Detection of Residual Yarn in Bobbin Based on Odd Partial Gabor Filter and Multi-Color Space Hierarchical Clustering

In an automatic bobbin management system that simultaneously detects bobbin color and residual yarn,a composite texture segmentation and recognition operation based on an odd partial Gabor filter and multi-color space hierarchical clustering are proposed.Firstly,the parameter-optimized odd partial Gabor filter is used to distinguish bobbin and yarn texture,to explore Garbor parameters for yarn bobbins,and to accurately discriminate frequency characteristics of yarns and texture.Secondly,multi-color clustering segmentation using color spaces such as red,green,blue(RGB)and CIELUV(LUV)solves the problems of over-segmentation and segmentation errors,which are caused by the difficulty of accurately representing the complex and variable color information of yarns in a single-color space and the low contrast between the target and background.Finally,the segmented bobbin is combined with the odd partial Gabor’s edge recognition operator to further distinguish bobbin texture from yarn texture and locate the position and size of the residual yarn.Experimental results show that the method is robust in identifying complex texture,damaged and dyed bobbins,and multi-color yarns.Residual yarn identification can distinguish texture features and residual yarns well and it can be transferred to the detection and differentiation of complex texture,which is significantly better than traditional methods.

应用程序属性感知的Yarn资源调度模型研究

Hadoop应用程序存在计算密集属性和调度时间属性,但是Hadoop大数据平台集成的第二代资源管理器Yarn内置的三种资源调度器无法将相同属性的应用程序均衡分配到计算节点上,导致部分节点负载过高,出现严重的计算任务长尾效应。文中提出了一种应用程序属性感知的Yarn负载均衡调度模型——APB Scheduler。APB Scheduler自动感知应用程序属性,将相同属性应用程序的Container按照动态资源计划均衡分配到集群计算节点上,并使用NSGA-Ⅲ算法完成最优分配方案计算。实验结果表明,APB Scheduler解决了相同属性应用程序的Container分配倾斜问题,大幅提升了集群的性能和稳定性。

Dual-Ion Co-Regulation System Enabling High-Performance Electrochemical Artificial Yarn Muscles with Energy-Free Catch States

Artificial yarn muscles show great potential in applications requiring low-energy consumption while maintaining high performance. However, conventional designs have been limited by weak ion-yarn muscle interactions and inefficient “rocking-chair” ion migration. To address these limitations, we present an electrochemical artificial yarn muscle design driven by a dual-ion co-regulation system. By utilizing two reaction channels, this system shortens ion migration pathways, leading to faster and more efficient actuation. During the charging/discharging process, PF_6~- ions react with carbon nanotube yarn, while Li~+ ions react with an Al foil. The intercalation reaction between PF_6~- and collapsed carbon nanotubes allows the yarn muscle to achieve an energy-free high-tension catch state. The dual-ion coordinated yarn muscles exhibit superior contractile stroke, maximum contractile rate, and maximum power densities, exceeding those of “rocking-chair” type ion migration yarn muscles. The dual-ion co-regulation system enhances the ion migration rate during actuation, resulting in improved performance. Moreover, the yarn muscles can withstand high levels of isometric stress, displaying a stress of 61 times that of skeletal muscles and 8 times that of “rocking-chair” type yarn muscles at higher frequencies. This technology holds significant potential for various applications, including prosthetics and robotics.

