Are yarn quality prediction tools useful in the breeding of high yielding and better fibre quality cotton(Gossypium hirsutum L.)?

Results The population had large variations for lint yield,fibre properties,predicted yarn properties,and composite fibre quality values.Lint yield with all fibre quality traits was not correlated.When the selection was conducted first to keep those with improved fibre quality,and followed for high yields,a large proportion in the resultant populations was the same between selections based on Cottonspec predicted yarn quality and HVI-measured fibre properties.They both exceeded the selection based on FQI and Background The approach of directly testing yarn quality to define fibre quality breeding objectives and progress the selection is attractive but difficult when considering the need for time and labour.The question remains whether yarn prediction tools from textile research can serve as an alternative.In this study,using a dataset from three seasons of field testing recombinant inbred line population,Cottonspec,a software developed by the Commonwealth Scientific and Industrial Research Organisation(CSIRO)for predicting ring spun yarn quality from fibre properties measured by High Volume Instrument(HVI),was used to select improved fibre quality and lint yield in the population.The population was derived from an advanced generation inter-crossing of four CSIRO conventional commercial varieties.The Cottonspec program was able to provide an integrated index of the fibre qualities affecting yarn properties.That was compared with selection based on HVI-measured fibre properties,and two composite fibre quality variables,namely,fibre quality index(FQI),and premium and discount(PD)points.The latter represents the net points of fibre length,strength,and micronaire based on the Premiums and Discounts Schedule used in the market while modified by the inclusion of elongation.PD points.Conclusions The population contained elite segregants with improved yield and fibre properties,and Cottonspec predicted yarn quality is useful to effectively capture these elites.There is a need to further develop yarn quality prediction tools through collaborative efforts with textile mills,to draw better connectedness between fibre and yarn quality.This connection will support the entire cotton value chain research and evolution.

Yarn Quality Prediction for Small Samples Based on AdaBoost Algorithm

In order to solve the problems of weak prediction stability and generalization ability of a neural network algorithm model in the yarn quality prediction research for small samples,a prediction model based on an AdaBoost algorithm(AdaBoost model) was established.A prediction model based on a linear regression algorithm(LR model) and a prediction model based on a multi-layer perceptron neural network algorithm(MLP model) were established for comparison.The prediction experiments of the yarn evenness and the yarn strength were implemented.Determination coefficients and prediction errors were used to evaluate the prediction accuracy of these models,and the K-fold cross validation was used to evaluate the generalization ability of these models.In the prediction experiments,the determination coefficient of the yarn evenness prediction result of the AdaBoost model is 76% and 87% higher than that of the LR model and the MLP model,respectively.The determination coefficient of the yarn strength prediction result of the AdaBoost model is slightly higher than that of the other two models.Considering that the yarn evenness dataset has a weaker linear relationship with the cotton dataset than that of the yarn strength dataset in this paper,the AdaBoost model has the best adaptability for the nonlinear dataset among the three models.In addition,the AdaBoost model shows generally better results in the cross-validation experiments and the series of prediction experiments at eight different training set sample sizes.It is proved that the AdaBoost model not only has good prediction accuracy but also has good prediction stability and generalization ability for small samples.

Yarn Quality Prediction and Diagnosis Based on Rough Set and Knowledge-Based Artificial Neural Network

In the spinning process, some key process parameters( i. e.,raw material index inputs) have very strong relationship with the quality of finished products. The abnormal changes of these process parameters could result in various categories of faulty products. In this paper, a hybrid learning-based model was developed for on-line intelligent monitoring and diagnosis of the spinning process. In the proposed model, a knowledge-based artificial neural network( KBANN) was developed for monitoring the spinning process and recognizing faulty quality categories of yarn. In addition,a rough set( RS)-based rule extraction approach named RSRule was developed to discover the causal relationship between textile parameters and yarn quality. These extracted rules were applied in diagnosis of the spinning process, provided guidelines on improving yarn quality,and were used to construct KBANN. Experiments show that the proposed model significantly improve the learning efficiency, and its prediction precision is improved by about 5. 4% compared with the BP neural network model.

Evaluation of Yarn Quality and Optimization of Cotton Spinning Parameter Based on Factors Analysis

In this paper, the spinning parameters are optimized by using the method of factor analysis. The yarns obtained from four different spinning parameters are evaluated by this method. Two common factors, fineness unevenness and tenacity level, are extracted from the seven yarn-quality indexes. The accumulative contribution percentage of the two factors is up to 91.813%,and much information in the yarn-quality indexes is reflected by the two factors. Then the score of each factor is calculated to evaluate the quality of yarn. Based on that, the techniques are optimized. The result is well in line with spinning practices, so it is testified feasibly to use this method to optimize spinning parameter.

