立体织物整体穿刺钢针阵列布放装置研制及实验验证

针对立体织物整体穿刺钢针阵列大量超长,穿刺钢针由人工布放的不足,设计了适用于钢针阵列自动化布放的穿刺模板工装,研制了用于立体织物整体穿刺钢针阵列布放的装置。这种装置由高重复定位精度水平移动平台、机身、钢针存储及施放机构等组成,通过红光十字激光器对穿刺模板等距密排精密微小孔进行定位。控制系统采用触摸屏为人机交互界面,运用具有多路脉冲输出的PLC控制移动平台的伺服电动机和钢针施放机构的闭环步进电动机,以保证整体穿刺钢针阵列布放工作可靠、持续进行。为验证整体穿刺钢针阵列布放装置工作原理的可行性,进行了T形截面整体穿刺钢针阵列的布放实验,结果表明布放装置原理可行,工作可靠。     

基于机器学习的医疗器械分类与预测方法研究

针对当前医疗器械分类仍然采用人工分类方式,费时费力的问题,提出一种基于机器学习的医疗器械分类与预测方法,通过引入机器学习和自然语言处理领域的经典算法,以新版《医疗器械分类目录》为标准,提取医疗器械产品注册证的关键信息作为语料库,实现对医疗器械的产品类别划分,达到真正意义上的医疗器械自动分类,为各级医疗机构的医疗器械分类管理信息化奠定基础,提供借鉴和启示。     

机器学习在食品贮藏品质预测中的应用

食品贮藏和流通过程中会出现不同程度的品质劣变现象。随着人们对食品品质和安全重视程度的不断提高，开展食品贮运过程中的品质预测研究，对食品品质调控具有重要意义。本文综述机器学习在食品贮藏品质预测中的研究进展，包括常规的品质预测方法及其局限性。重点介绍近年发展快、应用广的集成学习和人工神经网络算法以及预测性能评估方法，展望机器学习在食品领域的未来发展趋势，为开展食品科学的交叉研究提供参考。     

自动机器学习工具Auto-Keras在机场跑道视程预测中的应用

利用2010年1月至2021年2月的乌鲁木齐机场自动观测系统历史数据,使用自动机器学习工具Auto-Keras构建机场跑道视程预测模型,通过将过去2小时逐分钟跑道视程作为因子,输出机场跑道视程预测产品,经过独立的样本检验可以看到,Auto-Keras无需进行数据特征分析、参数选择等流程,即可自动完成最优模型的训练,并能够输出较为客观的跑道视程预报产品,改善现有低能见度天气下对跑道视程的预测水平。     

用测穿刺力的方法评定织物的松紧程度

本文对穿刺过程进行了理论分析,通过实验测得了各类织物的穿刺曲线,借此方法研究了织物的松紧程度,并将测试结果作聚类分析,证明这种方法能反映出不同风格织物的差异,可以作为评定织物松紧程度的方法之一。     

基于机器学习的箱纸板质量预测模型构建及比较

箱纸板生产涉及一系列复杂工艺流程,且由于缺乏关键质量的在线监测手段,进而导致质量管控困难。为此,本研究尝试基于机器学习方法建立可在线监测箱纸板质量的预测模型,也称软测量模型,以促进上述问题的有效解决。本研究采用箱纸板企业实际数据,训练并比较了随机森林（RF）、梯度提升回归（GBR）、K近邻回归（KNN）及偏最小二乘回归（PLS）在多项质量指标上的预测表现。结果表明,不同质量指标本身很大程度上影响了预测精度的上限,而不同算法对理论上限的逼近程度有显著差异。复杂、非线性的集成模型（RF、GBR）相较于简单模型（KNN、PLS）有更好的表现。     

