Evaluation of fabric pilling using hybrid imaging methods

A study has been made on the quantification and evaluation of fabric pilling using two-dimensional and three-dimensional hybrid imaging methods. Two-dimensional imaging method was good for some samples while three-dimensional measurement method for others, according to the properties of their base fabric. Various image processing techniques as well as three-dimensional data processing algorithms were applied for the extraction of pills from measured data and a series of shape parameters have been defined for the objective evaluation of fabric pilling. An evaluation criterion that is compatible with the conventional evaluation method has been proposed by applying the new evaluation method to the current photographic standards.     

Properties of cotton fabric after irradiation with infrared CO2 laser

Thermal effect of interaction between laser beam and fabrics presents a risk of material damage, because strong laser beam energy is applied to a very small area of fabric. At present infrared laser beam is widely applied to decolouration of denim fabrics, cutting of textiles. There were investigated the morphology, color change and mechanical properties of irradiated samples of cotton fabric. Chemical damage of cotton fibers was quantified by copper number and behaviour of dissolved polymer. Results was discussed in connection with practical applications.     

Application of the real fabric frictional speeds to the fabric sound analysis using water repellent fabrics

This study aims to determine fabric frictional speeds between the arm and the trunk when people walk (1.3 m/s), jog (2.5 m/s) and run (4.5 m/s), and to apply the measured speeds to setting a sound generator for each motion to obtain fabric rustling sounds. By analyzing body motions captured by the Falcon motion analysis system and a camcorder, it was identified that the friction between the arm and trunk occurred within 10° of shoulder angle along the center line of the trunk in the sagittal plane and the maximum frictional speed occurred at the elbow within the shoulder friction range. The averages (SDs) of maximum frictional speed at the elbow were found 0.63 m/s (0.17) at walking, 1.1 m/s (0.25) at jogging, and 1.98 m/s (0.35) at running. The frictional sounds of three coated nylon fabrics were obtained using these predetermined speeds. We calculated sound characteristics such as the sound pressure levels (SPL) and Zwicker’s psychoacoustic parameter using 1/3 octave band analysis. The SPL values ranged from 74.2 dB at running to 79.0 dB at jogging, which was about the same noise level as in the busy street. The values of loudness (Z) at walking and jogging were higher than that at running, but the fluctuation strength (Z) increased in the order of walking, jogging, and running.     

Evaluation of anti-stabbing performance of fabric layers woven with various hybrid yarns under different fabric conditions

Various types of special fibers are used for human body protection, mostly in the form of fiber-reinforced composites. These composites are made of special fibers and matrix resin; however, they are often not comfortable for the wearer due to the lack of flexibility and air permeability. This study focuses on an evaluation of human body protective performance against stabbing for various special fibers such as aramid, basalt, and steel fibers, being utilized in cotton hybrid forms. These hybrid forms are designed to improve wearer comfort, while maintaining adequate anti-stab resistance. Specimens prepared with various fabric densities are tested in terms of anti-stabbing performance, according to the NIJ standard. In addition, we investigate the influence of factors such as fiber type, the number of fabric layers, fabric weight, and fabric density on anti-stabbing performance. Results show that the penetration depth of the impactor, which punctures and protrudes through the specimens, decreases with the number of layers, the thickness, and the mass of the armor sample; however, these factors have different relationships according to the material type. Consequently an objective evaluation of anti-stabbing performance is needed. We suggest an anti-stabbing index that can be applied as a criterion to evaluate the antistabbing performance of various specimens woven with special fibers under different fabric conditions. Using the new index, anti-stabbing performances of various specimens can be compared and raw material and fabric conditions that offer the most efficient anti-stabbing performance can be selected.     

Estimation of evaporation from arid-irrigated region using MODIS satellite imagery

For sustainable development of irrigated agriculture in arid regions, improvement of water use efficiency is essentially required to maintain current production levels and meet food and fiber for population growth in future. To achieve high water use efficiency, a key consideration is to reduce unnecessary soil water loss due to evaporation. In this article, regional daily evaporation over Hetao Irrigation District in a typical arid region during the irrigation period of 2009 was determined by a developed maximum surface temperature model combining Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery. The results showed that maximum land surface temperature (LST) from MODIS satellite imagery was relatively higher in the western and middle parts than that of the eastern part of the district. At the same time, the mean minimum LST was shown somewhat higher in the eastern part. Mean daily evaporation was relatively higher in the eastern part, which showed water consuming is higher in the eastern part of the district. During the irrigation period of 2009, the total income water (irrigation water and rainfall) amount is 590.3 mm, and the outcome water (drainage discharge and evaporation) amount is 497.5 mm. The surplus of 92.8 mm in the irrigated season is considered to be consumed in winter season. Throughout the irrigated season, income and outcome almost equals each other. The daily evaporation distribution map could specify particular water consuming areas over the district where high daily evaporation may be occurred.     