Yarn Properties Prediction Based on Machine Learning Method

Although many works have been done to construct prediction models on yarn processing quality,the relation between spinning variables and yarn properties has not been established conclusively so far.Support vector machines(SVMs),based on statistical learning theory,are gaining applications in the areas of machine learning and pattern recognition because of the high accuracy and good generalization capability.This study briefly introduces the SVM regression algorithms,and presents the SVM based system architecture for predicting yarn properties.Model selection which amounts to search in hyper-parameter space is performed for study of suitable parameters with grid-research method.Experimental results have been compared with those of artificial neural network(ANN)models.The investigation indicates that in the small data sets and real-life production,SVM models are capable of remaining the stability of predictive accuracy,and more suitable for noisy and dynamic spinning process.

Predicting GM(1,N) Model for the Coefficient of Variation of Hectometer Yarn's Weight

The Coefficient of Variation(CV)of hectometer yarn's weight is one of the guidelines to evaluate its intrinsic quality.In the spinning manufacturing,the control of cotton yarn's weight unevenness is accomplished mainly in terms of a spot-check on semi-product and a succedent adjust in process parameters during spinning based on technicians' experience.However,it is theoretically believed among manufacturers that with fixed technical levels and parameters in the spinning process,the quality parameters of assorted cotton have a certain influence on the CV.In order to find out a rule of the influence that assorted cotton has on the CV,a GM(1,N)model,correlated raw cotton's quality parameter with the CV,has firstly been developed according to the modeling theory of grey system,and then been applied in the designing step to predict the CV.It has been approved by practical modeling and validation that the model could fit preferably an accrual CV value,and provide a method of quantitative predicting analysis for textile manufacturers to design cotton yarn's quality.

VARIATION OF WOVEN FABRIC GEOMETRY——Non-uniform Flattening of Yarns

In previous research much effort has been devoted to the geometry of woven fabrics and relat-ed problems under the assumption of constant yarn configuration in fabric.This paper will first re-port that image crimp (yarn crimp measured by an image analysis method) seems larger than actualvalue.From the explanation of this result,the variation of yarn configuration in woven fabric dueto the non-uniform flattening is revealed.The significance of this actual structure of woven fabricsis discussed.It is believed that the variation of yarn configuration is very important for fabric per-formance,and may be an advantage for fabric quality.

A Worsted Yarn Virtual Production System Based on BP Neural Network

Back-Propagation (BP) neural network and its modified algorithm are introduced. Two series of BP neural network models have been established to predict yarn properties and to deduce wool fiber qualities. The results from these two series of models have been compared with the measured values respectively, proving that the accuracy in both the prediction model and the deduction model is high. The experimental results and the corresponding analysis show that the BP neural network is an efficient technique for the quality prediction and has wide prospect in the application of worsted yarn production system.

THE PRACTICE OF ACRYLIC SIROSPUN YARN ON THE SEMI WORSTED SPINNING FRAME

The technique of sirospun process is applied on a modified semi-worsted balloonless spinningframe to investigate the effect of spindle-speed,yarn-twist and strand-spacing on yarn properties.Yarn breaking strength,breaking extension,evenness and imperfection are examined on the basisof CCD experimental design.Yarn hairiness is particularly concerned,being found that all spinningparameters tested have significant effects on hairiness and that the minimum number of hairs oc-curs at the strand-spacing of 14.4 mm.Compared to conventional single spun yarn,experimentshave revealed the greatest advantage of using sirospun process is that all sirospun yarns have muchless hairiness.A new sirospun yarn fault,so called“loop”,has also been examined.The most likely cause forthis yarn fault is the strand-tension unbalance between the two strands when low tension spinningis applied.Further analysis and some initial tests have been carried out in the hope of overcomingthis loop fault which is an important obstacle to the application of balloonless spinning.

THE PRACTICE OF ROTOR SPINNING IN PRODUCING SILK NOIL YARN AND THE DEVELOPMENT IN WOVEN FABRICS AND KNITTED GOODS

At present, we have succeeded in producing silk noil yarn by rotor spinning, and obtained good economic benefits. In this paper, through combining with the recent producing practice, systematically discussing and analysing the technology in the process of producing silk noil yarn by rotor spinning, we develop the new technology which is suited to produce silk noil yarn by rotor spinning, and address several noticable problems. The conclusions have been put into practice and proved to be effective and reliable. It can be consulted by the textile mills.

Influence of the Mode of Discrete Drum Speed and the Number of Inputs on the Technological Parameters of the Yarn Produced

This article is devoted to the research work aimed at improving the quality of yarns obtained by pneumomechanical spinning. The yarn quality indicators obtained at different speed modes of the pneumomechanical spinning machine discrete drum were studied and analyzed. The effect of the number of incisions of the sawtooth coatings on the discrete drum on the quality indicators of the yarn produced was also studied. The results of the experiments were analyzed by graphical and histogram methods, and alternative options were suggested.

