29 results on '"Chung-Yang Shih"'
Search Results
2. Separating Color and Identifying Repeat Pattern Through the Automatic Computerized Analysis System for Printed Fabrics.
- Author
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Chung-Feng Jeffrey Kuo, Chung-Yang Shih, and Jiunn-Yih Lee
- Published
- 2008
3. Image database of printed fabric with repeating dot patterns part (I) – image archiving
- Author
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Chung-Feng Jeffrey Kuo, Chung-Yang Shih, and Cheng-Lin Lee
- Subjects
Polymers and Plastics ,business.industry ,Color image ,Computer science ,Binary image ,Resolution (electron density) ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Centroid ,02 engineering and technology ,021001 nanoscience & nanotechnology ,Image (mathematics) ,Wavelet ,Transformation (function) ,Image database ,0202 electrical engineering, electronic engineering, information engineering ,Chemical Engineering (miscellaneous) ,020201 artificial intelligence & image processing ,Computer vision ,Artificial intelligence ,0210 nano-technology ,business - Abstract
An image database of printed fabrics with repeating dot patterns was created to alleviate issues associated with management of and searches for numerous dot printed fabrics in the printing industry. The function of the database is to archive and allow retrieval of images. First, we discuss image archiving of repeating pattern-based dot printed fabrics. The color image was scanned by resolution of 200 dpi. The wavelet transformation was used to preprocess the image to obtain a scanned image 1/16 of the size of the original to be the stored image. To acquire images with repeating pattern color and repeating pattern template, the binary image of each pattern was obtained using the Sobel edge detection method and a morphological operation. Then pattern elements identical to the target pattern element were screened out. Afterwards, the centroid positions of these identical pattern elements were used to subdivide the repeating pattern color image and repeating pattern template image using a vertical vector method. Finally, the RGB 512-color histogram was used as the color feature of the dot printed fabrics, and the geometric and moment invariant feature values of the repeating pattern template image were used as the pattern feature of the dot printed fabrics. Our experimental results show that images can be acquired that are suitable for use in a dot printed fabric image database. The color and template images of the repeating patterns, which represent the image content of the printed fabrics, were obtained to create an image database of repeating pattern-based dot printed fabrics. This image database contains data on 300 printed fabrics which can be used for subsequent research on database image retrieval.
- Published
- 2016
4. A study of automated color, shape and texture analysis of Tatami embroidery fabrics
- Author
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Chung-Yang Shih, Jung-Hsiang Cheng, and Chung-Feng Jeffrey Kuo
- Subjects
010407 polymers ,Scanner ,Engineering ,Polymers and Plastics ,business.industry ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Binary number ,Color analysis ,Wavelet transform ,02 engineering and technology ,021001 nanoscience & nanotechnology ,01 natural sciences ,0104 chemical sciences ,Median filter ,Chemical Engineering (miscellaneous) ,Preprocessor ,RGB color model ,Computer vision ,Artificial intelligence ,0210 nano-technology ,Cluster analysis ,business ,ComputingMethodologies_COMPUTERGRAPHICS - Abstract
This paper proposes an automated analysis system for Tatami embroidery fabric images that automates color analysis, pattern shape analysis and texture analysis. Firstly, the RGB (red, green and blue) image of embroidery fabric is obtained by a color scanner. In color analysis, the wavelet transforms and median filter are used for RGB image preprocessing, and then the Fuzzy C-Means (FCM) clustering method is used for the binary region splitting method, the color features of statistical values of colors and number of colors can be obtained. In the pattern shape analysis, the individual pattern components are segmented by the separated colors, and the shape features of pattern components are obtained by moment invariants. Finally, in the texture analysis of Tatami embroidery fabrics, the gray image of the weave block is segmented from the pattern component region, after the entropy filter, the parallel division lines are segmented by a threshold value, and then the angle and spacing between Tatami embroidery parallel division lines are obtained by Hough transform to represent texture features. The experimental results show that this method can implement Tatami embroidery fabric color, shape and texture analyses automatically.
- Published
- 2016
5. Image inspection of knitted fabric defects using wavelet packets
- Author
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Chung-Yang Shih, Chang-Chiun Huang, Yao-Ming Wen, and Chung-Feng Jeffrey Kuo
- Subjects
Engineering ,Polymers and Plastics ,Artificial neural network ,business.industry ,020207 software engineering ,02 engineering and technology ,Neural network classifier ,Wavelet packet decomposition ,0202 electrical engineering, electronic engineering, information engineering ,Chemical Engineering (miscellaneous) ,020201 artificial intelligence & image processing ,Computer vision ,Image Inspection ,Artificial intelligence ,business - Abstract
Image inspection by wavelet packets and a neural network classifier is presented for non-defect and six kinds of defects in knitted fabrics. The types of defect include a hole, set mark (coarse), dropped stitch, oil stain, streak, and tight end. In this study, wavelet packet decomposition of a sample image is carried out based on the best-basis wavelet packet tree with three resolution levels. The lowest-two entropy among all sub-band images and the standard deviation for the original image are selected as feature inputs of the neural network classifier. These textural features are shown in seven groups, which are separately distributed in the feature space. We gathered a total of 112 experimental samples, with 16 samples in each of the seven aforementioned categories. The results demonstrate that with the three features, 56 test samples are correctly inspected. However, the lack of one of the three features yields wrong classification of some samples. Therefore, the three features selected are definitely suitable for recognition of our knitted fabric defects and also are the smallest number of features required to give accurate inspection.
