Study on Licker-In and Flat Speeds of Carding Machine and Its Effects on Quality of Cotton Spinning Process

Spinning has a significant influence on all textile processes. Combinations of all the capital equipment display the process’ critical condition. By transforming unprocessed fibers into carded sliver and yarn, the carding machine serves a critical role in the textile industry. The carding machine’s licker-in and flat speeds are crucial operational factors that have a big influence on the finished goods’ quality. The purpose of this study is to examine the link between licker-in and flat speeds and how they affect the yarn and carded sliver quality. A thorough experimental examination on a carding machine was carried out to accomplish this. The carded sliver and yarn produced after experimenting with different licker-in and flat speed combinations were assessed for important quality factors including evenness, strength, and flaws. To account for changes in material qualities and machine settings, the study also took into consideration the impact of various fiber kinds and processing circumstances. The findings of the investigation showed a direct relationship between the quality of the carded sliver and yarn and the licker-in and flat speeds. Within a limited range, greater licker-in speeds were shown to increase carding efficiency and decrease fiber tangling. On the other hand, extremely high speeds led to more fiber breakage and neps. Higher flat speeds, on the other hand, helped to enhance fiber alignment, which increased the evenness and strength of the carded sliver and yarn. Additionally, it was discovered that the ideal blend of licker-in and flat rates varied based on the fiber type and processing circumstances. When being carded, various fibers displayed distinctive behaviors that necessitated adjusting the operating settings in order to provide the necessary quality results. The study also determined the crucial speed ratios between the licker-in and flat speeds that reduced fiber breakage and increased the caliber of the finished goods. The results of this study offer useful information for textile producers and process engineers to improve the quality of carded sliver and yarn while maximizing the performance of carding machines. Operators may choose machine settings and parameter adjustments wisely by knowing the impacts of licker-in and flat speeds, which will increase textile industry efficiency, productivity, and product quality.

Study on Licker-In and Flat Speeds of Carding Machine and Its Effects on Quality of Cotton Spinning Process

Spinning has a significant influence on all textile processes. Combinations of all the capital equipment display the process’ critical condition. By transforming unprocessed fibers into carded sliver and yarn, the carding machine serves a critical role in the textile industry. The carding machine’s licker-in and flat speeds are crucial operational factors that have a big influence on the finished goods’ quality. The purpose of this study is to examine the link between licker-in and flat speeds and how they affect the yarn and carded sliver quality. A thorough experimental examination on a carding machine was carried out to accomplish this. The carded sliver and yarn produced after experimenting with different licker-in and flat speed combinations were assessed for important quality factors including evenness, strength, and flaws. To account for changes in material qualities and machine settings, the study also took into consideration the impact of various fiber kinds and processing circumstances. The findings of the investigation showed a direct relationship between the quality of the carded sliver and yarn and the licker-in and flat speeds. Within a limited range, greater licker-in speeds were shown to increase carding efficiency and decrease fiber tangling. On the other hand, extremely high speeds led to more fiber breakage and neps. Higher flat speeds, on the other hand, helped to enhance fiber alignment, which increased the evenness and strength of the carded sliver and yarn. Additionally, it was discovered that the ideal blend of licker-in and flat rates varied based on the fiber type and processing circumstances. When being carded, various fibers displayed distinctive behaviors that necessitated adjusting the operating settings in order to provide the necessary quality results. The study also determined the crucial speed ratios between the licker-in and flat speeds that reduced fiber breakage and increased the caliber of the finished goods. The results of this study offer useful information for textile producers and process engineers to improve the quality of carded sliver and yarn while maximizing the performance of carding machines. Operators may choose machine settings and parameter adjustments wisely by knowing the impacts of licker-in and flat speeds, which will increase textile industry efficiency, productivity, and product quality.