基于机器学习算法的食品污染物神经毒性预测模型建立

基于机器学习算法建立分类预测模型,研究常见食品中化学性污染物的理化结构与其神经毒性间关联。通过查阅文献建立化合物数据库,纳入包含影响神经分化成熟、影响神经元迁移/空间定向等各类神经毒性机制化合物57种,无神经毒性化合物50种。运用R、SPSS软件,使用随机森林（Random Forests,RF）、类神经网络（Artificial Neural Network,ANN）、支持向量机（Support Vector Machine,SVM）等机器学习算法筛选分子描述符并构建分类模型,预测化合物神经毒性。结果显示随机森林算法模型综合表现最佳,十折交叉验证准确率70.24%,训练集、测试集预测准确率分别达95.51%和83.33%,曲线下面积分别达0.99和0.85,是个较为理想的算法。本研究基于机器学习算法建立的分类模型可通过化合物的分子描述符准确预测化合物的神经毒性。在多种机器学习算法中,基于随机森林算法建立的预测模型表现最优。分子描述符重要性结果显示,化合物神经毒性主要与其质量加权Burden矩阵最大特征值有关。     

基于机器学习方法的智能家居安防系统探究

近年来,随着人们的生活水平不断提高,智能化系统和机器学习方法也在不断发展.现代智能化家居安防系统就是在家庭中布置各类传感设备,门禁系统、监控系统、警报系统等.通过这些传感器,可以实时监测各类异常情况并进行紧急处理,并能通过手机端及时给人们发送邮件、短信以及视频,这样人们就可以实时掌握家庭情况.利用机器学习的方法,可以更...     

基于机器学习的切丝后含水率预测及控制方法

选取云烟(A)牌号制丝生产过程稳态数据样本,采用递归特征消除法分析模型的影响变量。基于车间温湿度SARIMAX预测模型,利用蒙特卡洛仿真、神经网络算法和XGBoost算法建立切丝后含水率控制模型,通过预测值与实际值对比的方法进行模型检验。结果表明,在工艺标准值±0.15%的误差范围内,切丝后含水率准确率由62.57%提升至86.49%;切丝后含水率的过程能力指数达标率由91.44%提升至97.30%。该方法实现了前后工序参数协同和精准控制,有效保证了制丝过程中切丝后含水率的稳定性。     

基于机器学习的素描图像处理技术

图像素描化在激光雕刻领域中发挥着极其重要的作用,传统的图像素描处理方法是不同的图像采用同一种算法进行处理,存在适用率不高和部分参数不通用问题。文章提出一种基于机器学习的素描图像处理技术,通过搭建素描图像处理神经网络系统,使用构建的素描图片数据库对模型进行多次迭代训练,最后达到预期素描处理效果,提高了图片素描化处理的普适性。     

基于机器学习的布匹瑕疵识别研究

纺织工业是我国制造业出口的重要组成部分。布匹的质量控制在纺织工业中尤为重要,而布匹瑕疵是影响布匹质量控制的重要因素之一。在中小企业中,布匹瑕疵识别主要依靠人工流水线作业,存在着人工成本高、人眼识别准确度低等问题。因此,一个有效的布匹瑕疵检验系统是十分必要的,布匹瑕疵分类算法是保证疵点判决效率的核心。基于布匹生产企业存在的问题,有针对性地研究了机器学习与计算机视觉的布匹瑕疵识别算法的基本原理,介绍了各类布匹瑕疵识别中的检测与分类算法,将最近发展迅速的机器学习的理论研究引入布匹瑕疵识别中,对涉及机器学习的模式识别算法进行了介绍。     

Prediction of fish quality level with machine learning

In this study, sea bream, sea bass, anchovy and trout were captured and recorded using a digital camera during refrigerated storage for 7 days. In addition, their total viable counts (TVC) were determined on a daily basis. Based on the TVC, each fish was classified as ‘fresh’ when it was <5 log cfu per g, and as ‘not fresh’ when it was >7 log cfu per g. They were uploaded on a web-based machine learning software called Teachable Machine (TM), which was trained about the pupils and heads of the fish. In addition, images of each species from different angles were uploaded to the software in order to ensure the recognition of fish species by TM. The data of the study indicated that the TM was able to distinguish fish species with high accuracy rates and achieved over 86% success in estimating the freshness of the fish species tested.     