- Published
- 2015
6. A novel image processing technology for recognizing the weave of fabrics
- Author
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Chung-Yang Shih, Chang-Chiun Huang, Te-Li Su, I-Che Liao, and Chung-Feng Jeffrey Kuo
- Subjects
Engineering drawing ,Engineering ,Polymers and Plastics ,business.industry ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Image processing ,02 engineering and technology ,Yarn ,Backlight ,021001 nanoscience & nanotechnology ,visual_art ,Woven fabric ,Digital image processing ,0202 electrical engineering, electronic engineering, information engineering ,Median filter ,visual_art.visual_art_medium ,Chemical Engineering (miscellaneous) ,020201 artificial intelligence & image processing ,Computer vision ,Artificial intelligence ,Noise (video) ,0210 nano-technology ,business ,Histogram equalization - Abstract
The current analysis of fabric weave diagrams requires using fabric analyzing glass to record the weave number manually. This method damages eyesight and is also very time consuming. In addition, the unweaving mode damages the weave structure of woven fabric. This study uses a computer vision system and digital image processing technology for direct non-destructive analysis of the commonly used 12 fabric textures of woven fabrics without unweaving. Moreover, it proposes an automated woven fabric weave recognition method to enhance the practicability and fault tolerance of the recognition system. Firstly, the woven fabric image was shot by using a front light source and back light source, the noise of the woven fabric image was reduced by using a median filter and the contrast was increased by using histogram equalization. The statistical threshold value was used to segment the warp yarn area and the opening operation of morphology was used to disconnect the connected blocks and erode small noise. Horizontal projection and vertical projection were used to segment the warp yarn and weft yarn. The weave diagram was drawn to improve the computing time of the gray-level co-occurrence matrix. The contrast in the gray-level co-occurrence matrix was selected as the eigenvalue. In terms of woven fabric samples, 12 target samples were obtained, the Euclidean distance classifier was used and the 12 test samples were used for the experiment. The result showed a recognition rate of 100%. The recognition system was adopted by this study to effectively recognize the woven fabric weave.
- Published
- 2015
7. Optimization and practical verification of system configuration parameter design for a photovoltaic thermal system combined with a reflector
- Author
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Chung-Yang Shih, Chao-Yang Huang, Sheng-Siang Syu, Wei-Lun Lan, and Chung-Feng Jeffrey Kuo
- Subjects
Thermal efficiency ,Engineering ,business.industry ,020209 energy ,Photovoltaic system ,Reflector (antenna) ,02 engineering and technology ,TRNSYS ,Industrial and Manufacturing Engineering ,Taguchi methods ,Artificial Intelligence ,Control theory ,Thermal ,0202 electrical engineering, electronic engineering, information engineering ,business ,Electrical efficiency ,Software ,Thermal energy ,Simulation - Abstract
This study designed and optimized the system parameters for a photovoltaic thermal system (PV/thermal system) combined with reflectors. Moreover, it discussed the gain of electrical efficiency and thermal efficiency on the system after adding two reflectors on each of the south and north sides, and adjusting the water circulation system. As the rising angle and position of the sun varies each season, in order to make this study more rigorous, experiments were conducted in four seasons of a year. The Taguchi orthogonal array was used for experimental planning, and the optimal parameters were analyzed for electrical efficiency and thermal efficiency. The analysis of variance was conducted to examine the influential parameters, and principal component analysis was used to calculate the principal component point of each experiment. The results were employed to construct a response surface methodology model. Finally, the steepest descent method was applied to obtain the optimal parameters. The reflector theory was applied to calculate the gain of solar radiation amount after installing the reflector. Moreover, the gain was inputted into the simulation software TRNSYS to simulate the electrical power output and the water temperature in the water storage tank. The confirmatory experiments of the four seasons found that the electrical energy after installing the reflector increased by 0.117---0.183 kWh, and the thermal energy increased by 1.7---2.6 $$^{\circ }\hbox {C}$$źC. The experiment confirmed that the prediction error was $$
- Published
- 2015
8. Pattern-making simulation on embroidery using probabilistic neural network and texture fitting method
- Author
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Chung-Feng Jeffrey Kuo, Chien-Tung Max Hsu, and Chung-Yang Shih
- Subjects
Engineering ,Polymers and Plastics ,business.industry ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Weighted median ,Filter (signal processing) ,Texture (geology) ,Probabilistic neural network ,Planar ,Texture filtering ,Digital image processing ,Plotter ,Chemical Engineering (miscellaneous) ,Computer vision ,Artificial intelligence ,business ,ComputingMethodologies_COMPUTERGRAPHICS - Abstract
Embroidery fabric is different from other planar fabrics such as printed fabrics and twill fabrics. Because embroidery fabrics have inherent solid texture patterns, furry edges, voids and thickness shadows, it is very difficult to filter and simulate texture patterns and this is the bottleneck for embroidery automation. Therefore, this paper proposes the texture fitting method. The texture fitting method is a kind of nonfiltered digital image processing method. For embroidery fabrics full of multiple single-connected, single-color and single-texture closed regions, the texture fitting method can complete color and region separation, and texture simulation fast. Then the results can be output to monitors or plotters to investigate the simulation effect and it can be compared to real fabrics, or this technology can be used as a generalized filter for embroidery fabrics. This paper first addresses a combination of mean, morphological and central weighted median filters to remove light variation on embroidery surface, periodic darkness on the greige, and noised texture structures, so as to separate colors by weighted fuzzy C-means method and reshape one-dimensional image pixels to finish region separation. The second part of this paper utilizes the texture fitting method to identify stitch colors and simulate texture patterns over the whole image. By exporting the result to visual devices, we can prove the integral correctness and efficiency of the texture simulation.