Scalable Core–Sheath Yarn for Boosting Solar Interfacial Desalination Through Engineering Controllable Water Supply

Tailoring water supply to achieve confined heating has proven to be an effective strategy for boosting solar interfacial evaporation rates.However,because of salt clogging during desalination,a critical point of constriction occurs when controlling the water rate for confined heating.In this study,we demonstrate a facile and scalable weaving technique for fabricating core-sheath photothermal yarns that facilitate controlled water supply for stable and efficient interracial solar desalination.The core-sheath yarn comprises modal fibers as the core and carbon fibers as the sheaths.Because of the core-sheath design,remarkable liquid pumping can be enabled in the carbon fiber bundle of the dispersed superhydrophilic modal fibers.Our woven fabrics absorb a high proportion(92%)of the electromagnetic radiation in the solar spectrum because of the weaving structure and the carbon fiber sheath.Under one-sun(1 kW·m^(-2))illumination,our woven fabric device can achieve the highest evaporation rate(of 2.12kg·m^(-2)·h^(-1) with energy conversion efficiency:93.7%)by regulating the number of core-sheath yarns.Practical application tests demonstrate that our device can maintain high and stable desalination performance in a 5 wt%NaCl solution.

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.

The Influence of Elimination of Roving Frame on Ring Spun Cotton Yarn Characteristics

Generally, ring spun yarns are manufactured from roving which is produced by roving frame. In this paper, an experiment has been done producing ring spun cotton yarn directly from finisher drawn sliver eliminating the roving frame. Total 3 types of yarn with the various linear density of 8 Ne, 10 Ne & 12 Ne were produced using a roving frame and without using a roving frame. In the next step, physical and mechanical properties of those yarns including unevenness, imperfections, hairiness & tenacity were investigated. The result showed that ring spun cotton yarns produced from sliver exhibited inferior physical and mechanical properties compared with samples from the conventional ring spinning system.

Study on the Presence of Filament Yarn in Jamdani Saree in Bangladesh

Jamdani weaving is one of the oldest heredities of Bangladesh. From the beginning 100% cotton yarn was used to produce high quality jamdani saree. The weavers were the finest with weaving skills. Higher yarn count yarns were used to weave the jamdani saree. In course of time at present manmade fibres are also used to produce jamdani saree. The use of filament yarn may have eased the manufacturing difficulties, but the jamdani saree is missing its originality without 100% cotton. In this project, random jamdani saree sample was collected to identify the fibre composition. Samples of filament were also collected from the manufacturer and tested. It was evident that instead of cotton yarn in warp and weft silk and polyester filament yarn were used.

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.

Performance Improvement through Novel Adaptive Node and Container Aware Scheduler with Resource Availability Control in Hadoop YARN

The default scheduler of Apache Hadoop demonstrates operational inefficiencies when connecting external sources and processing transformation jobs.This paper has proposed a novel scheduler for enhancement of the performance of the Hadoop Yet Another Resource Negotiator(YARN)scheduler,called the Adaptive Node and Container Aware Scheduler(ANACRAC),that aligns cluster resources to the demands of the applications in the real world.The approach performs to leverage the user-provided configurations as a unique design to apportion nodes,or containers within the nodes,to application thresholds.Additionally,it provides the flexibility to the applications for selecting and choosing which node’s resources they want to manage and adds limits to prevent threshold breaches by adding additional jobs as needed.Node or container awareness can be utilized individually or in combination to increase efficiency.On top of this,the resource availability within the node and containers can also be investigated.This paper also focuses on the elasticity of the containers and self-adaptiveness depending on the job type.The results proved that 15%–20%performance improvement was achieved compared with the node and container awareness feature of the ANACRAC.It has been validated that this ANACRAC scheduler demonstrates a 70%–90%performance improvement compared with the default Fair scheduler.Experimental results also demonstrated the success of the enhancement and a performance improvement in the range of 60%to 200%when applications were connected with external interfaces and high workloads.