基于近红外光谱结合机器学习的鳕鱼品种二分类方法研究

目的 鳕鱼假冒伪劣事件层出不穷,为保障鳕鱼肉制品购买者能够买到放心的鳕鱼产品,探索适合分析鳕鱼近红外光谱数据的机器学习模型,实现鳕鱼品种的快速二分类。方法 选取挪威大西洋真鳕、冰岛黑线鳕等8种鳕鱼,对其研磨物进行傅里叶变换近红外光谱测试,并采用Min-Max归一化和独立成分分析法对近红外光谱数据进行预处理和降维,进一步分别使用9种机器学习模型进行二分类,通过6项指标对比各个模型的预测效果,从中选出最适合鳕鱼二分类的模型。结果 本文提出的独立成分析法结合支持向量机的鳕鱼品种二分类模型的预测准确率可达到97.2%,F1分数可达到97.3%,召回率达到99.4%。结论 本研究可实现较为准确的大西洋鳕鱼和非大西洋鳕鱼品种的分类,为鳕鱼品种鉴别提供了方法依据。     

Prediction of insemination outcomes in Holstein dairy cattle using alternative machine learning algorithms

When making the decision about whether or not to breed a given cow, knowledge about the expected outcome would have an economic impact on profitability of the breeding program and net income of the farm. The outcome of each breeding can be affected by many management and physiological features that vary between farms and interact with each other. Hence, the ability of machine learning algorithms to accommodate complex relationships in the data and missing values for explanatory variables makes these algorithms well suited for investigation of reproduction performance in dairy cattle. The objective of this study was to develop a user-friendly and intuitive on-farm tool to help farmers make reproduction management decisions. Several different machine learning algorithms were applied to predict the insemination outcomes of individual cows based on phenotypic and genotypic data. Data from 26 dairy farms in the Alta Genetics (Watertown, WI) Advantage Progeny Testing Program were used, representing a 10-yr period from 2000 to 2010. Health, reproduction, and production data were extracted from on-farm dairy management software, and estimated breeding values were downloaded from the US Department of Agriculture Agricultural Research Service Animal Improvement Programs Laboratory (Beltsville, MD) database. The edited data set consisted of 129,245 breeding records from primiparous Holstein cows and 195,128 breeding records from multiparous Holstein cows. Each data point in the final data set included 23 and 25 explanatory variables and 1 binary outcome for of 0.756 ± 0.005 and 0.736 ± 0.005 for primiparous and multiparous cows, respectively. The naïve Bayes algorithm, Bayesian network, and decision tree algorithms showed somewhat poorer classification performance. An information-based variable selection procedure identified herd average conception rate, incidence of ketosis, number of previous (failed) inseminations, days in milk at breeding, and mastitis as the most effective explanatory variables in predicting pregnancy outcome.     

基于深度学习的布匹疵点检测方法

为快速、准确检测布匹疵点,提出以深度学习目标检测框架YOLOv4为基础的布匹疵点检测方式,首先将5种常见疵点图像(吊经、百脚、结点、破洞、污渍)进行预处理,然后将图像输入到YOLOv4算法中进行分类。YOLOv4采用CSPDarknet53作为主干网络提取疵点特征,SPP模块、FPN+PAN的方式作为Neck层进行深层疵点特征提取,预测层采用3种尺度预测方式,对不同大小的疵点进行检测。研究结果表明:经600个测试集样本的验证,该方法对疵点图像的检测准确率达95%,检测单张疵点图像的速率为33 ms。与SSD、Faster R-CNN、YOLOv3方法进行比较,采用YOLOv4方法准确率更高,速度更快。     