- Published
- 2011
9. Automatic pattern recognition and color separation of embroidery fabrics
- Author
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Chung-Yang Shih, Chien-Tung Max Hsu, and Chung-Feng Jeffrey Kuo
- Subjects
Discrete wavelet transform ,Engineering ,Polymers and Plastics ,business.industry ,Pattern recognition (psychology) ,Genetic algorithm ,Separation (aeronautics) ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Chemical Engineering (miscellaneous) ,Pattern recognition ,Computer vision ,Artificial intelligence ,business - Abstract
Currently, there is very little literature on automatic image recognition and classification of embroidery fabrics. In today’s embroidery industry, front-end pattern-making still relies greatly on labor, using pattern-making software to carefully depict patterns and images in different colors and regions. Hence, an image analysis system that can recognize colors, regions and patterns automatically is a critical technique of improving the competitiveness of the embroidery industry. In this paper, the mean filtering method, central-weighted median filtering method and morphological operation were employed to filter out the light variation on the embroidery fabric surface structure, and a genetic algorithm was applied to distinguish images of repeat pattern embroidery from that of nonrepeat pattern embroidery. If it is a repeat pattern, then a much smaller sized subimage would be searched in the original image for the same color components and spatial structure, which could lower the computing load of the entire image greatly and is expected to achieve the processing speed required in an online real-time system. As for nonrepeat pattern embroidery images, discrete wavelet transform was applied to acquire low-frequency subimages, which can retain important image features while improving the computing efficiency. Be it a repeat or nonrepeat pattern, after obtaining subimages, specific criteria were used to determine the exact number of clusters, and the weighted fuzzy C-means method was employed to run color separation and region separation. The experiment proved that, in regard to the color embroidery images of repeat and nonrepeat patterns, the method proposed in this paper succeeded in color and region separations with good result.
- Published
- 2011
10. Printed fabric computerized automatic color separating system
- Author
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Chung-Yang Shih and Chung-Feng Jeffrey Kuo
- Subjects
Scanner ,Engineering ,Polymers and Plastics ,Color image ,Fiber (mathematics) ,business.industry ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Yarn ,Separation system ,Computer graphics (images) ,visual_art ,visual_art.visual_art_medium ,Chemical Engineering (miscellaneous) ,RGB color model ,Color filter array ,Computer vision ,Artificial intelligence ,business ,ComputingMethodologies_COMPUTERGRAPHICS - Abstract
This paper presents an innovative Printed Fabric Computerized Automatic Color Separation System. First, a scanner is used to obtain the digitized color image of printed fabric in RGB (red, green, blue) mode, and the image is then converted to the digitized color image in HSI (hue, saturation, intensity) mode. Next, the genetic algorithms are applied to search the smaller sub-image having the same color distribution as the original fabric for proceeding with the subsequent color separation algorithm. For the color separation algorithm, the FCM-based (Fuzzy C-Means) Region Splitting Method is used to carry out the color segmentation of the printed fabric image. The experimental result shows that the color separation method proposed by this study can quickly and correctly achieve the automatic color separation of the printed fabric.
