Estimation of fabric opacity by scanner

Sensor Review ◽  
2014 ◽  
Vol 34 (4) ◽  
pp. 404-409
Author(s):  
Abbas Hajipour ◽  
Ali Shams Nateri ◽  
Alireza Sadr Momtaz
Keyword(s):  
Textile Industry ◽  
Polynomial Regression ◽  
Low Cost ◽  
Color Space ◽  
Simple Method ◽  
Content Type ◽  
Textile Fabrics ◽  
Order Polynomial ◽  
Textile Fabric ◽  
Fourth Order Polynomial

Purpose – This study aimed to use a scanner as a low-cost method for measuring the opacity of textile fabric. Textile fabrics must have specific ranges of opacity according to their uses for shirting, curtaining, etc. In this way, opacity is an important property in the textile industry. Conventionally, textile opacity is estimated using a spectrophotometer, which is an expensive method. Design/methodology/approach – In this study a scanner was used as a low-cost method for measuring the opacity of textile fabric. The opacity was estimated by using red, green and blue (RGB) parameters of images of fabric against white and black background. Findings – The accuracy of opacity estimation was improved by converting RGB into several color spaces. The best opacity estimation was obtained by using the XYZ color space. In addition, using a regression method, the best estimation was obtained by using a fourth-order polynomial regression with the LSLM color space. Originality/value – The opacity of fabric has been measured by spectrophotometer, but in this study, the opacity of fabric was measured by scanner as a low cost device and also with novel and simple method. This method achieved acceptable accuracy for opacity estimation. The obtained result is comparable with spectrophotometer results.

An intelligent detection and assessment method based on textile fabric image feature

2019 ◽  
Vol 31 (3) ◽  
pp. 390-402 ◽  
Author(s):  
Xueqing Zhao ◽  
Xin Shi ◽  
Kaixuan Liu ◽  
Yongmei Deng
Keyword(s):  
Frequency Spectrum ◽  
Textile Industry ◽  
Assessment Method ◽  
Image Feature ◽  
Textile Fiber ◽  
Textile Fibers ◽  
Content Type ◽  
Intelligent Detection ◽  
Textile Fabric

PurposeThe quality of produced textile fibers plays a very important role in the textile industry, and detection and assessment schemes are the key problems. Therefore, the purpose of this paper is to propose a relatively simple and effective technique to detect and assess the quality of produced textile fibers.Design/methodology/approachIn order to achieve automatic visual inspection of fabric defects, first, images of the textile fabric are pre-processed by using Block-Matching and 3-D (BM3D) filtering. And then, features of textile fibers image are respectively extracted, including color, texture and frequency spectrum features. The color features are extracted by using hue–saturation–intensity model, which is more consistent with the human vision perception model; texture features are extracted by using scale-invariant feature transform scheme, which is a quite good method to detect and describe the local image features, and the obtained features are robust to local geometric distortion; frequency spectrum features of textiles are less sensitive to noise and intensity variations than spatial features. Finally, for evaluating the quality of the fabric in real time, two quantitatively metric parameters, peak signal-to-noise ratio and structural similarity, are used to objectively assess the quality of textile fabric image.FindingsCompared to the quality between production and pre-processing of textile fiber images, the BM3D filtering method is a very efficient technology to improve the quality of textile fiber images. Compared to the different features of textile fibers, like color, texture and frequency spectrum, the proposed detection and assessment method based on textile fabric image feature can easily detect and assess the quality of textiles. Moreover, the objective metrics can further improve the intelligence and performance of detection and assessment schemes, and it is very simple to detect and assess the quality of textiles in the textile industry.Originality/valueAn intelligent detection and assessment method based on textile fabric image feature is proposed, which can efficiently detect and assess the quality of textiles, thereby improving the efficiency of textile production lines.

Markov chain based modelling and prediction of natural gas allocation structure in Pakistan

2020 ◽  
Vol 14 (5) ◽  
pp. 911-933
Author(s):  
Hussaan Ahmad ◽  
Nasir Hayat
Keyword(s):  
Markov Chain ◽  
Natural Gas ◽  
Polynomial Regression ◽  
Markov Chain Model ◽  
Chain Model ◽  
Transition Matrices ◽  
Allocation Pattern ◽  
Content Type ◽  
Structural Prediction ◽  
Order Polynomial