A Regular Study on Yarn Count, Size Box Temperature and Machine Speed to Increase Weaving Efficiency of Cotton Fabric

Sizing is an inherent part of weaving works, consisting in the coating of the warp yarn with a polymeric adhesive, such as starch, in order to assist efficient weaving. The study is aimed to assess the effects of squeezed roller pressure, dryer temperature, yarn count, machine speed (rpm) on cotton fabric weaving. Coarser and finer cotton yarn samples were prepared using sizing solution BENSIZE 850. Different size box temperature, yarn count, fabric construction, machine speed, squeeze roller pressure were considered to construct different weaving designs to study yarn breakages parameter. A warping plan was designed on TAROKO V5.4 (190325) software. The results established that size box lower temperature and higher machine speed provide the smallest amount yarn break during weaving for coarser cotton yarn and the highest for finer cotton yarn. Size box higher temperature and lower machine speed provide maximum yarn breakage during weaving coarser cotton yarn and minimum for fine yarn. Size penetration is uniform, which provides a higher strength of the yarn to less breakage. This aspect of the research suggested that higher yarn strength gives a lesser amount of breakage.

Design Research on Drive System of Underwater Tapping Machine

The underwater tapping machine is composed of a center bit, a tapping cutter, a seal box, a main drive box, a boring bar assembly, a envelop, a gear case, a counter and so on. The drive system in underwater tapping machine consists of a worm drive, a gear drive system and a screw drive. The worm drive is in the main drive box. The worm is connected with a hydraulic motor and driven by the hydraulic motor. The gear drive system is a combined gear train which is the combinations of the fixed axes and differential gear train in the gear case. On the one hand, by means of the fixed axes gear trains the turn and power of transmission shaft are transferred to the boring bar and the screw rod, causing differential turn between the boring bar and the screw rod. On the other hand, the turns of the boring bar and the screw rod are transferred to the differential gear train. The differential gear train is used to drive a special counter to count axial travel of the boring bar. The screw drive is composed of a feed screw and a nut on the boring bar. There is the differential turn between the boring bar and the feed screw. By means of the nut, the boring bar can feed automatically. With the movement of the sliding gear 7 in the gear case, the designed drive system can also be provided with the ability of fast forward and fast backward movement of the boring bar in its idle motion, resulting in the increase of the tapping efficiency.

Impact of the Speed of Flat of a Typical Carding Machine on the Quality of Carded Sliver and 40 Ne Yarn

The carding cycle affects the sliver quality and the subsequent yarn attributes since it is the main sliver formation step. Processing parameters assume a significant part in affecting the nature of the eventual outcome in any sorts of production. In the case of carding machine, a higher production rate makes the operation more sensitive. And this will cause degradation in product quality. So optimization of speed is the talk of the town in spinning field [1]. Extreme higher speed can prompt fiber harm and unnecessary neps generation will corrupt the end result. Again lower speed will lessen the production rate which isn’t reasonable. So we need to discover the ideal speed which will be advantageous to both product quality and production rate. In carding machine, real operational activity happens between flats and cards [1]. From an ordinary perspective, high produce able cards generates higher level of speed. Speed of the cards impacts the carding cycle and the nature of the yarn and in practical point of view, flat’s level of speed is advanced and optimized. The aim of the project was to find out the optimum flat speed in the context of yarn quality. 40 Ne cotton yarns were produced with the slivers manufactured at different flat speeds such as 240, 260, 280, 300 and 320 mm/min. The quality parameters of slivers and yarns were tested and analyzed.

Modified Differential Box Counting in Breast Masses for Bioinformatics Applications

Breast cancer is one of the common invasive cancers and stands at second position for death after lung cancer.The present research work is useful in image processing for characterizing shape and gray-scale complexity.The proposed Modified Differential Box Counting(MDBC)extract Fractal features such as Fractal Dimension(FD),Lacunarity,and Succolarity for shape characterization.In traditional DBC method,the unreasonable results obtained when FD is computed for tumour regions with the same roughness of intensity surface but different gray-levels.The problem is overcome by the proposedMDBCmethod that uses box over counting and under counting that covers the whole image with required scale.In MDBC method,the suitable box size selection and Under Counting Shifting rule computation handles over counting problem.An advantage of the model is that the proposed MDBC work with recently developed methods showed that our method outperforms automatic detection and classification.The extracted features are fed to K-Nearest Neighbour(KNN)and Support Vector Machine(SVM)categorizes the mammograms into normal,benign,and malignant.The method is tested on mini MIAS datasets yields good results with improved accuracy of 93%,whereas the existing FD,GLCM,Texture and Shape feature method has 91%accuracy.