- Published
- 2010
11. Application of computer vision in the automatic identification and classification of woven fabric weave patterns
- Author
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Kai-Ching Peng, Chung-Feng Jeffrey Kuo, Cheng-En Ho, and Chung-Yang Shih
- Subjects
Engineering ,Polymers and Plastics ,Pixel ,business.industry ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Process (computing) ,Stability (learning theory) ,Texture (music) ,Fuzzy logic ,Digital image ,Identification (information) ,Woven fabric ,Chemical Engineering (miscellaneous) ,Computer vision ,Artificial intelligence ,business - Abstract
Traditionally, fabric texture identification is based on visual inspection. Recent studies have proposed automatic recognition, which utilizes computer vision to recognize the texture of different fabrics. In the recognition process, the fabric weave patterns are identified by the warp and weft floats. However, due to the optical environments and the appearance differences of fabrics and yarns, the stability and fault-tolerance of the computer vision method are yet to be improved. By using the fabric weave patterns image identification system, this study analyzed the fabric image to find out the warp and weft by the pixel gray-level cumulative values. It then cut out the image of the warp and weft floats to obtain the texture feature values, and used the Fuzzy C-Means (FCM) algorithm to identify the warp and weft floats. The identification results can derive the black-white digital image and the digital matrix of the fabric weave patterns. Finally, weaves classification is conducted based on the successfully trained two-stage Back-Propagation Neural Network. This two-stage neural network can be used to construct the computer vision system to recognize fabric texture, and to increase the system reliability and accuracy. This study used the first-order and second-order co-occurrence matrix, and confirmed that fabric patterns can be identified and classified accurately with this method.
- Published
- 2010
12. Computerized Color Distinguishing System for Color Printed Fabric by Using the Approach of Probabilistic Neural Network
- Author
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Yi-Jen Huang, Chung-Yang Shih, Chung-Feng Jeffrey Kuo, and Te-Li Su
- Subjects
Color histogram ,Materials science ,Polymers and Plastics ,business.industry ,General Chemical Engineering ,Materials Science (miscellaneous) ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Color balance ,Pattern recognition ,Color space ,Web colors ,RGB color space ,Color depth ,High color ,Materials Chemistry ,RGB color model ,Artificial intelligence ,business ,ComputingMethodologies_COMPUTERGRAPHICS - Abstract
This article proposes an innovative color printed fabric computer color distinguishing system whose main functions are to precisely distinguish the printed fabric pattern colors and match colors to improve the current time-consuming color distinguishing conducted by manpower. The RGB color mode is an industrial color standard, by which the change and overlapping of color channels of red, green, and blue represent types of colors. RGB stands for red, green, and blue, respectively. It is one of the most widely used color system and covers almost all of the colors sensible to human vision. Hence, this paper adopts the RGB color mode to present color printed fabric images. First, to reduce the color distinguishing computation, a genetic algorithm was applied in search of small images of the same color in the original color printed fabric. Then, color distinguishing computation was conducted by a probabilistic neural network (PNN), which has the advantage of a very fast learning speed. Finally, PANTONE® standa...
- Published
- 2008
13. Repeat Pattern Segmentation of Printed Fabrics by Hough Transform Method
- Author
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Chung-Yang Shih, Chung-Feng Jeffrey Kuo, and Jiunn-Yih Lee
- Subjects
010302 applied physics ,Repeat pattern ,Engineering ,Scanner ,Polymers and Plastics ,business.industry ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,02 engineering and technology ,Full color ,021001 nanoscience & nanotechnology ,01 natural sciences ,Fuzzy logic ,Hough transform ,law.invention ,Image (mathematics) ,law ,0103 physical sciences ,Chemical Engineering (miscellaneous) ,Segmentation ,Computer vision ,Artificial intelligence ,0210 nano-technology ,business ,Cluster analysis - Abstract
A novel approach of repeat pattern segmentation is proposed for printed fabrics. Printed fabrics are captured by a color scanner and converted into full color digital files. To classify the color segmentation and pattern elements a fuzzy C-means (FCM) clustering algorithm and a specific cluster-validity criterion are used to obtain the pattern image of printed fabric. Then, the repeat pattern segmentation of the pattern image is established by Hough transform. The experimental results have shown that this systematic method is very suitable for the analysis of the repeat pattern of printed fabrics.
- Published
- 2005
14. Color and Pattern Analysis of Printed Fabric by an Unsupervised Clustering Method
- Author
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Jiunn-Yih Lee, Chih-Yuan Kao, Chung-Yang Shih, and Chung-Feng Jeffrey Kuo
- Subjects
010302 applied physics ,Color histogram ,Engineering ,Polymers and Plastics ,business.industry ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Pattern analysis ,Pattern recognition ,02 engineering and technology ,021001 nanoscience & nanotechnology ,01 natural sciences ,Fuzzy logic ,ComputingMethodologies_PATTERNRECOGNITION ,0103 physical sciences ,Chemical Engineering (miscellaneous) ,Computer vision ,Artificial intelligence ,0210 nano-technology ,Unsupervised clustering ,business ,Cluster analysis ,Analysis method - Abstract
In this paper, a novel approach to color and pattern analysis is proposed for printed fabric. An unsupervised analysis method is developed using a fuzzy C-means (FCM) clustering algorithm and a specific cluster-validity criterion (SC criterion). First, the printed fabric is captured by a color scanner and converted into full color digital files, then the mean filter is used to smooth the color of the image. The search for good cluster numbers is made by the SC criterion, and the corresponding color clusters are obtained based on the FCM clustering algorithm. Finally, the color and pattern features of the printed fabric are calculated. The experimental results show that this approach is very suitable for analyzing the colors and patterns of printed fabrics.
