill) With Electronic Nose TechnologyAbstract, The purpose of this study is to detect the contents and levels of formalin in tomatoes by using instruments based on Electronic nose technology. This study used tomatoes that have been soaked in formalin with a concentration of 0.5%, 1%, 2%, 3%, 4%, 5% and tomatoes without soaking with formalin (0%). The samples in this study were 18 samples. The measurements of the intensity on tomatoes aroma were using Piezoelectric Transducer sensors. The classification of tomato spectrum data was using the Principal Component Analysis (PCA) method with Gap Reduction pretreatment. The results of this study were obtained: the Electronic nose began to respond the smell of formalin on tomatoes at 8.14 seconds, and it could classify the content and formalin levels in tomatoes at 25.77 seconds. Electronic nose combined with the principal component analysis (PCA) method have successfully detected the content and levels of formalin in tomatoes with a success rate at 99% (PC-1 of 93% and PC-2 of 6%). The difference of grade formalin levels is the main factor that causes Electronic nose to be able to distinguish the tomato samples tested, because the higher of formalin content in tomatoes, the distinctive of tomatoes aroma is increasingly disappearing. Thereby, the Electronic nose based on  the olfactory ability can distinguish them. 


2020 ◽  
pp. 004051752097720
Author(s):  
Yuan Tian ◽  
Yi Sun ◽  
Zhaoqun Du ◽  
Dongming Zheng ◽  
Haochen Zou ◽  
...  

Down jacket fabric is greatly important in determining the quality of a down jacket. In order to enrich the research on fabric handle, subjective and objective evaluations were made for down jacket fabrics that were less studied. The comprehensive handle evaluation system for fabrics and yarns (CHES-FY) can be used to evaluate the tactile handle of the fabric by accurately and efficiently measuring the basic mechanical properties of the fabric. Therefore, the CHES-FY was used to link the objective evaluation with the subjective handle, so as to effectively estimate the total handle value of the down jacket fabric. Fifty-two kinds of down jacket fabrics were objectively tested through measuring 17 extracted parameters, and principal component analysis was adopted to establish the five main handle characteristics of fullness, softness, stiffness, smoothness, looseness and tightness to characterize basic style of the down jacket fabrics. The results showed that the subjective and objective results were in good agreement. These characteristics can be used as indicators to characterize fabric performance, and the principal component expression to characterize fabric handle can better predict the handle characteristics of down jacket fabrics. This also proves that the CHES-FY can quickly and accurately obtain the fabric handle value, and can also evaluate the fabric quality level.


2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Yuchou Chang ◽  
Haifeng Wang

A phased array with many coil elements has been widely used in parallel MRI for imaging acceleration. On the other hand, it results in increased memory usage and large computational costs for reconstructing the missing data from such a large number of channels. A number of techniques have been developed to linearly combine physical channels to produce fewer compressed virtual channels for reconstruction. A new channel compression technique via kernel principal component analysis (KPCA) is proposed. The proposed KPCA method uses a nonlinear combination of all physical channels to produce a set of compressed virtual channels. This method not only reduces the computational time but also improves the reconstruction quality of all channels when used. Taking the traditional GRAPPA algorithm as an example, it is shown that the proposed KPCA method can achieve better quality than both PCA and all channels, and at the same time the calculation time is almost the same as the existing PCA method.


2007 ◽  
Vol 04 (01) ◽  
pp. 15-26 ◽  
Author(s):  
XIUQING WANG ◽  
ZENG-GUANG HOU ◽  
LONG CHENG ◽  
MIN TAN ◽  
FEI ZHU

The ability of cognition and recognition for complex environment is very important for a real autonomous robot. A new scene analysis method using kernel principal component analysis (kernel-PCA) for mobile robot based on multi-sonar-ranger data fusion is put forward. The principle of classification by principal component analysis (PCA), kernel-PCA, and the BP neural network (NN) approach to extract the eigenvectors which have the largest k eigenvalues are introduced briefly. Next the details of PCA, kernel-PCA and the BP NN method applied in the corridor scene analysis and classification for the mobile robots based on sonar data are discussed and the experimental results of those methods are given. In addition, a corridor-scene-classifier based on BP NN is discussed. The experimental results using PCA, kernel-PCA and the methods based on BP neural networks (NNs) are compared and the robustness of those methods are also analyzed. Such conclusions are drawn: in corridor scene classification, the kernel-PCA method has advantage over the ordinary PCA, and the approaches based on BP NNs can also get satisfactory results. The robustness of kernel-PCA is better than that of the methods based on BP NNs.


2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Fadi Afif Fayyad ◽  
Filip Vladimir Kukić ◽  
Nemanja Ćopić ◽  
Nenad Koropanovski ◽  
Milivoj Dopsaj

PurposeThe purpose of the study is to determine the prevalence of stress and to identify the occupational stressors among Lebanese police officers.Design/methodology/approachOperational Police Stress Questionnaire (PSQ-op) was addressed to 100 randomly selected male Lebanese Police officers. Twenty items from the PSQ-op were run through the principal component analysis to determine the most significant factors of stress and loading within each of the factors.FindingsThe results indicated that 59% of officers reported moderate stress level and 41% reported strenuous stress. Principal component analysis identified six independent factors or stress among Lebanese police officers explaining in total 72.1% of the total variance: excessive workload (30.6%), social-life time management (12.8%), occupational fitness (9.1%), success-related stress (8.6%), physical and psychological health (5.8%), and working alone at night (5.2%).Research limitations/implicationsThis research approach encountered some limitations so further research must: use a larger sample size, include female gender and identify other sources of stressors mainly organizational or job context stressors.Originality/valueAddressing and understanding stress factors among Lebanese police officers helps improving awareness and developing individualized treatment strategies leading police officers to engage in stress-management training to learn coping strategies and use effective tools for preventing stress before it becomes chronic.


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