纯亚麻短麻色纺纱的生产及成纱性能

针对当前亚麻短麻色纺纱少且短麻利用率不高等问题,采用梳棉试样机对化学脱胶后的亚麻短麻纤维进行2次梳理开松,再经染色处理、给油加湿焖包处理,最后在棉纺设备上进行试纺。通过优化纺纱各工序的工艺参数配置,成功纺制出41.7 tex纯亚麻短麻色纺纱,测试了其成纱质量指标,并与41.7 tex亚麻湿纺染色纱、41.7 tex纯亚麻纱线染色纱进行性能对比。结果表明:在棉纺设备上所纺制的41.7 tex纯亚麻短麻色纺纱的主要成纱指标达到了T/CNTAC60—2020《干纺环锭纺纯亚麻本色纱》优等纱质量标准;其断裂强度、强力CV值、断裂伸长率等均介于同线密度的纯亚麻湿纺染色纱和纯亚麻纱线染色纱之间,可部分替代纯亚麻湿纺染色纱用于后续织造工艺。

饲粮中添加胱氨酸对安哥拉兔产毛性能的影响

本试验旨在考察饲粮中添加不同水平胱氨酸对安哥拉兔产毛性能的影响。选取已剪二刀毛安哥拉兔180只,随机分为5组,每组36个重复,每个重复1只。对照组饲喂玉米-豆粕型基础饲粮,试验组饲喂在基础饲粮中分别添加0.1%、0.2%、0.3%和0.4%胱氨酸的试验饲粮。预试期3 d,正试期73 d。结果显示:与对照组相比,饲粮中添加0.1%、0.2%和0.3%的胱氨酸显著提高了安哥拉兔的平均日采食量(P<0.05),添加0.1%和0.2%的胱氨酸显著降低了安哥拉兔的料毛比(P<0.05);饲粮中添加0.4%的胱氨酸显著提高了血清中谷胱甘肽过氧化物酶(GSH-Px)活性(P<0.05);饲粮中添加0.1%和0.3%的胱氨酸显著提高了安哥拉兔背部皮下次级毛囊密度(P<0.05),添加0.1%、0.2%和0.4%的胱氨酸显著提高了安哥拉兔毛的断裂强力(P<0.05),添加0.4%的胱氨酸显著提高了安哥拉兔毛的纤维细度(P<0.05);此外,饲粮中添加0.2%和0.3%的胱氨酸显著上调了安哥拉兔背部皮肤中角蛋白关联蛋白6.1(KAP 6.1)的表达(P<0.05)。综上所述,饲粮中添加胱氨酸可改善安哥拉兔毛品质,促进毛囊发育;本试验条件下,安哥拉兔饲粮中胱氨酸的适宜添加水平为0.1%。

蛋白质及氨基酸组成对毛皮动物毛皮质量影响的研究进展

毛囊是一个再生器官,其生长过程不断经历生长期、衰退期和休止期。毛囊周期性的循环生长决定毛皮动物被毛的生长与脱落。毛囊不仅受遗传、环境等因素调控,还与饲粮中营养物质的含量和种类有关,尤其与蛋白质含量及氨基酸组成密切相关。蛋白质是毛皮动物被毛组成的重要物质。含硫氨基酸是限制被毛生长的主要氨基酸。硫元素以二硫键形式存在于被毛中,对维持被毛纤维蛋白分子网状空间结构、抵御外界各种不利因素的侵蚀具有重要作用。此外,赖氨酸、色氨酸、酪氨酸、谷氨酸等其他非含硫氨基酸也参与毛皮动物皮张的形成,在控制毛皮动物皮肤被毛颜色、促进被毛生长和调节毛皮成熟中发挥重要作用。文章综述了近年来饲粮中蛋白质含量及氨基酸组成对毛皮动物毛皮质量影响的研究进展,以期为指导毛皮动物遗传选育和生产提供参考。

中日友好医院毛发专病门诊实践与探索

公立医院建立毛发专病门诊既是顺应市场需求,也是学科高质量发展的重要方向。中日友好医院于2015年开设毛发专病门诊,于2017年成立全国首家毛发医学中心。目前毛发专病门诊已成为医院特色专病门诊,毛发专病门诊建设管理和治疗优势突出,得到了广大毛发疾病患者与同行的认可。笔者介绍了中日友好医院毛发专病门诊建设方面的实践探索,以期为公立医院高质量发展毛发专病门诊提供参考。