- Published
- 2005
15. Automatic Recognition of Fabric Weave Patterns by a Fuzzy C-Means Clustering Method
- Author
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Jiunn-Yih Lee, Chung-Yang Shih, and Kuo
- Subjects
010302 applied physics ,Engineering ,Scanner ,Polymers and Plastics ,biology ,business.industry ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Morpho ,02 engineering and technology ,Decision rule ,Texture (music) ,021001 nanoscience & nanotechnology ,biology.organism_classification ,01 natural sciences ,Fuzzy logic ,0103 physical sciences ,Pattern recognition (psychology) ,Chemical Engineering (miscellaneous) ,Computer vision ,Artificial intelligence ,0210 nano-technology ,business ,Cluster analysis ,Recognition algorithm - Abstract
A new robust recognition algorithm is proposed for fabric weave pattern recognition. The gray-level images of solid woven fabrics are captured by a color scanner and converted into digital files, then enhanced images are obtained by a gray-level morpho logical operation. Based on the interstices of yarns, warp and weft crossed areas are located, and four texture features of these areas are obtained by first-order and second- order statistics. Unsupervised decision rules for recognizing warp and weft floats are developed using a fuzzy c-means clustering method. The experimental materials include plain, twill, and satin woven fabrics. Experimental results demonstrate that three basic weave patterns can be clearly identified.
- Published
- 2004
16. Adsorption of color dyestuffs on polyurethane-chitosan blends
- Author
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Chung-Yang Shih, Kuo-Shien Huang, and Chien-Wen Chen
- Subjects
Materials science ,Polymers and Plastics ,Polymer adsorbent ,Contact time ,technology, industry, and agriculture ,General Chemistry ,Surfaces, Coatings and Films ,Chitosan ,chemistry.chemical_compound ,Adsorption ,chemistry ,PEG ratio ,Materials Chemistry ,Organic chemistry ,Ethylene glycol ,Nuclear chemistry ,Polyurethane - Abstract
This study made use of poly(ethylene glycol) (PEG) samples of different molecular weights, which were reacted with a diisocyanate ester, and an anion center for the synthesis of polyurethane (PU), which was then mixed with chitosan to form a polymer adsorbent. It was tested for the determination of its adsorption toward acidic dyestuffs under various conditions. Our results showed that under all the tested conditions, the blended polymer adsorbent possessed a better adsorbing ability than chitosan by itself, and the degree of adsorption varied positively as the adsorbent concentration, ambient temperature, and contact time increased. Furthermore, the addition of PU remarkably increased the adsorption efficiency, whereas PEG with a greater molecular weight yielded a better adsorption performance. As for the dyestuffs, the red one surpassed the others in adsorption efficiency. Finally, a 5 mg/mL concentration of the adsorbent solution, a temperature of 45°C, and a contact time of 15 min gave fairly good adsorption results. © 2004 Wiley Periodicals, Inc. J Appl Polym Sci 91: 3991–3998, 2004
- Published
- 2004
17. Synthesis of a polyurethane-chitosan blended polymer and a compound process for shrink-proof and antimicrobial woolen fabrics
- Author
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Chung-Yang Shih and Kuo-Shien Huang
- Subjects
chemistry.chemical_classification ,Materials science ,Polymers and Plastics ,technology, industry, and agriculture ,General Chemistry ,Polymer ,Surfaces, Coatings and Films ,Chitosan ,chemistry.chemical_compound ,chemistry ,Chemical engineering ,parasitic diseases ,Materials Chemistry ,Polymer blend ,Composite material ,Ethylene glycol ,Ionomer ,Natural fiber ,Shrinkage ,Polyurethane - Abstract
The purpose of this study was to develop a finishing process to improve the shrinkage and anti microbial properties of woolen fabrics. First, polyurethane (PU) prepolymers were synthesized from poly(ethylene glycol) (PEG) of different molecular weights. Next, the PU prepolymers were mixed with chitosan to form blended polymers. Then, these blended polymers were used to treat woolen fabric in a compound process to determine if they could modify the fabric, making it more resistant to shrinkage and bacteria. Our experimental results indicate an improvement in both the shrink-proof and antimicrobial properties of the fabric with an increase in the temperature or duration of the heat treatment, as well as with an increase in the concentration of the processing agent. However, the yellowing and softness tendency of the fabric shifted towards the opposite, unfavorable direction. The treatment also seems to somewhat improve the strength of the fabric. Furthermore, our results show that the addition of chitosan remarkably increased the shrink-proof and antimicrobial properties of the treated fabric. Finally, the blended polymer made of PEG with a molecular weight of 600 and chitosan gave the best results of the polymer combinations tested. © 2003 Wiley Periodicals, Inc. J Appl Polym Sci 88: 2356–2363, 2003