Purpose The purpose of this paper is to analyze the historical gas allocation pattern for seeking appropriate arrangement and utilization of potentially insufficient natural gas supply available in Pakistan up to 2030. Design/methodology/approach This study presents Markov chain-based modeling of historical gas allocation data followed by its validation through error evaluation. Structural prediction using classical Chapman–Kolmogorov method and varying-order polynomial regression in the historical transition matrices are presented. Findings Markov chain model reproduces the terminal state vector with 99.8 per cent accuracy, thus demonstrating its validity for capturing the history. Lower order polynomial regression results in better structural prediction compared with higher order ones in terms of closeness with Markov approach-based prediction. Research limitations/implications The data belongs to a certain geographic region with specific gas demand and supply profile. The proposition may be tested further by researchers to check the validity for other comparable structural predictions/analyses. Practical implications This study can facilitate policy-making in the field of natural gas allocation and management in Pakistan specifically and other comparable countries generally. Originality/value Two major literature gaps filled through this study are: first, Markov chain model becomes stationary when projected using Chapman–Kolmogorov relation in terms of a fixed, average transition matrix resulting in an equilibrium state after a finite number of future steps. Second, most of the previous studies analyze various gas consumption sectors individually, thus lacking integrated gas allocation policy.

Research and application of K/S value in stain identification

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Qingbin Cui ◽  
Fenjuan Shao
Keyword(s):  
Color Difference ◽  
Simple Method ◽  
Content Type ◽  
Stain Removal ◽  
Before And After ◽  
S Curve ◽  
Smart Home Appliances ◽  
Stain Analysis ◽  
Textile Fabric ◽  
Practical Implications

Purpose The intelligent identification of stains can quickly and accurately identify stains. At present, stains are identified subjectively by appearance, color, taste, feel, location, etc. Color is an important factor in identifying stains. K/S value is used to analyze the color of textile fabric, and it has additivity. The purpose of the study is to explore its application in stain recognition is of great significance to intelligent washing. Design/methodology/approach A certain method used to stain the textile, then the K/S value of the textile before and after the stain was analyzed and tested by the color difference instrument. The K/S curve of the stain was calculated by the addition of K/S, and then the stain was identified and distinguished. Findings The K/S value of the textile stained with stains could be deducted by the K/S value of the color difference meter. After deducting the base cloth, the K/S curve of the same stain is basically the same. Then the stain can be identified and analyzed. Research limitations/implications The K/S value can be used for stain analysis, but it needs to be analyzed and tested in the laboratory. Practical implications This study provides a simple method for stains identification. Originality/value In addition to common methods of stain identification, such as appearance, color, feel, smell, location, stain removal materials, breaking the substrate, IR, etc., K/S value can be used for stain analysis. Identifying stains and washing them in a targeted way to achieve a better washing effect could provide certain technical support for the development of smart washing and smart home appliances.

Ensemble learning-based CNN for textile fabric defects classification

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xueqing Zhao ◽  
Min Zhang ◽  
Junjun Zhang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Ensemble Learning ◽  
Textile Industry ◽  
Visual Fatigue ◽  
Content Type ◽  
Fabric Defect ◽  
Low Efficiency ◽  
Defects Classification ◽  
Textile Fabric

PurposeClassifying the types of fabric defects in the textile industry requires a way to effectively detect. The traditional textile fabric defects detection method is human eyes, which performs very low efficiency and high cost. Therefore, how to improve the classification accuracy of textile fabric defects by using current artificial intelligence and to better meet the needs in the textile industry, the purpose of this article is to develop a method to improve the accuracy of textile fabric defects classification.Design/methodology/approachTo improve the accuracy of textile fabric defects classification, an ensemble learning-based convolutional neural network (CNN) method in terms of textile fabric defects classification (short for ECTFDC) on an enhanced TILDA database is used. ECTFDC first adopts ensemble learning-based model to classify five types of fabric defects from TILDA. Subsequently, ECTFDC extracts features of fabric defects via an ensemble multiple convolutional neural network model and obtains parameters by using transfer learning method.FindingsThe authors applied ECTFDC on an enhanced TILDA database to improve the robustness and generalization ability of the proposed networks. Experimental results show that ECTFDC outperforms the other networks, the precision and recall rates are 97.8%, 97.68%, respectively.Originality/valueThe ensemble convolutional neural network textile fabric defect classification method in this paper can quickly and effectively classify textile fabric defect categories; it can reduce the production cost of textiles and it can alleviate the visual fatigue of inspectors working for a long time.