Research and application of uniform material counting method based on machine vision

Manufacturing and agricultural industries use manual methods to count materials. This leads to low accuracy and inefficiency. This paper proposes a secondary counting method that combines main and differential counting. The area-fill identification algorithm is applied to mark the counted materials. To verify the effectiveness of the proposed counting algorithm, numbers of countings are conducted for different materials, such as the screws, hole gaskets, beans, jujube, etc. The results show that the counting accuracy reaches 98% for materials with size of 2—20 mm. The method has delivered a high-efficiency and high-accuracy automatic intelligent counting, with a wide range of application prospects and reference value.

基于改进COF-YOLO v8n的油茶果静态与动态检测计数方法

针对自然环境下油茶果存在严重遮挡、近景色、小目标等现象,使用YOLO网络存在检测精度低、漏检现象严重等问题,提出对YOLO v8n网络进行改进。首先使用MPDIOU作为YOLO v8n的损失函数,有效解决因为果实重叠导致的漏检问题;其次调整网络,向其中加入小目标检测层,使网络能够关注小目标油茶以及被树叶遮挡的油茶;最后使用SCConv作为特征提取网络,既能兼顾检测精度又能兼顾检测速度。改进COF-YOLO v8n网络精确率、召回率、平均精度均值分别达到97.7%、97%、99%,比未改进的YOLO v8n分别提高3.2、4.8、2.4个百分点,其中严重遮挡情况下油茶检测精确率、召回率、平均精度均值分别达到95.9%、95%、98.5%,分别比YOLO v8n提高4.0、9.1、4.6个百分点。因此改进后COF-YOLO v8n网络能够明显提高油茶在严重遮挡、近景色、小目标均存在情况下的识别精度,减小油茶的漏检。此外,模型能够实现动、静态输入条件下油茶果计数。动态计数借鉴DeepSORT算法的多目标跟踪思想,将改进后COF-YOLO v8n的识别输出作为DeepSORT的输入,实现油茶果实的追踪计数。所得改进模型具有很好的鲁棒性,且模型简单可以嵌入到边缘设备中,不仅可用于指导自动化采收,还可用于果园产量估计,为果园物流分配提供可靠借鉴。

川西南筇竹寺组有机质富集模式与随钻有机碳含量计算

川西南筇竹寺组是四川盆地深层海相页岩气新热点,为深化地质认识,探寻低有机碳含量却高产的机理,基于测录井、实验分析资料,利用地球化学法,通过计算相关元素比值并与有机碳含量等开展相关分析,对有机质富集影响因素与有机质富集模式进行研究。结果表明,有机质富集主要影响因素有古氧化-还原环境、古生产力、陆源输入等,而且有机碳含量与P、Zr等元素正相关。研究认为该区有机质富集成藏基本条件是具有较强古生产力与陆源输入快速沉积,在上覆良好盖层条件下形成了贫氧的还原环境,进而以录井元素为基础,利用机器学习算法,随钻获取有机碳含量,探寻水平井轨迹方向有机质分布差异,为该区深化页岩气成藏认识提供更丰富素材。

基于损伤力学的矿用定向钻机金属结构寿命评估

针对ZYL-15000D型煤矿用履带式全液压定向钻机底座寿命评估,首先通过Q345B钢的脉动循环加载拉伸疲劳试验数据,拟合得到应力幅值和寿命的关系式,经由应力修正和安全系数的选取,转换得到对称循环下的应力幅值和寿命的关系式。结合基于损伤力学推导出的损伤退化模型,根据Q345B钢试验数据拟合确定损伤退化模型中的待定参数,将Q345B钢对称循环下应力幅值和寿命的关系式代入,得到Q345B钢的疲劳损伤退化模型。然后借助ABAQUS对钻机整机进行结构分析,得到钻机底座的危险点位置(即应力最大处),在该型号样机试验台上通过应变片测得钻机底座危险点处的应力—时间历程。最后采用雨流计数和应力修正,对测得的随机载荷谱进行处理得到雨流计数直方图(应力均值-幅值-循环次数),再转换为对称循环载荷的形式,代入确定的疲劳损伤退化模型中,评估钻机底座的疲劳寿命。