- Published
- 2003
18. The study of rapid curing crease-resistant processing on cotton fabrics. II. Effect of poly(ethylene glycol) on physical properties of processed fabrics
- Author
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Kuo-Shien Huang and Chung-Yang Shih
- Subjects
Poly ethylene glycol ,Materials science ,Polymers and Plastics ,Moisture ,technology, industry, and agriculture ,Concentration effect ,General Chemistry ,Surfaces, Coatings and Films ,chemistry.chemical_compound ,chemistry ,PEG ratio ,Ultimate tensile strength ,Materials Chemistry ,medicine ,Swelling ,medicine.symptom ,Composite material ,Ethylene glycol ,Curing (chemistry) - Abstract
This study aimed to examine the effects of the addition of poly(ethylene glycol) (PEG) on the physical properties of processed cotton fabrics in a rapid heat-curing crease-resistant process. Our results show that this addition influences the moisture absorbency, crease resistance in both dry and wet conditions, and tensile strength preservation rate of the processed fabrics. Moreover, with such addition, the use of higher temperature in the process would enhance the moisture absorbency and dry–wet crease resistance but reduce the tensile strength preservation rate. The optimum condition for processing cotton fabric is to use PEG with a molecular weight of 1000 at a concentration of 10%, heated at 200°C for 30 s. © 2002 Wiley Periodicals, Inc. J Appl Polym Sci 85: 1008–1012, 2002
- Published
- 2002
19. Kinetic studies of crease-resistant finishing process for cotton fabrics with DMEU/MMEU prepolymer mixture
- Author
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Kuo-Shien Huang and Chung-Yang Shih
- Subjects
Materials science ,Polymers and Plastics ,Kinetics ,technology, industry, and agriculture ,chemistry.chemical_element ,General Chemistry ,Activation energy ,Nitrogen ,Surfaces, Coatings and Films ,chemistry.chemical_compound ,Reaction rate constant ,chemistry ,Polymer chemistry ,Materials Chemistry ,Paraformaldehyde ,Prepolymer ,Natural fiber ,Curing (chemistry) ,Nuclear chemistry - Abstract
The kinetics of the crease-resistant finishing process for cotton fabrics with DMEU/MMEU prepolymer mixture are studied. The DMEU/MMEU prepolymer resin is made from ethylene urea (EU) and paraformaldehyde (PF) with different mole ratios. The results show that the nitrogen content in the treated fabrics and the reaction rate constant increases with curing temperature and PF mole ratio. The treated fabrics with more PF in the source material have smaller Ea, Δ H*ast;, and ΔS*. The ΔG* was independent of the mole ratio in the source material, but it increases with curing temperature. © 2002 Wiley Periodicals, Inc. J Appl Polym Sci 85: 509–513, 2002
- Published
- 2002
20. Silicone resin synthesized by tetraethoxysilane and chlorotrimethylsilane through hydrolysis-condensation reaction
- Author
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Chao-Yang Huang, Chung-Feng Jeffrey Kuo, Jiong-Bo Chen, and Chung-Yang Shih
- Subjects
inorganic chemicals ,chemistry.chemical_classification ,Materials science ,Polymers and Plastics ,Polydimethylsiloxane ,technology, industry, and agriculture ,General Chemistry ,equipment and supplies ,complex mixtures ,Surfaces, Coatings and Films ,stomatognathic diseases ,Silanol ,chemistry.chemical_compound ,Silicone ,chemistry ,Silicone resin ,Polymer chemistry ,Materials Chemistry ,Copolymer ,Thermal stability ,Adhesive ,Solubility - Abstract
Silicone pressure-sensitive adhesives compositions contain a polydimethylsiloxane and a silicone resin, which can enhance the instant bonding ability and bonding strength of the adhesive. In this study, silicone resin was designed to have a low molecular weight and a highly nonpolar chemical structure. The silicone resin was applied to silicone pressure-sensitive adhesives. The molecular structure of silicone resin was characterized by FT-IR, GPC, 1 H-NMR, and 29 Si-NMR spectroscopic techniques. Properties such as thermal stability, solubility, hydrophobic, and transparent properties were researched and compared. When the chlorotrimethylsilane increased, it appeared that the amount of silanol groups, molecular weight and thermal stability decreased, while the hydrophobic and transparent properties increased. The silicone resin was completely soluble in toluene and xylene. It was also applied to silicone pressure-sensitive adhesives, resulting in good peel adhesion. V C 2013 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2014, 131, 40317.
