Modal/Wool混纺精纺纱的实践

介绍新型绿色纤维Modal和毛混纺的生产工艺 ,对该生产工艺进行了研究与分析 。

Structural Modal Parameter Recognition and Related Damage Identification Methods under Environmental Excitations: A Review

Modal parameters can accurately characterize the structural dynamic properties and assess the physical state of the structure.Therefore,it is particularly significant to identify the structural modal parameters according to the monitoring data information in the structural health monitoring(SHM)system,so as to provide a scientific basis for structural damage identification and dynamic model modification.In view of this,this paper reviews methods for identifying structural modal parameters under environmental excitation and briefly describes how to identify structural damages based on the derived modal parameters.The paper primarily introduces data-driven modal parameter recognition methods(e.g.,time-domain,frequency-domain,and time-frequency-domain methods,etc.),briefly describes damage identification methods based on the variations of modal parameters(e.g.,natural frequency,modal shapes,and curvature modal shapes,etc.)and modal validation methods(e.g.,Stability Diagram and Modal Assurance Criterion,etc.).The current status of the application of artificial intelligence(AI)methods in the direction of modal parameter recognition and damage identification is further discussed.Based on the pre-vious analysis,the main development trends of structural modal parameter recognition and damage identification methods are given to provide scientific references for the optimized design and functional upgrading of SHM systems.

Modal／Wool混纺精纺纱的实践

1 使用原料情况 纤维细度是设计毛纱截面纤维根数、决定产品手感和毛纱条干均匀度最重要的品质参数，而细度CV值对毛纱线强度和仲长率有直接关系，

Sparse Modal Decomposition Method Addressing Underdetermined Vortex-Induced Vibration Reconstruction Problem for Marine Risers

When investigating the vortex-induced vibration(VIV)of marine risers,extrapolating the dynamic response on the entire length based on limited sensor measurements is a crucial step in both laboratory experiments and fatigue monitoring of real risers.The problem is conventionally solved using the modal decomposition method,based on the principle that the response can be approximated by a weighted sum of limited vibration modes.However,the method is not valid when the problem is underdetermined,i.e.,the number of unknown mode weights is more than the number of known measurements.This study proposed a sparse modal decomposition method based on the compressed sensing theory and the Compressive Sampling Matching Pursuit(Co Sa MP)algorithm,exploiting the sparsity of VIV in the modal space.In the validation study based on high-order VIV experiment data,the proposed method successfully reconstructed the response using only seven acceleration measurements when the conventional methods failed.A primary advantage of the proposed method is that it offers a completely data-driven approach for the underdetermined VIV reconstruction problem,which is more favorable than existing model-dependent solutions for many practical applications such as riser structural health monitoring.

An Approach to Assertive and Non-assertive Epistemic Modality in Virginia Woolf＇s Essays

This study entails a first approach to epistemic modality in Woolf＇s essays. In particular, we will distinguish between assertive and non-assertive epistemic modality. Woolf uses assertive epistemic modality when transmitting her criticism to, sometimes, women＇s inferiority condition in history, revealing Woolf＇s more confident stance. These markers are different from those used in more ＂gentle＂ themes related to reviewing an author and her work. In this second case, non-assertive epistemic modality is more frequent. Being less harsh towards an author＇s surroundings and her literary production, Woolf＇s stance proves less self-assured. The corpus consists of 10 short essays. We also refer to her longer essays A Room of One＇s Own （1929） and Three Guineas 0938）. They were selected bearing in mind their subject-matter. Half of them deal with literature and women writers, their lives and works; the other half ones also have to do with women-related topics, but referring to their position in history and their difficulties to undertake, for example, a literary career. Some concluding remarks indicate the predominance of non-assertive epistemic modality in relation to an intersubjective reading of her texts. This reading favours the inclusion of her audience in her commentary about women writing.

Synthesis and Characterization of Nonionic Waterborne Polyurethane and Application to Wool Fabric Finishing

A nonionic waterborne polyurethane(WPU) was synthesized by the self-emulsification method using polyether diol(N220),isophorone diisocyanate(IPDI),trimethylolpropane poly(ethylene glycol monomethyl ether)(N120),1,4-butanediol(BDO) and trimethylolpropane(TMP) as the main materials.The effects of the NCO/OH ratio on the emulsion and film properties of NWPU were explored.The experimental results show that the NWPU prepared at an NCO/OH ratio of 1.1 has good emulsion stability and easy film formation,and the resultant film was elastic,soft,and transparent.The sample was used for wool finishing and the application performance was evaluated.When the NWPU dosage reached 40 g·L^(-1),the fabric area felt shrinkage rate reduced from 8.97% to 4.75%,the pilling rating raised from grade 2-3 to grade 4,and the whiteness value only decreased by 3.87%.