Effective management of international technology transfer projects

2014 ◽  
Vol 25 (1) ◽  
pp. 69-99 ◽  
Author(s):  
Milton Vieira Junior ◽  
Wagner Cezar Lucato ◽  
Rosangela Maria Vanalle ◽  
Kalinga Jagoda
Keyword(s):  
Technology Transfer ◽  
Textile Industry ◽  
New Technologies ◽  
Low Cost ◽  
Far East ◽  
Business Strategies ◽  
Local Market ◽  
International Technology Transfer ◽  
Content Type ◽  
Textile Sector

Purpose – The Brazilian textile industry has been facing fierce competition from low-cost imports from China and other Far East countries. To maintain their competitiveness in the local market, Brazilian companies have been adopting the product differentiation strategy. By using new technologies, they are able to develop new products with better quality at lower costs. With regard to new technologies, companies in the Brazilian textile industry have been using get-some and buy-some strategy, and international technology transfer (TT) has become an important part of their business strategies. However, due to lack of planning, many projects failed to achieve the desired results. This paper aims to provide theoretical insights and practical guidance on how textile firms could use a stage-gate model to enhance the effectiveness of their TT projects. Design/methodology/approach – In order to investigate the TT practices in the Brazilian context, three issues are assessed. First, the paper evaluates the possibility of deploying TT practices used by firms in similar industries, to enhance the effectiveness of TT process. Second, it verifies whether it is possible for the textile firms to use a stage-gate model to manage their TT processes, using as a normative framework the stage-gate model proposed by Jagoda and Ramanathan and Jagoda et al. Finally, possible changes to the stage-gate model are evaluated to specifically fit the Brazilian textile sector. This step is accomplished through four case studies from the Brazilian textile industry. Findings – The analyses of TT projects carried out by four companies show that there are many similarities and differences among the TT practices that are employed by the four companies that were investigated. The evaluation of the TT practices of the Brazilian textile companies against the stage-gate framework allowed authors to identify the gaps between the model and the TT practices of the companies investigated. Broader guidelines in adapting the stage-gate model to improve the TT process in the textile industry are discussed in the final part of this study. Originality/value – The TT process in the Brazilian textile industry is not a widely investigated phenomenon; however, this process has been critical to enhancing Brazil's competitiveness. Thus, providing a better framework to support the TT process in the local textile sector could be relevant information for improving management action in the area.

scholarly journals Direct Integration Methods of the N-Body Problem

1971 ◽  
Vol 10 ◽  
pp. 118-132 ◽  
Author(s):  
S. J. Aarseth
Keyword(s):  
Body Problem ◽  
Computational Algorithm ◽  
Close Binaries ◽  
Polynomial Method ◽  
Time Step ◽  
Simple Method ◽  
Regularization Procedure ◽  
N Body Problem ◽  
Order Polynomial ◽  
Fourth Order Polynomial

AbstractA fourth-order polynomial method for the integration of N-body systems is described in detail together with the computational algorithm. Most particles are treated efficiently by an individual time-step scheme but the calculation of close encounters and persistent binary orbits is rather time-consuming and is best performed by special techniques. A discussion is given of the Kustaanheimo-Stiefel regularization procedure which is used to integrate dominant two-body encounters as well as close binaries. Suitable decision-making parameters are introduced and a simple method is developed for regularizing an arbitrary number of simultaneous two-body encounters.

Segmentation of defects in textile fabric with robust texture representation and total variation

2020 ◽  
Vol 32 (6) ◽  
pp. 813-823
Author(s):  
Jian Zhou ◽  
Jianli Liu
Keyword(s):  
Total Variation ◽  
Dictionary Learning ◽  
Inspection System ◽  
Content Type ◽  
Robust Representation ◽  
Textile Fabrics ◽  
Human Eyes ◽  
Textile Fabric ◽  
Critical Process ◽  
Selection Of

PurposeVisual quality control on raw textile fabrics is a vital process in weaving factories to ensure their exterior quality (visual defects or imperfection) satisfying customer requirements. Commonly, this critical process is manually conducted by human inspectors, which can hardly provide a fast and reliable inspection results due to fatigue and subjective errors. To meet modern production needs, it is highly demanded to develop an automated defect inspection system by replacing human eyes with computer vision.Design/methodology/approachAs a structural texture, fabric textures can be effectively represented by a linearly summation of basic elements (dictionary). To create a robust representation of a fabric texture in an unsupervised manner, a smooth constraint is imposed on dictionary learning model. Such representation is robust to defects when using it to recover a defective image. Thus an abnormal map (likelihood of defective regions) can be computed by measuring similarity between recovered version and itself. Finally, the total variation (TV) based model is built to segment defects on the abnormal map.FindingsDifferent from traditional dictionary learning method, a smooth constraint is introduced in dictionary learning that not only able to create a robust representation for fabric textures but also avoid the selection of dictionary size. In addition, a TV based model is designed according to defects' characteristics. The experimental results demonstrate that (1) the dictionary with smooth constraint can generate a more robust representation of fabric textures compared to traditional dictionary; (2) the TV based model can achieve a robust and good segmentation result.Originality/valueThe major originality of the proposed method are: (1) Dictionary size can be set as a constant instead of selecting it empirically; (2) The total variation based model is built, which can enhance less salient defects, improving segmentation performance significantly.