Description of batter mixing using near infrared spectroscopy

The aim of the present study was to describe the physicochemical events occurring during batter mixing at different water contents (51.8, 54.4, and 56.7 g of water/100 g of dough) using near infrared (NIR) spectroscopy. An FT-NIR spectrometer over the 1000–2500 nm range with a fibre optic probe was used to record NIR spectra in-line. The analysis of both one-dimensional statistical method (principal components analysis) and two-dimensional statistical methods (generalised two-dimensional correlation spectroscopy) was conducted to evaluate the possibilities of NIR spectroscopy to monitor physical and physicochemical modifications observed during mixing of batter. The NIR results were in agreement with the physical and physicochemical analysis traditionally used to study bread dough mixing (consistency and glutenin depolymerisation). PCA on raw NIR spectra demonstrated that PC₁ describes the same traces as the dough consistency curves. PCA on raw NIR spectra can be used to monitor the batter mixing and to identify the NIR mixing time close to the t_peak.PCA on spectra after second derivative demonstrated that PC₁ and PC₂ traces described different traces compared to the dough consistency curves. The loading spectra associated to PC₁ and PC₂ suggested that almost the same physicochemical and chemical mechanisms occur during the dough mixing at 51.8 or 54.4% water contents, but with kinetic and intensity differences. The 2D COS method allowed a sequence of chemical events occurring during mixing for the batters at 51.8 and 54.4% water contents to be tentatively proposed. The 2D COS did not give clear physicochemical differences between the three batters during mixing. The NIR results for the highly hydrated batter (56.7%) were difficult to analyse due to its high water content.     

Description of batter mixing using near infrared spectroscopy

The aim of the present study was to describe the physicochemical events occurring during batter mixing at different water contents (51.8, 54.4, and 56.7 g of water/100 g of dough) using near infrared (NIR) spectroscopy. An FT-NIR spectrometer over the 1000–2500 nm range with a fibre optic probe was used to record NIR spectra in-line. The analysis of both one-dimensional statistical method (principal components analysis) and two-dimensional statistical methods (generalised two-dimensional correlation spectroscopy) was conducted to evaluate the possibilities of NIR spectroscopy to monitor physical and physicochemical modifications observed during mixing of batter. The NIR results were in agreement with the physical and physicochemical analysis traditionally used to study bread dough mixing (consistency and glutenin depolymerisation). PCA on raw NIR spectra demonstrated that PC₁ describes the same traces as the dough consistency curves. PCA on raw NIR spectra can be used to monitor the batter mixing and to identify the NIR mixing time close to the t_peak.PCA on spectra after second derivative demonstrated that PC₁ and PC₂ traces described different traces compared to the dough consistency curves. The loading spectra associated to PC₁ and PC₂ suggested that almost the same physicochemical and chemical mechanisms occur during the dough mixing at 51.8 or 54.4% water contents, but with kinetic and intensity differences. The 2D COS method allowed a sequence of chemical events occurring during mixing for the batters at 51.8 and 54.4% water contents to be tentatively proposed. The 2D COS did not give clear physicochemical differences between the three batters during mixing. The NIR results for the highly hydrated batter (56.7%) were difficult to analyse due to its high water content.     

Analysis of woven fabric structure using image analysis and artificial intelligence

An integrated hardware and software system has been developed that can automate the analysis process of various woven fabric structures. Although the analysis of woven structure is one of the most important steps in the fabric design and quality control process, it has been dependent only on human skills with primitive devices. In this study, a dedicated hardware system has been developed to obtain an ultra-high resolution fabric images. Then a series of image analysis technique was applied to locate the intersecting regions of warps and wefts on those images. Finally, an artificial neural network was formed to determine the woven structure of fabric based on the two shape parameters extracted from those regions.     

Dimensional stabilization of cotton plain weft knitted fabric using mercerization treatment

Investigation on dimensional stability of cotton plain weft knitted fabric manufactured from rotor spun yarn, subjected to mercerization treatment has been represented. Several fabric samples were mercerized considering variation in time of treatment, bath temperature, concentration of alkali solution and also mercerizing tension. Values of constant of course (K _c ), constant of wale (K _w ), the area geometry constant (K _s ) which are indicative of fabric dimensional stability were calculated after treatment for mercerized samples. Then, these values were compared with those of un-treated samples subjected to dry and wet relaxation and also were compared with each other. Based on the effect of each variable itself and their simultaneous effect, it was concluded that, mercerization treatment and considered parameters had a distinctive influence on dimensional stability of the fabric. Mercerized samples had better dimensional stability in comparison with un-treated ones. A comprehensive experimental analysis showed that, there is meaningful difference between K _s values of the samples mercerized at various conditions. Also, the area geometry constant (K _s ) achieved after treatment was higher than that of other relaxation methods.     