Utilization of biomarkers for the prognostic prediction of cardiac arrest survivors using a multi-modal approach

International guidelines for post-cardiac arrest care recommend using multi-modal strategies to avoid the withdrawal of life-sustaining therapy(WLST)in patients with the potential for neurological recovery.[1]However,a clear methodology for multi-modal approaches has yet to be developed.Neuron-specific enolase(NSE)is currently the only recommended biomarker,and the European Resuscitation Council(ERC)and the European SocietyofIntensiveCareMedicine(ESICM)have proposed a cutoff value of 60μg/L at 48 and/or 72 h after the return of spontaneous circulation(ROSC)as a multimodal prognostic tool for predicting poor neurological outcomes.

Multimodal Data-Driven Reinforcement Learning for Operational Decision-Making in Industrial Processes

Dear Editor, This letter proposes a multimodal data-driven reinforcement learning-based method for operational decision-making in industrial processes. Due to the frequent fluctuations of feedstock properties and operating conditions in the industrial processes, existing data-driven methods cannot effectively adjust the operational variables. In addition, multimodal data such as images, audio.

The role of multimodality imaging in calcified valves with infective endocarditis

A 68-years old woman presented with ahistory of recurrent fever of 38-39°ac-companied by chills and weakness over the past month.Her physical examination was unremarkable except for an audible 3/6 ejection murmur at the 2nd right intercostal space.Her vital signs were normal with no fever at presentation.Laboratory tests showed elevated white blood count of 11,800cells/mm3 with a remarkable neutrophilia and elevated C-reactive protein of 14 mg/dL.Blood glucose,renal and liver function tests were all normal.

Generalized load graphical forecasting method based on modal decomposition

In a“low-carbon”context,the power load is affected by the coupling of multiple factors,which gradually evolves from the traditional“pure load”to the generalized load with the dual characteristics of“load+power supply.”Traditional time-series forecasting methods are no longer suitable owing to the complexity and uncertainty associated with generalized loads.From the perspective of image processing,this study proposes a graphical short-term prediction method for generalized loads based on modal decomposition.First,the datasets are normalized and feature-filtered by comparing the results of Xtreme gradient boosting,gradient boosted decision tree,and random forest algorithms.Subsequently,the generalized load data are decomposed into three sets of modalities by modal decomposition,and red,green,and blue(RGB)images are generated using them as the pixel values of the R,G,and B channels.The generated images are diversified,and an optimized DenseNet neural network was used for training and prediction.Finally,the base load,wind power,and photovoltaic power generation data are selected,and the characteristic curves of the generalized load scenarios under different permeabilities of wind power and photovoltaic power generation are obtained using the density-based spatial clustering of applications with noise algorithm.Based on the proposed graphical forecasting method,the feasibility of the generalized load graphical forecasting method is verified by comparing it with the traditional time-series forecasting method.

Modal Frequency Prediction of Chladni Patterns Using Machine Learning

The introduction of machine learning (ML) in the research domain is a new era technique. The machine learning algorithm is developed for frequency predication of patterns that are formed on the Chladni plate and focused on the application of machine learning algorithms in image processing. In the Chladni plate, nodes and antinodes are demonstrated at various excited frequencies. Sand on the plate creates specific patterns when it is excited by vibrations from a mechanical oscillator. In the experimental setup, a rectangular aluminum plate of 16 cm x 16 cm and 0.61 mm thickness was placed over the mechanical oscillator, which was driven by a sine wave signal generator. 14 Chladni patterns are obtained on a Chladni plate and validation is done with modal analysis in Ansys. For machine learning, a large number of data sets are required, as captured around 200 photos of each modal frequency and around 3000 photos with a camera of all 14 Chladni patterns for supervised learning. The current model is written in Python language and model has one convolution layer. The main modules used in this are Tensor Flow Keras, NumPy, CV2 and Maxpooling. The fed reference data is taken for 14 frequencies between 330 Hz to 3910 Hz. In the model, all the images are converted to grayscale and canny edge detected. All patterns of frequencies have an almost 80% - 99% correlation with test sample experimental data. This approach is to form a directory of Chladni patterns for future reference purpose in real-life application. A machine learning algorithm can predict the resonant frequency based on the patterns formed on the Chladni plate.

Sustainable art exhibition RETURN visualizes wool's biodegradable properties

In early April,The Woolmark Company,the authoritative wool textiles organization,presented RETURN,a sustainable art exhibition at R130,a buyer shop during Shanghai Fashion Week,where three pieces of naturally degraded wool fabrics were on display for the first time in China.The exhibition visualised the biodegradation process of this natural fabric,leading people to experience the natural charm of wool through art and natural beauty.