- Published
- 2013
21. Preparation and characterization of electrospun polycaprolactone/polyethylene oxide membranes
- Author
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Chih-Yuan Yang, Chung-Yang Shih, Yu-Hsun Nien, Chien-Ju Lu, and Quan-Xun Ye
- Subjects
Materials science ,Polymers and Plastics ,Enthalpy of fusion ,Organic Chemistry ,Composite number ,technology, industry, and agriculture ,macromolecular substances ,Polyethylene oxide ,equipment and supplies ,musculoskeletal system ,Electrospun membranes ,Electrospinning ,chemistry.chemical_compound ,Crystallinity ,Membrane ,Chemical engineering ,chemistry ,Polycaprolactone ,Materials Chemistry ,Composite material - Abstract
In this study, we prepared electrospun polycaprolactone/polyethylene oxide (PCL/PEO) membranes fabricated by electrospinning. We designed the membranes for the application of medical patch. In order to understand the drug-released behavior when the membranes were used in medical patch, ketoprofen was used as a model drug. The results show that ketoprofen-loaded electrospun membranes that comprised PCL/PEO released all ketoprofen in 30 min. The mebranes had potential application in medical patch. In additions, the viscosity of the PCL/PEO solution increased with the proportion of PEO. The low conductivities of the all PCL/PEO solutions were caused by the lack of electric charge on both PCL and PEO molecules. A higher PEO percentage was associated with high enthalpy of fusion of the electrospun PCL/PEO membrane. The crystallinity of PEO in the PCL/PEO composite decreased as the PEO content declined.
- Published
- 2013
22. Printed fabric computerized automatic color separating system.
- Author
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Chung-Feng Jeffrey Kuo and Chung-Yang Shih
- Subjects
TEXTILES ,GENETIC algorithms ,YARN ,TEXTILE fibers ,FUZZY systems ,COLOR separation (Printing) - Abstract
This paper presents an innovative Printed Fabric Computerized Automatic Color Separation System. First, a scanner is used to obtain the digitized color image of printed fabric in RGB (red, green, blue) mode, and the image is then converted to the digitized color image in HSI (hue, saturation, intensity) mode. Next, the genetic algorithms are applied to search the smaller sub-image having the same color distribution as the original fabric for proceeding with the subsequent color separation algorithm. For the color separation algorithm, the FCM-based (Fuzzy C-Means) Region Splitting Method is used to carry out the color segmentation of the printed fabric image. The experimental result shows that the color separation method proposed by this study can quickly and correctly achieve the automatic color separation of the printed fabric. [ABSTRACT FROM PUBLISHER]
- Published
- 2011
- Full Text
- View/download PDF
23. Application of computer vision in the automatic identification and classification of woven fabric weave patterns.
- Author
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Kuo, Chung-Feng Jeffrey, Chung-Yang Shih, Cheng-En Ho, and Kai-Ching Peng
- Subjects
COMPUTER vision ,AUTOMATIC identification ,WEAVING patterns ,PATTERN recognition systems ,ARTIFICIAL neural networks - Abstract
Traditionally, fabric texture identification is based on visual inspection. Recent studies have proposed automatic recognition, which utilizes computer vision to recognize the texture of different fabrics. In the recognition process, the fabric weave patterns are identified by the warp and weft floats. However, due to the optical environments and the appearance differences of fabrics and yarns, the stability and fault-tolerance of the computer vision method are yet to be improved. By using the fabric weave patterns image identification system, this study analyzed the fabric image to find out the warp and weft by the pixel gray-level cumulative values. It then cut out the image of the warp and weft floats to obtain the texture feature values, and used the Fuzzy C-Means (FCM) algorithm to identify the warp and weft floats. The identification results can derive the black-white digital image and the digital matrix of the fabric weave patterns. Finally, weaves classification is conducted based on the successfully trained two-stage Back-Propagation Neural Network. This two-stage neural network can be used to construct the computer vision system to recognize fabric texture, and to increase the system reliability and accuracy. This study used the first-order and second-order co-occurrence matrix, and confirmed that fabric patterns can be identified and classified accurately with this method. [ABSTRACT FROM PUBLISHER]
- Published
- 2010
- Full Text
- View/download PDF
24. Separating Color and Identifying Repeat Pattern Through the Automatic Computerized Analysis System for Printed Fabrics.