基于机器学习方法的人体农兽药及化学污染物暴露与高血压的相关性研究

目的 基于不同的机器学习方法探究石家庄与杭州成年居民体内农兽药及化学污染物暴露与高血压患病情况之间的关系。方法 采用2018—2019年在石家庄与杭州进行的“降低成年超重者营养相关慢性病风险的适宜身体活动量研究”调查数据,选择496名包含人口学资料、体格测量、常规血清检测和血清农兽药及化学污染物暴露信息的成年居民作为研究对象,在Lasso变量筛选后分别使用传统的逻辑回归模型与多种机器学习模型建立高血压的预测模型,利用ROC曲线下面积（AUC）评估模型效果。结果 Lasso变量筛选结果显示,农兽药及化学污染物暴露4-氯苯氧乙酸（4-CPA）、全氟辛酸（PFOA）、全氟己烷磺酸（PFHxS）和全氟辛烷磺酸（PFOS）与高血压具有显著的关联。机器学习模型中支持向量机模型预测效果最好（AUC=0.71）,优于传统的逻辑回归模型（AUC=0.57）。结论 农兽药及化学污染物暴露中4-CPA、PFOA、PFHxS和PFOS是高血压的重要危险因素,机器学习模型在流行病学影响因素研究中具有很好的适应性,在拟合非线性关系的数据时有一定的优势。     

基于GMM-MD组合算法的过程工业故障预测模型

介绍了一种基于高斯混合模型（GMM）和马氏距离（MD）组合算法的过程工业故障预测模型。该模型首先通过相关系数去除冗余变量和无关变量,然后通过K-Means聚类算法标记故障前的异常数据以获得核心特征变量,最后基于GMM-MD组合算法构建健康指标,以评估生产过程的健康程度。利用国内某造纸厂实时生产数据对该模型进行验证;结果表明,该模型的故障预测精准率为76.82%,召回率为72.50%,可较好地跟踪造纸过程设备运行状态的变化过程,起到过程工业故障预测作用。     

Prediction of detailed blood metabolic profile using milk infrared spectra and machine learning methods in dairy cattle

The adoption of preventive management decisions is crucial to dealing with metabolic impairments in dairy cattle. Various serum metabolites are known to be useful indicators of the health status of cows. In this study, we used milk Fourier-transform mid-infrared (FTIR) spectra and various machine learning (ML) algorithms to develop prediction equations for a panel of 29 blood metabolites, including those related to energy metabolism, liver function/hepatic damage, oxidative stress, inflammation/innate immunity, and minerals. For most traits, the data set comprised observations from 1,204 Holstein-Friesian dairy cows belonging to 5 herds. An exception was represented by β-hydroxybutyrate prediction, which contained observations from 2,701 multibreed cows pertaining to 33 herds. The best predictive model was developed using an automatic ML algorithm that tested various methods, including elastic net, distributed random forest, gradient boosting machine, artificial neural network, and stacking ensemble. These ML predictions were compared with partial least squares regression, the most commonly used method for FTIR prediction of blood traits. Performance of each model was evaluated using 2 cross-validation (CV) scenarios: 5-fold random (CV_r) and herd-out (CV_h). We also tested the best model's ability to classify values precisely in the 2 extreme tails, namely, the 25th (Q25) and 75th (Q75) percentiles (true-positive prediction scenario). Compared with partial least squares regression, ML algorithms achieved more accurate performance. Specifically, elastic net increased the R² value from 5% to 75% for CV_r and 2% to 139% for CV_h, whereas the stacking ensemble increased the R² value from 4% to 70% for CV_r and 4% to 150% for CV_h. Considering the best model, with the CV_r scenario, good prediction accuracies were obtained for glucose (R² = 0.81), urea (R² = 0.73), albumin (R² = 0.75), total reactive oxygen metabolites (R² = 0.79), total thiol groups (R² = 0.76), ceruloplasmin (R² = 0.74), total proteins (R² = 0.81), globulins (R² = 0.87), and Na (R² = 0.72). Good prediction accuracy in classifying extreme values was achieved for glucose (Q25 = 70.8%, Q75 = 69.9%), albumin (Q25 = 72.3%), total reactive oxygen metabolites (Q25 = 75.1%, Q75 = 74%), thiol groups (Q75 = 70.4%), total proteins (Q25 = 72.4%, Q75 = 77.2.%), globulins (Q25 = 74.8%, Q75 = 81.5%), and haptoglobin (Q75 = 74.4%). In conclusion, our study shows that FTIR spectra can be used to predict blood metabolites with relatively good accuracy, depending on trait, and are a promising tool for large-scale monitoring.