层彩涡流色纺纱的开发与实践

针对牛仔纱的流行趋势及目前染色存在的不足,模拟牛仔色纱特征,开发一种层彩涡流色纺纱。融合了传统环锭纺和喷气涡流包芯纺技术,芯纱采用短纤维纱线代替长丝,以达到芯纱色彩可调、粗细可调、捻向可调;纺纱过程中通过芯纱与外包层纤维捻向相反组合技术,选择相应的捻系数,抵消纤维的加捻应力;利用喷气涡流纺包缠不均匀的特点,可呈现独特的露芯效果,内外层可采用不同颜色及配比。纺制的层彩涡流色纺纱可作为针织、机织用纱,丰富的色彩变化使层彩涡流色纺纱可用于生产无染牛仔织物。

生物基功能环保纱线的开发与应用现状

为深入探究生物基功能环保纱线的工艺措施,通过查阅大量有关文献进行归纳总结,得出不同类别生物基纤维的预处理、加工过程工艺参数设定、纺纱环境及工艺原则等。指出:对于强力比较低的生物基纤维需要进行自由开松,以避免损伤纤维;对于回潮率较高的生物基纤维,需控制好车间温湿度,使纤维最好处于放湿状态,以防止纤维缠绕胶辊;对于可纺性能比较差的生物基纤维,为不影响成纱强度,其占比不超过30%;对于易产生静电的生物基纤维,为提高其蓬松性,需对纤维喷洒预处理剂,且密封处理24 h以上;对于长度较长的生物基纤维,为减小纤维损伤,开清棉采用“以梳代打,减少落棉,降低打手速度”的原则,梳棉工序采取“低速度,大隔距,适当增加锡林刺辊线速比”的原则,实现纤维快速转移。

喷气涡流纺长片段竹节段彩纱的设计与生产

探讨有色超短纤维在喷气涡流纺花式纱开发中的应用及生产要点。采用有色粘胶超短纤维与本色竹浆纤维、椰炭改性涤纶纤维,在并条机上加装竹节装置,利用喷气涡流纺纱技术纺制成长片段竹节段彩纱。针对粘胶纤维长度相对较短的特点,工艺上尝试采用短流程、减小隔距、降低打击件速度等技术措施。结果表明:开发的喷气涡流纺段彩纱具有段彩片段长、生产效率高的特点,其在布面上色点朦胧,时而形成连续分布,时而散点分布,形成不同于传统段彩纱的独特风格。

Spark on Yarn模式的电信大数据处理平台

针对电信大数据处理系统中存在的问题,文章提出一种基于Spark on Yarn模型的SY-TPP。在SY-TPP平台上,应用Hadoop2.0 Yarn标准,并利用Spark分布式存储技术,将SY-TPP系统的数据在内存中进行集中处理。以分级聚类算法为案例,对SY-TPP平台的开发过程进行了详细的分析。实验结果表明,TPP平台上的GB级用户可以在半个工作日内完成数据处理,而32个实体节点的SYTPP系统的速度比相同配置下的Map Reduce平台提高了10.25倍。

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。

基于改进遗传算法的细纱接头路径指引方法

为了提高细纱车间整体断头的接头效率,在细纱单锭监测系统响应到细纱断头信息的条件下,建立以整体车间断头接头路径最优化为目标函数的细纱接头路径指引模型,并运用Python编程语言分别实现对贪心算法、遗传算法和改进遗传算法接头路径模型的求解,结合传统巡回式接头路径与3种算法在效率、运行速度和解的质量上的仿真测试,对比验证改进遗传算法用于细纱接头路径指引方法的可行性。试验结果表明:相较于传统巡回式路径,改进遗传算法平均效率提升了11.8%,且解均优于其他两种算法;相较于遗传算法,改进遗传算法的响应时间缩短较多,平均为1.25 s,且波动较小,时间效率提升了80.3%。认为:改进遗传算法在效率、运行速度和解的质量上都优于其他算法,在细纱接头领域具有较高的应用优势,能够显著提高接头效率和降低成本。