Flexible biosensor using inkjet printing of silver nanoparticles

Sensor Review ◽  
2014 ◽  
Vol 34 (4) ◽  
pp. 360-366 ◽  
Author(s):  
Zahra Abadi ◽  
Vahid Mottaghitalab ◽  
Mansour Bidoki ◽  
Ali Benvidi
Keyword(s):  
Silver Nanoparticles ◽  
Inkjet Printing ◽  
Design Methodology ◽  
Electrochemical Sensors ◽  
Flexible Substrate ◽  
Paper Substrate ◽  
Content Type ◽  
Textile Fabrics ◽  
Textile Fabric

Purpose – The purpose of this paper is to present a sophisticated methodology for inkjet printing of silver nanoparticles (AgNPs) in the range of 80-200 nm on different flexible substrate. AgNPs was chemically deposited by ejection of silver nitrate and ascorbic acid solutions onto different substrates such as paper and textile fabrics. The fabricated pattern was used to employ as electrode for electrochemical sensors. Design/methodology/approach – The morphology of deposited AgNPs was characterized by means of scanning electron microscopy. Moreover, conductivity and electrochemical behavior were identified, respectively, using four probe and cyclic voltammetry techniques. Acquired image shows a well-defined shape and size for the deposited AgNP. Findings – The conductivity of the paper substrate after printing process reached 5.54 × 105 S/m. This printed electrode shows a sharp electrochemical response for early determination of glucose. The proposed electrode provides a new alternative to develop electrochemical sensors using AgNPs chemically deposited on paper and textile fabric surfaces.

A simple method to construct a low-cost immunosensor based on a dithiol-functionalized polydopamine platform

2021 ◽  
Author(s):  
Bo Liu ◽  
Luanying Yang ◽  
Gang Wang ◽  
Sha He ◽  
Xiaobo Wang ◽  
...  
Keyword(s):  
Detection Limit ◽  
Linear Response ◽  
Low Cost ◽  
Covalent Immobilization ◽  
The Self ◽  
Simple Method ◽  
Low Detection Limit

A simple and low-cost electrochemical CEA immunosensor was investigated via the self-polymerization of dopamine and a dithiol compound spacer for the covalent immobilization of antibodies. The designed CEA immunosensor exhibited a linear response and a low detection limit.

scholarly journals A set of simple methods for detection and extraction of laminarinase

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ananthamurthy Koteshwara ◽  
Nancy V. Philip ◽  
Jesil Mathew Aranjani ◽  
Raghu Chandrashekhar Hariharapura ◽  
Subrahmanyam Volety Mallikarjuna
Keyword(s):  
Ammonium Sulfate ◽  
Ammonium Sulphate ◽  
Gold Standard ◽  
Direct Detection ◽  
Low Cost ◽  
Direct Analysis ◽  
Reducing Sugar ◽  
Ammonium Sulfate Precipitation ◽  
Simple Method ◽  
Functional Step

AbstractA carefully designed ammonium sulfate precipitation will simplify extraction of proteins and is considered to be a gold standard among various precipitation methods. Therefore, optimization of ammonium sulfate precipitation can be an important functional step in protein purification. The presence of high amounts of ammonium sulphate precludes direct detection of many enzymatically active proteins including reducing sugar assays (e.g. Nelson-Somogyi, Reissig and 3,5-dinitrosalicylic acid methods) for assessing carbohydrases (e.g. laminarinase (β (1–3)-glucanohydrolase), cellulases and chitinases). In this study, a simple method was developed using laminarin infused agarose plate for the direct analysis of the ammonium sulphate precipitates from Streptomyces rimosus AFM-1. The developed method is simple and convenient that can give accurate results even in presence of ammonium sulfate in the crude precipitates. Laminarin is a translucent substrate requiring the use of a stain to visualize the zones of hydrolysis in a plate assay. A very low-cost and locally available fluorescent optical fabric brightener Tinopal CBS-X has been used as a stain to detect the zones of hydrolysis. We also report simple methods to prepare colloidal chitin and cell free supernatant in this manuscript.

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