桌面数纸机采集气道设计及特性分析

针对桌面数纸机采集气道特殊结构,采用SolidWorks分析了真空角跟转盘贴合并相对运动过程中,不同阶段所形成的采集气道特性。分析真空角下吸气口不同结构形式对采集气道造成的影响以及转盘气道不同形式对采集气道造成的影响。结果表明,真空角下吸气口为椭圆形;转盘检测口直连对外口时,采集气道性能更好。为桌面数纸机采集气道设计和制造提供了理论依据。

基于改进YOLOv8和多目标跟踪的鱼苗计数方法

水产养殖业中鱼苗的数量检测是一个重要环节。针对传统的人工计数方法效率低、精度差、易造成鱼苗应激和损伤等问题,该研究以体长20~50 mm的草鱼苗为检测对象,提出了一种基于改进YOLOv8和多目标跟踪的小鱼苗计数方法。根据鱼苗目标小且检测速度要求高的特点,在YOLOv8算法中引入了P2小目标检测层,同时在检测头前添加GAM(global attention mechanism)注意力机制,并将目标识别损失函数优化为Inner-SIoU(inner-SCYLLA-intersection over union)损失函数以加快模型的收敛速度、提高对小目标和重叠目标的识别准确率;然后,针对检测识别到的鱼苗目标,结合多目标跟踪算法实现了一种适用于小鱼苗的跟踪计数方法。最后通过设计鱼苗计数试验平台、采集制作数据集、训练计数模型并进行计数试验验证该计数方法的优点和性能指标。试验结果表明,平均计数精度、平均绝对误差、均方根误差分别为97.16%、 3.67、 5.26,各项指标优于YOLOv5+DeepSORT、 YOLOv8+DeepSORT、 YOLOv8+StrongSORT、YOLOv8+ByteTrack、YOLOv8+BoT-SORT等方法。该研究方法能够以更快的速度和更高的准确性统计视频中小鱼苗数量,为工厂化水产养殖的鱼苗快速准确计数、生物量估计等奠定了基础。

基于ADAMS的全断面竖井掘进机锥形刀盘载荷特性

全断面竖井掘进机锥形刀盘在竖井施工中具有导向性好、姿态调整方便等优点,滚刀作为竖井掘进机施工中最直接的破岩工具,获取其载荷分布规律对于竖井掘进机的刀盘刀具系统的合理设计和掘进过程中装备姿态的调整具有重要意义。基于ADAMS建立了全断面竖井掘进机锥形刀盘与岩体耦合模型,合理施加约束条件对正常掘进工况进行仿真,获取了竖井掘进机锥形刀盘刀具的载荷分布规律。结果表明:由于安装方式的不同,正滚刀所受到的侧向力和滚动力均大于中心滚刀,对正滚刀刀座与辐条之间的连接强度提出了更高要求;随着正滚刀在刀盘上安装半径的增大,其受到的垂直力也增大。中心滚刀所受到合力载荷稳定性较正滚刀明显较好,波动更小;随着滚刀在刀盘上安装半径的增加,滚刀所受到的合力载荷大小波动更大,载荷稳定性也更差。

基于深度学习的到课率统计系统设计与实现

到课率作为宏观教学管理数据,对高校教学管理具有重要作用。虽然近年来出现了一些课率统计的数字化方法,解决了传统到课率统计费时、费力、滞后等问题,但由于成本高、使用不方便、准确率不高等原因,导致其无法推广。随着技术的发展,深度学习在多目标检测中的准确率越来越高,有助于解决此类问题。为此,文章利用深度学习技术,设计了一种基于教室摄像头RTSP视频流的到课学生头部识别的模型1MB-Plus,并将其应用于某高校的一百余间教室的到课率统计中,取得了97.3%的准确率。研究表明,该模型有助于解决到课率统计存在的问题。文章通过研究,旨在以最小的成本为高校教务管理部门提供较为准确的宏观到课率数据,辅助学校的教学管理工作。