基于负离子纤维的喷毛带子纱产品开发

以涤纶负离子中长纤为原料,经过和毛、梳理、并条、粗纱工序后制成粗纱条,在花色喷毛机上喂入到由涤纶长丝编织的带子纱中,通过改变花色喷毛机的工艺参数制成三种不同细度的负离子喷毛带子纱,织成样片后进行负离子发生量的测试和评价。结果表明,三种规格的喷毛带子纱负离子发生量皆大于1 000个/cm^(3),负离子发生量较高,能达到维持人体基本健康需求、增强免疫力的效果。纺纱试验表明,当粗纱喂入罗拉的速度控制在20 r/min~25 r/min,纱线细度控制在3 NM~3.3 NM时,所纺纱线的负离子发生量维持在最高水平。产品利用喷毛带子纱蓬松的纱线结构,使负离子纤维能更好地释放出负离子,发挥保健功效。

宠物犬猫毛发质量的评定方法及营养调控技术研究进展

宠物犬猫的毛发质量是宠物主人尤为关注的问题,而在影响犬猫毛发质量的各种因素中,营养因素备受重视。目前主要的营养调控手段是通过调节宠物犬猫食品中的营养成分(矿物质、维生素、脂肪酸、氨基酸)比例或额外饲喂营养保健品来改善犬猫的毛发质量。科学合理的毛发质量评定方法可以更清晰地展现犬猫毛发质量的变化,有助于进一步判断营养调控是否有效改善了犬猫毛发质量。而由于现阶段对犬猫毛发质量评定方法的研究不够系统和深入,难以建立统一的标准,一定程度上限制了营养调控犬猫毛发质量的研究。本文综述近年来研究中所使用的宠物犬猫毛发质量评定的方法及营养调控技术的研究进展,以期为犬猫的美毛护毛饲粮配制和调控犬猫毛发质量的研究提供参考。

采绒季节对驼绒纤维品质性状的影响

为了探究季节性因素对驼绒纤维品质性状的影响,对其在春季脱毛时期和秋季新毛长成时期2个重要节点进行实验,对204峰来自同一牧场、相同饲养条件的周岁棕色母驼进行2次采样,获得春季和秋季共408袋驼绒样品,测试纤维的长度、直径和强伸性能,分析和比对春秋两季驼绒样品的频率分布情况、表面微观形貌和品质性状。结果发现,驼绒纤维的春季平均纤维长度极显著高于秋季(P<0.001),春季平均纤维直径极显著高于秋季(P<0.001),春季直径变异系数显著高于秋季(P<0.05);驼绒纤维春季断裂强度极显著低于秋季(P<0.001),春季断裂强力极显著低于秋季(P<0.001),春季断裂伸长极显著高于秋季(P<0.001),春季断裂伸长率极显著高于秋季(P<0.001),春季初始模量极显著低于秋季(P<0.001)。

麻纤维高效短流程纺纱机理研究

探讨麻纤维的高效短流程纺纱问题。对比了棉、毛、麻纺等纺纱系统的流程和效率。基于理论分析和实际纺纱结果,分析了纤维长度对成纱条干和强伸性的影响。通过纺纱实例,对比了不同长度麻纤维纺纱后的成纱质量,提出了用棉型纺纱系统对麻切断纤维进行纺纱。认为:过长的麻纤维会导致加工流程变长和生产效率变低;纤维达到一定长度后,纤维长度增加对成纱质量的影响已不显著;适当短的纤维有利于简化纺纱流程和改善成纱质量;将麻纤维长度控制在棉纤维长度范围内,完全可在棉纺设备上进行麻纤维纺纱,从而利用棉纺设备来改造和提升麻纺的装备和技术水平。

基于粒子群优化支持向量机的纱线质量预测

针对复杂纺纱过程中成纱质量预测精度不足以及深度学习对庞大数据集依赖性的缺陷,提出一种基于粒子群算法优化支持向量机的小样本成纱质量预测方法。首先,对原始数据集样本序列进行灰色关联预处理,按照关联度大小进行排序,再结合先验知识库筛选出主要的原棉纤维指标;其次,针对小样本预测问题,建立了线性核、多项式核、高斯核以及自适应带宽RBF核等不同核函数支持向量回归(SVR)预测模型;最后,采用粒子群优化(PSO)算法对高斯核SVR模型的超参数(正则化系数和带宽调节参数)进行辨识,设计一种综合适应度函数与线性递减惯性权重策略,用以提高PSO算法的寻优能力。仿真结果表明:PSO优化高斯核SVR模型对不同成纱质量指标有较好的预测效果,其平均相对误差不超过2%。认为:PSO优化高斯核SVR模型对成纱质量指标的预测误差较低,具有良好的适应性。