Automatic color separating system for printed fabric using the self-organizing map network approach

We can only use color numbers, color values and design to describe the color pattern of printed fabrics, which is different from woven fabrics with yarn disposition and texture as pattern determinants. Since most printed fabrics contain many different patterns nowadays, we need more than words and simple methods to describe the color patterns. The complication in pattern identification has made the analysis and comparison difficult and will have to be conducted manually. The automatic computer color separating system for printed fabrics proposed in this paper uses unsupervised learning network to automatically separate printed colors. The system first uses color scanner to pick the image of the printed fabrics and stores it as digital image. Then, it uses wavelet transformation to minify the fabric image to reduce the calculation load of color separation and also reserve the printing structure and color distribution of the original image. It also uses LAB color model to acquire characteristic value of the colors and the Self-Organizing Map Network (SOMN) to conduct color separation. According to our experimental results, this system can rapidly and automatically complete color separation and identify repeating patterns for printed fabrics’ images.     

Measurement of fuzz fibers on fabric surface using image analysis methods

Fuzz on the fabrics, which is the fibers protruded from the fabric surface, is very important in view of appearance quality, since it causes unpleasant appearance on the fabrics and also leads to pilling which makes fabric appearance and softness worse. However, fuzz on fabric surface is measured mostly by subjective methods (human vision) rather than objective methods. Thus, in this study, objective method using image analysis techniques has been developed for the measurement of fuzz on fabric surface. Fuzz on the fabric has also been ranked and rated by experts in order to see the reliability of the results obtained from the fuzz measurement. It was observed that correlation coefficient (r) between rating value and objective measurement value was 0.9 and this correlation coefficient value confirmed the reliability of this method.     

Durable press finish of cotton fabric using malic acid as a crosslinker

It has been considered that malic acid,α-hydroxy succinic acid, could not form crosslinks in the cellulosic materials unless activated by other polycarboxylic acids such as butanetetracarboxylic acid or citric acid because there are only two carboxylic acids per molecule available for the formation of one anhydride intermediate. However we found that the dicarboxylic malic acid with sodium hypophosphite catalyst without the addition of other crosslinkers was able to improve wrinkle resistance of cotton up to 294° (dry WRA) and 285° (wet WRA), which is a measure of crosslinking level in cotton.¹H FT-NMR, FT-IR and GPC analysis indicated the in-situ formation of an trimericα, β-malic acid with a composition of 1:3 through the esterification between hydroxyl group and one of carboxylic groups in malic acid during curing. The crosslinking of cotton was attributed to the trimericα, β-malic acid, a tetracarboxylic acid, which can form two anhydride rings during curing. The influence of crosslinking conditions such as concentrations of malic acid and catalyst, pH of the formulation bath, and curing temperature were investigated in terms of imparted wrinkle resistance and whiteness. The addition of reactive polyurethane resin in the formulation slightly increased the mechanical strength retention of crosslinked fabric coupled with additional increase in wrinkle resistance.     

基于远红外成像技术的花生苗期抗旱性鉴定

远红外热成像;花生;苗期;干旱;生长;光合特性;耐旱系数     

Objective measurement of water repellency of fabric using image analysis (I)

A methodology for the objective evaluation of water repellency is studied using image analysis of the sprayed pattern on woven fabrics according to a standard spray test (AATCC Test Method 22-2001). The wet area ratio obtained from the spray standard test ranking is found to be exponentially related with its water repellency rating. Mean filtering is used to remove the effect of weave texture and the transmitted light through interyarn spaces. The ring frame of the instrument and wet region are recognized using Otsu thresholding technique. And Hough transform and outline operation are used to obtain the size and position of the ring frame. The objective assessment of the water repellency using image processing can reduce unnecessary confusion in the subjective determination of the water repellency.     

Preparation of the cotton fabric with ultraviolet resistance and antibacterial activity using nano attapulgite colloidal particles

A carefully designed surface modification technique for the preparation of multifunctional cotton fabric was successfully developed by the functionalization of cotton fabric with nano attapulgite (ATP) colloidal particles. The dispersion of the nano ATP colloidal particles, the morphology, microstructure, thermal stability, ultraviolet resistance, antibacterial activity and air permeability of the treated cotton fabric were characterized. The results showed that the particle size of the ATP particle distributed between 100 nm to 150 nm after dispersion. The SEM (scanning electron microscopy) and FTIR (Fourier transform infrared spectra) analysis demonstrated that the ATP particles were successfully introduced to surface of the cotton fabric. The structural and thermal stability of the treated fabric were higher than those of the untreated fabric. The ATP treated cotton fabric possessed excellent ultraviolet resistance and antibacterial activity. Furthermore, the treatment did not affect the wear ability of the cotton fabric. The multifunctional cotton fabric meets the market demand for natural products.     