- Author
-
Kuo, Chung-Feng Jeffrey, Chung-Yang Shih, and Jiunn-Yih Lee
- Subjects
TEXTILES ,COLOR separation (Printing) ,COLORS ,GENETIC algorithms ,REPETITIVE patterns (Decorative arts) - Abstract
This study proposes a novel analysis system for printed fabrics that can automatically make color separation and identify repeat patterns. The system uses a scanner to obtain red, green and blue (RGB) color images of printed fabrics and then convert them into hue, saturation, intensity (HSI) color images. In order to obtain color separation, a genetic algorithm is used to search for a smaller sub-image with the same color distribution, and then the color separation is conducted by use of the recursive region splitting method. Then carry out another Fuzzy C-means (FCM) calculation on the HSI image using the color clusters (cluster number) and values (cluster centers) obtained from separating the colors of sub-images to quickly classify colors for the pixels. Pixels of different color categories are marked with different gray levels. In this way, a polychromatic pattern image is formed. For identifying repeat patterns, first, a template matching method is applied to discover distributions of same pattern elements. Then, the Hough transform method is used to obtain the cutting positions and dimensions of the repeat patterns in the polychromatic pattern image. Next, the images of the repeat patterns are extracted out from the polychromatic images. Finally, the repeat units of the black pictures are generated based on the color categories and they are expanded to become black pictures that can be used to make plates. According to the experimental results, this system can rapidly and automatically separate colors and identify repeat patterns of images on printed fabrics. [ABSTRACT FROM AUTHOR]
- Published
- 2008
25. Computerized Color Distinguishing System for Color Printed Fabric by Using the Approach of Probabilistic Neural Network.
- Author
-
Kuo, Chung-Feng Jeffrey, Yi-Jen Huang, Te-Li Su, and Chung-Yang Shih
- Subjects
COLOR computer graphics ,TEXTILES ,GENETIC algorithms ,COLOR in textile crafts ,ARTIFICIAL neural networks - Abstract
This article proposes an innovative color printed fabric computer color distinguishing system whose main functions are to precisely distinguish the printed fabric pattern colors and match colors to improve the current time-consuming color distinguishing conducted by manpower. The RGB color mode is an industrial color standard, by which the change and overlapping of color channels of red, green, and blue represent types of colors. RGB stands for red, green, and blue, respectively. It is one of the most widely used color system and covers almost all of the colors sensible to human vision. Hence, this paper adopts the RGB color mode to present color printed fabric images. First, to reduce the color distinguishing computation, a genetic algorithm was applied in search of small images of the same color in the original color printed fabric. Then, color distinguishing computation was conducted by a probabilistic neural network (PNN), which has the advantage of a very fast learning speed. Finally, PANTONE® standard color tickets were applied in matching colors. The experimental results revealed that the PNN design can easily realize and achieve accurate, fast color classification. It is proved that this color distinguishing system can be practically applied in printed fabric color distinguishing and matching. [ABSTRACT FROM AUTHOR]
- Published
- 2008
- Full Text
- View/download PDF
26. Color and Pattern Analysis of Printed Fabric by an Unsupervised Clustering Method.
- Author
-
Kuo, Chung-Feng Jeffrey, Chung-Yang Shih, Chi-Yang Shih, Chih-Yuan Kao, and Jiunn-Yih Lee
- Subjects
TEXTILE printing ,COLOR in the textile industries ,TEXTILE design ,TEXTILE industry - Abstract
In this paper, a novel approach to color and pattern analysis is proposed for printed fabric. An unsupervised analysis method is developed using a fuzzy C-means (FCM) clustering algorithm and a specific cluster-validity criterion (SC criterion). First, the printed fabric is captured by a color scanner and converted into full color digital files, then the mean filter is used to smooth the color of the image. The search for good cluster numbers is made by the SC criterion, and the corresponding color clusters are obtained based on the FCM clustering algorithm. Finally, the color and pattern features of the printed fabric are calculated. The experimental results show that this approach is very suitable for analyzing the colors and patterns of printed fabrics. [ABSTRACT FROM AUTHOR]
- Published
- 2005
- Full Text
- View/download PDF
27. Adsorption of color dyestuffs on polyurethanechitosan blends.
- Author
-
Chung-Yang Shih, Chien-Wen Chen, and Kuo-Shien Huang
- Published
- 2004
28. Automatic Recognition of Fabric Weave Patterns by a Fuzzy C-Means Clustering Method.
- Author
-
Kuo, Chung-Feng Jeffrey, Chung-Yang Shih, and Jiunn-Yih Lee, Chung-Feng Jeffrey
- Subjects
ALGORITHMS ,WEAVING patterns ,DRY goods ,NONPARAMETRIC statistics ,WOOL ,TEXTILES - Abstract
A new robust recognition algorithm is proposed for fabric weave pattern recognition. The gray-level images of solid woven fabrics are captured by a color scanner and converted into digital files, then enhanced images are obtained by a gray-level morphological operation. Based on the interstices of yarns, warp and weft crossed areas are located, and four texture features of these areas are obtained by first-order and second-order statistics. Unsupervised decision rules for recognizing warp and weft floats are developed using a fuzzy c-means clustering method. The experimental materials include plain, twill, and satin woven fabrics. Experimental results demonstrate that three basic weave patterns can be clearly identified. [ABSTRACT FROM AUTHOR]
- Published
- 2004
- Full Text
- View/download PDF
29. Synthesis of a polyurethanechitosan blended polymer and a compound process for shrink-proof and antimicrobial woolen fabrics.
- Author
-
Chung-Yang Shih and Kuo-Shien Huang
- Published
- 2003
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