Analysis of Commuting Modal Shift in Consideration of Social Interaction of Consciousness for Environment

It is the matter for achievement of the low carbon transport system that the excessive use of private vehicles can be controlled appropriately.Not only improvement of service level of modes except private vehicle,but also consciousness for environmental problem of individual trip maker is important for eco-commuting promotion.On the other hand,consciousness for environment would be changed by influence of other person.Accordingly,it is aimed in the study that the structure of decision-making process for modal shift to the eco-commuting mode in the local city is described considering environmental consciousness and social interaction.For the purpose,the consciousness for the environment problem and the travel behavior of the commuter at the suburban area in the local city are investigated by the questionnaire survey.The covariance structure about the eco-consciousness is analyzed with the database of the questionnaire survey by structural equation modeling.As the result,it can be confirmed with the structural equation model that the individual environmental consciousness is strongly related with the intention of self-sacrifice and is influenced with the local interaction of the individual connections.On the other hand,the intention of modal shift for the commuting mode is analyzed with the database of the questionnaire survey.It can be found out that the environmental consciousness is not statistically significant for commuting mode choice with the present poor level of service of public transport.However,the intention of self-sacrifice for the prevention of the global warming is statistically confirmed as the factor of modal shift with the operation of eco-commuting bus service with the RP/SP integrated estimation method.As the result,the multi-agent simulation system with social interaction model for eco consciousness is developed to measure the effect of the eco-commuting promotion.For the purpose,the carbon dioxide emission is estimated based on traffic demand and road network condition in the traffic environment model.On the other hand,the relation between agents is defined based on the small world network.The proposed multi-agent simulation is applied to measure the effect of the eco-commuting promotion such as improvement of level of service on the public transport or education of eco-consciousness.The effect of the promotion plan can be observed with the proposed multi-agent system.Finally,it can be concluded that the proposed multi-agent simulation with social interaction for eco-consciousness is useful for planning of eco-commuting promotion.

基于音频特征的拖拉机发动机状况识别系统设计

拖拉机发动机是保证拖拉机正常运行的关键部件,目前主要采用振动信号开展发动机故障预测与状况识别。为此,提出了一种基于GRU的循环神经网络模型,通过对拖拉机发动机在不同作业条件下产生的音频信号进行分析,提取Mel作为主要特征,构建基于音频特征的拖拉机发动机状况识别系统。预测结果表明:系统能够准确地识别发动机的正常运行状态和不同类型的故障状况,对拖拉机发动机异常的识别率可以达到97.15%。研究结果可以提高拖拉机的运行安全性和可靠性,减少故障停机时间,提高农业生产效率。

用水溶性维纶伴纺高支羊绒/Modal产品

采用水溶性维纶伴纺技术生产的高支轻薄羊绒/Modal产品轻薄滑爽,手感柔软,悬垂性好,服用性能优良,而且生产成本低。文章对纤维的各项性能进行了分析,并重点介绍了纺纱各工序的关键工艺与措施。

Modal 14.8 tex赛络纺针织纱重定量纺纱实践

探讨Modal 14.8 tex赛络纺针织纱重定量纺纱工艺。分析阐述了Modal赛络纺针织纱的特点以及推行重定量工艺的必要性通过正确选择细纱设备,试验优化有关工艺参数配置,采用高性能纺纱专件,严格控制温湿度等工艺技术措施,在稳定细纱质量的前提下,使粗纱定量增加33.3%,有效地减少了前纺机台的配置和用工,提高了生产效率,增加了企业经济效益。指出:采用重定量工艺是提高Modal赛络纺针织纱生产率的一条有效途径。

Modal纤维紧密纺纱的开发

探讨紧密纺技术在Modal纤维纺纱过程中的应用,通过优化试验确定纺纱过程中梳理、并条、粗纱、细纱及络筒的工艺参数。在对成纱原理分析的基础上,优选出紧密纺中最优的集聚张力和负压值,并确定出钢领、钢丝圈及网格圈。通过与紧密纺和普通纺成纱质量进行对比发现,采用紧密纺技术生产的纱线质量较好,毛羽少,条干不匀率仅为10.75%,且断裂强度达到20.73 cN/tex,此外纱线的断头率、纱疵率均降低,生产效率相对提高,体现出紧密纺纱的优越性。

精梳棉/Modal/山羊绒毛针织纱的纺纱实践

文章介绍了精梳棉、Modal纤维及山羊绒的性能及其混纺纱的工艺及质量控制实践。

Modal针织物的数码喷射印花工艺

讨论了莫代尔(Modal)纤维针织物数码喷射印花上浆处方中海藻酸钠、改性淀粉、防染盐S、硫酸钠、碳酸氢钠、增稠剂和尿素的选择对表观颜色深度的影响,并测试了印花织物的色牢度。优化的数码喷射印花浆料处方是海藻酸钠1%,改性淀粉3%,碳酸氢钠4%,尿素9%,防染盐S 2.5%,硫酸钠3%,增稠剂3%。印花织物的各项牢度优良,耐水洗、耐摩擦牢度均达到4级以上。

Modal棉混纺纱性能与混纺比的关系

分析了Modal棉混纺纱的条干CV、毛羽及拉伸性能与混纺比之间的关系,试验结果表明,不论是29.15 tex还是14.5 tex混纺纱,其条干CV、毛羽指数和拉伸性能,随着混纺比的变化规律基本相同,并不是随着Modal纤维含量的增加而一直改善或恶化,而是随着Modal含量的减少先得到改善,而后又在某处恶化,再改善再恶化的规律变化,在实际生产中,应避开这一混纺比。