Decadal changes in the rice-cropping system in the Ayeyarwady Delta using a large archive of satellite imagery from 1981 to 2020

In the last 40 years, the rice-cropping system has considerably changed in the Ayeyarwady Delta. The large archive of satellite imagery provides a history of how land and water resource managements have changed in the face of growing populations, resource demand, and climate change. This study aimed to assess the decadal changes in the rice-cropping system in the Ayeyarwady Delta by using the large archive of satellite imagery for the last 40 years (1981???2020). The long-term NDVI dataset provided various information on rice cultivation. Signal processing techniques were used to detect on the historical changes in the rice-cropping system, and the impact of climate change was assessed by using trend analysis. Until the 1980s, single-cropping of summer rice was dominant in the Delta. To enhance the grain yield of rice, the irrigation facilities were introduced in 1992 under an initiative of the Myanmar government. As a result, the annual cropping intensities increased from 1.087?±?0.390 in the 1980s to 1.422?±?0.499 in the 2010s. The information on historical change in the rice-cropping system would be useful to consider the practical and cost-effective utilization of remaining land and water resources. Moreover, the trend analysis of NDVI time-series showed negative trends in coastal areas. This indicates that the rice production in coastal areas has been constrained by the saline intrusion. The salt-affected areas are expected to expand under future climate change scenarios. Government support is highly required for sustainable rice production in the Delta.

     

Visible-near infrared spectroscopy based citrus greening detection: Evaluation of spectral feature extraction techniques

Citrus greening is a serious disease affecting citrus production in Florida and different parts of the world. This disease is spread by an insect vector and the trees are killed several years after infection. There is no known treatment for the disease. Disease detection and removal of infected trees is a critical part of citrus greening disease management efforts. This paper reports the evaluation of spectral features extracted from visible-near infrared spectroradiometer spectra for their potential to detect citrus greening disease. The extraction of spectral features is an effort to lower the cost of the optical sensor while maintaining their performance. Spectral features: (i) spectral reflectance bands and (ii) vegetation indices (VIs) were derived from 350-2,500 nm spectral reflectance data using two feature extraction methods: stepwise discriminant analysis and stepwise regression analysis. Following the selection of spectral features, the features were assessed using two classifiers, quadratic discriminant analysis (QDA) and soft independent modeling of classification analogies (SIMCA) to determine the overall and individual class classification accuracies. The classification results indicated that both the spectral features (spectral bands and VIs) yielded good overall (higher than 80%) and healthy class (higher than 85%) classification accuracies using the QDA-based algorithm. The SIMCA-based algorithm yielded good average citrus greening class classification accuracy (higher than 83%) using selected spectral features. Thus, the present study demonstrates the applicability of utilizing spectral features for detection of greening in citrus.     

Prediction of the changes on the CIELab values of fabric after chemical finishing using artificial neural network and linear regression models

Changes on the CIELab values of the dyed materials after the different chemical finishing treatments using artificial neural network (ANN) and linear regression (LR) models have been predicted. The whole structural properties of fabrics and some process data which were from fiber to the finishing parameters were accepted as inputs in these models. The networks having different structures were established, and it was also focus on the parameters which could affect the performance of the established networks. It was determined that we could successfully predict the color differences values occurring on the material after the finishing applications. In addition, we realized that some ANN parameters affected the prediction performance while establishing the models. After training ANN models, the prediction of the color difference values was also tried by linear regression models. Then, extra ANN models were established for all outputs using the parameters as inputs in the LR equations, and the prediction performances of both established models were compared. According to the results, the neural network model gives a more accurate prediction performance than the LR models.     

Prediction of maize seed attributes using a rapid single kernel near infrared instrument

Non-destructive measurements of seed attributes would significantly enhance breeder selection of seeds with specific traits, and could potentially improve hybrid development. A single kernel near infrared reflectance (NIR) instrument was developed for rapidly predicting maize grain attributes, which would enable plant breeders to quickly select promising individual seeds. With the overall goal being to develop spectrometric calibrations, absorbance spectra from 904 to 1685 nm were collected from 87 maize samples, with 30 kernels of each sample (2610 kernels total), representing a wide variability in the essential amino acids tryptophan and lysine, crude protein, oil and soluble sugar contents. Average sample spectra were matched to bulk reference values. Partial least squares regression (PLSR) calibration models with cross-validation were developed for both relative (% dry matter) and absolute (mg kernel⁻¹) constituent contents. Similarly, models using bagging PLSR were developed. The best model obtained was for relative crude protein content, with an R²p of 0.75 and a SEP of 0.47%. Kernel mass was also highly predictable (R²p=0.76, SEP=0.03 g). Tryptophan, lysine and oil were less predictable, but showed good potential for segregating individual seeds using NIR. Soluble sugar contents produced poor model statistics. Bagging PLSR yielded models with similar levels of prediction.