纬编电热针织物设计和性能评价

为了探究金属长丝作为电加热元件在纬编电热针织物中的应用,通过赛络菲尔纺工艺开发了棉/铜镁合金和棉/不锈钢复合导电纱,并与碳纤维进行比较,采用罗纹空气层加衬纬组织制作了电加热元件并联的3种电热织物,并对导电纱和纬编织物电热性能进行实测及数据拟合。结果显示:复合导电纱达到导电纱A类产品要求;在12 V电压时,棉/铜镁合金导电纱表面温度最高可达40.9℃;金属复合导电纱电热织物升温和时间、电压有关,电热织物的表面平衡温度与输入功率呈线性关系,温度达45℃以上时,电热织物电阻变化率低于2%,热稳定性好;相同条件下,棉/铜镁合金织物升温速度最快,输入功率最大,在4 V电压下最高温度达49.8℃;采用赛络菲尔复合导电纱开发罗纹空气层加衬纬的纬编电加热织物具有一定的可行性。

基于自适应Loess的纱线张力信号处理方法

为保持针织系统中纱线张力的稳定并解决纱线张力检测过程中的噪声干扰问题,提出了一种基于自适应Loess的纱线张力信号优化方法。通过信号特征分析,将纱线张力结果划分为周期性信号、奇异点信号、高频干扰信号和低频耦合干扰信号。为消除这些干扰信号,提出了相应的数据处理方法:限幅滤波去除奇异点信号、低通滤波消除高频干扰信号以及改进的Loess方法去除低频耦合干扰信号。在3种不同情况的张力信号上验证了该方法的可靠性。实验结果显示,自适应Loess算法能有效平滑张力波动,提升信噪比分别达25.014%、27.661%、25.276%,证明了该方法在不同情况下的有效性和稳定性,为针织系统中纱线张力的稳定性和生产效率的提升提供了可行的解决方案。

纱线异纤与针织布面异纤数量的相关性研究

为了预测布面异纤的数量,采用有异纤检测功能的USTER CLASSIMAT 5型纱疵仪检测纱线异纤数量,并根据异纤的外观(长度,反射率)对异纤进行分类分级,探讨了纱疵仪检测的纱线异纤数据与对应针织布面上人工检验到的异纤数量之间的相关性。试验表明:纱疵仪检测的纱线异纤数据与对应针织布面上人工检验到的异纤数量之间表现出强相关。认为:纱线的异纤数量可作为针织工厂采购纱线时的验收基准,结合布面的异纤要求有利于合理使用纱线。

基于逆投影变换的纱筒纱线余量检测算法

纱筒纱线余量检测是纺织行业自动化生产过程中的重要一环,针对目前纱线余量检测算法检测精度低的问题,提出一种基于三点逆投影变换模型的纱筒纱线余量检测技术。根据实际生产过程中相机与纱筒的位置关系,建立三点逆投影变换模型进行初步矫正。为得到更好的图像矫正结果,设置补偿矩阵并根据内外筒位置信息优化变换矩阵,最后将逆投影变换后的图像按照改进的椭圆坐标变换模型展开为矩形,根据余量计算准则计算得到纱筒纱线余量。在自主搭建的检测平台上进行实验,结果表明,本文算法检测精度在5 mm以内,基本满足实际生产要求,可为纺织产业自动化生产提供依据。

考虑梭口因素的积极式凸轮开口设计

为协调复杂机织物对不同梭口的适应性,将共轭凸轮、平面杆组与织机开口系统的设计开发相结合,形成了一种适用于半开梭口织造的积极式凸轮开口构型。利用MatLab开发具有非对称静止时长的摆臂运动规律,有效延长下层经纱的静停时间;在机构设计、梭口分析和程序编译基础上,实现极坐标条件下的主凸轮理论廓线精确反求与压力角校核;通过路径跟踪仿真生成副凸轮理论廓线,构建基于ADAMS/View的共轭凸轮开口数字功能样机。研究结果表明:主、副凸轮与滚子之间具有良好的几何封闭接触,凸轮实际轮廓曲线达到理想共轭精度;综框运动规律与摆臂从动件高度匹配,可满足半开梭口对于下层经纱的长停要求。该设计对高速织机的梭口创新和形式改造具有重要借鉴。