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。

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.

Calculation of the Coupling Coefficient of Twin-Core Fiber Based on the Supermode Theory with Finite Element Method

Currently, coupled mode theory (CMT) is widely used for calculating the coupling coefficient of twin-core fibers (TCFs) that are used in a broad range of important applications. This approach is highly accurate for scenarios with weak coupling between the cores but shows significant errors in the strong coupling scenarios, necessitating the use of a more accurate method for coupling coefficient calculations. Therefore, in this work, we calculate the coupling coefficients of TCFs using the supermode theory with finite element method (FEM) that has higher accuracy than CMT, particularly for the strong coupling TCF. To investigate the origin of the differences between the results obtained by these two methods, the modal field distributions of the supermodes of TCF are simulated and analyzed in detail.

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.

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.

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.

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.

喷气涡流纺纱机的发展现状及趋势

为了进一步推进喷气涡流纺纱机的发展,分析了喷气涡流纺纱机在全球的存量分布情况、近8年国内累计进口量及主要省份等市场情况,回顾了喷气涡流纺纱机的国内外技术发展历程,阐述了喷气涡流纺纱技术的创新应用,介绍了相关政策支持,并提出了喷气涡流纺纱机的发展思路。认为:我国喷气涡流纺纱机存量具有全球优势地位,正处于产业快速发展期,市场空间广阔,未来随着各种新工艺新技术层出不穷,在开发喷气涡流纺花式纱、包芯纱、细号纱以及差异化原料应用的优势将不断凸显,但国内急需开发拥有自主知识产权的喷气涡流纺纱机,突破关键技术装备和工艺,促使喷气涡流纺纱机在我国稳步健康发展。

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。

数字化配色技术在色纺纱领域的应用

数字化配色技术有利于纺织行业的数字化和智能化体系建设和发展,能够更好地满足市场和消费者需求。文章分析了目前数字化配色技术的最新发展和应用领域,提出了数字化配色技术的概念以及应用于色纺纱生产中的具体步骤,阐述了数字化配色技术应用在色纺纱领域的优点和局限性,并通过具体案例指明了数字化配色技术在色纺企业实际开发应用中的重要作用。

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。

ZJ116型卷接机组吸丝带转向带轮机构的改进

为解决ZJ116型卷接机组吸丝带转向带轮拆装不方便,轴承更换作业时间长的问题,对转向带轮机构进行了改进。设计了一种三段组合式偏心轴代替原来的一体式偏心轴,使得转向带轮拆装方便、快捷,从而减少了转向带轮轴承更换作业时间。以济南卷烟厂的ZJ116型卷接机组为对象进行测试,结果表明:改进后的吸丝带转向带轮轴承更换作业时间缩短了约66.6%,且对转向带轮轴承使用寿命无显著负面影响。该方法可为提高设备维修效率、降低维修人员劳动强度提供技术支持。

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。

喷气涡流纺新型纱线开发探析

为提升开发喷气涡流纺纱线的技术水平,介绍喷气涡流纺纱在我国的发展历程及存在问题,对比环锭纺、转杯纺、喷气涡流纺的优势、局限及技术特性;详细分析喷气涡流纺开发多种纤维混纺纱、包芯包覆纱、有色纱等特色纱线开发途径;总结发展喷气涡流纺需注意的问题。指出:喷气涡流纺纱尚无法替代环锭纺与转杯纺;开发喷气涡流纺纱线产品时,应避免同质化竞争,合理配置纺纱速度,不断优化纺纱工艺配置,努力拓宽喷气涡流纺纱线的应用领域。