基于无人机图像的荔枝果实计数与产量预测模型研究

本文研究了基于无人机(Unmanned Aerial Vehicle,UAV)图像技术的荔枝果实计数与产量预测模型。通过利用无人机搭载的高分辨率相机采集荔枝果园的荔枝图像,结合图像处理和机器学习算法,实现了荔枝果实的精准计数与产量预测。该方法不仅提高了数据采集效率,还显著提升了产量预测的精度,为荔枝产业的精准管理和决策支持提供了科学依据。

WiCount:一种基于WiFi-CSI的人数识别方法

人数识别即是对一定区域内活动人数的监测计数,在人群控制、流量监管等方面有着重要应用。例如,在百货商场或者机场中,对排队人数或者服务区休息人数进行估计可以为提升服务质量做出贡献。目前,研究人员已提出了一些基于摄像头和可穿戴设备的人数识别方法,但是这些方案均存在一些不足,例如摄像头只能提供可视范围内的监控,可穿戴设备需要被监控对象有意识地穿戴。也有一些学者利用雷达相关技术实现了穿墙式感知识别,但是这类系统设计复杂,应用成本较高,多用于军事领域。文中提出了一种基于WiFi信号的室内人数识别方案WiCount,其利用信道状态信息(Channel State Information,CSI)的幅值波动来刻画室内人数的变化,利用机器学习算法实现对人的计数。WiCount旨在进行更细粒度的室内人数识别,即人在室内任意位置时该方法均能准确识别人数。它根据室内人数与CSI幅值变化的关系,提取了有效的数学特征,减弱了相同人数在室内不同位置所产生的CSI幅值波动差异,然后通过训练3种分类器(SVM、KNN、BP神经网络)来识别监测区域内的人数。在实验室和会议室分别部署了验证系统,结果显示,在人数规模较小的情况下,所提方法的识别效果良好。其中,实验室环境下,不超过4人时,系统的识别率达90%;会议室环境下,不超过2人,在监测区域内任意位置活动时,系统的识别率可达89.58%。

Application Research of Robust LS-SVM Regression Model in Forecasting Patent Application Counts

A forecasting system of patent application counts is studied in this paper. The optimization model proposed in the research is based on support vector machines （SVM）, in which cross-validation algorithm is used for preferences selection. Results of data simulation show that the proposed method has higher forecasting precision power and stronger generalization ability than BP neural network and RBF neural network. In addi- tion, it is feasible and effective in forecasting patent application counts.

基于机器视觉的松林天牛远程智能监测系统

松墨天牛和褐梗天牛是松树上两种重要蛀干害虫,及时获取松林天牛的数量变化趋势是松林害虫精准防治的重要前提。为此,该研究构建一款基于机器视觉的松林天牛远程智能监测系统。系统主要由诱捕器模块、天牛检测模块和系统Web端三部分组成。诱捕器模块通常放置于松林重点区域来诱捕天牛害虫,并通过摄像头定时采集天牛图像;天牛检测模型部署于边缘端,以深度学习YOLOv5s模型为基础搭建轻量化检测模型,实现边缘端的天牛实时检测统计;检测结果经无线传输在系统Web端进行呈现,实现天牛数据可追溯。试验结果表明,智能监测系统对天牛监测效果良好,模型的准确率为94.4%,召回率为93.6%,IoU阈值为0.5下的平均精度均值(m_(AP0.5))为96.2%,单张推理耗时为1.40 s,模型大小为9.3 MB;用户可通过系统Web端查看天牛数量变化趋势。该系统可实现诱捕器场景下的天牛远程智能监测,对提高森林害虫防控智能化水平具有重要意义。

臭氧消毒机固定对病区床单位消毒合格率及菌落数的影响

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基于机器视觉的水产养殖计数研究综述

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