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Unveiling the Predictive Capabilities of Machine Learning in Air Quality Data Analysis: A Comparative Evaluation of Different Regression Models
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作者 Mosammat Mustari Khanaum Md Saidul Borhan +2 位作者 Farzana Ferdoush Mohammed Ali Nause Russel Mustafa Murshed 《Open Journal of Air Pollution》 2023年第4期142-159,共18页
Air quality is a critical concern for public health and environmental regulation. The Air Quality Index (AQI), a widely adopted index by the US Environmental Protection Agency (EPA), serves as a crucial metric for rep... Air quality is a critical concern for public health and environmental regulation. The Air Quality Index (AQI), a widely adopted index by the US Environmental Protection Agency (EPA), serves as a crucial metric for reporting site-specific air pollution levels. Accurately predicting air quality, as measured by the AQI, is essential for effective air pollution management. In this study, we aim to identify the most reliable regression model among linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), logistic regression, and K-nearest neighbors (KNN). We conducted four different regression analyses using a machine learning approach to determine the model with the best performance. By employing the confusion matrix and error percentages, we selected the best-performing model, which yielded prediction error rates of 22%, 23%, 20%, and 27%, respectively, for LDA, QDA, logistic regression, and KNN models. The logistic regression model outperformed the other three statistical models in predicting AQI. Understanding these models' performance can help address an existing gap in air quality research and contribute to the integration of regression techniques in AQI studies, ultimately benefiting stakeholders like environmental regulators, healthcare professionals, urban planners, and researchers. 展开更多
关键词 Regression Analysis air Quality Index Linear Discriminant Analysis Quadratic Discriminant Analysis Logistic Regression K-Nearest Neighbors machine Learning Big Data Analysis
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Micro-Locational Fine Dust Prediction Utilizing Machine Learning and Deep Learning Models
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作者 Seoyun Kim Hyerim Yu +1 位作者 Jeewoo Yoon Eunil Park 《Computer Systems Science & Engineering》 2024年第2期413-429,共17页
Given the increasing number of countries reporting degraded air quality,effective air quality monitoring has become a critical issue in today’s world.However,the current air quality observatory systems are often proh... Given the increasing number of countries reporting degraded air quality,effective air quality monitoring has become a critical issue in today’s world.However,the current air quality observatory systems are often prohibitively expensive,resulting in a lack of observatories in many regions within a country.Consequently,a significant problem arises where not every region receives the same level of air quality information.This disparity occurs because some locations have to rely on information from observatories located far away from their regions,even if they may be the closest available options.To address this challenge,a novel approach that leverages machine learning and deep learning techniques to forecast fine dust concentrations was proposed.Specifically,continuous location features in the form of latitude and longitude values were incorporated into our models.By utilizing a comprehensive dataset comprising weather conditions,air quality measurements,and location properties,various machine learning models,including Random Forest Regression,XGBoost Regression,AdaBoost Regression,and a deep learning model known as Long Short-Term Memory(LSTM)were trained.Our experimental results demonstrated that the LSTM model outperforms the other models,achieving the best score with a root mean squared error of 23.48 in predicting fine dust(PM10)concentrations on an hourly basis.Furthermore,the fact that incorporating location properties,such as longitude and latitude values,enhances the overall quality of the regression models was discovered.Additionally,the implications and contributions of our research were discussed.By implementing our approach,the cost associated with relying solely on existing observatories can be substantially reduced.This reduction in costs can pave the way for economically efficient fine dust observation systems,ensuring more widespread and accurate air quality monitoring across different regions. 展开更多
关键词 Fine dust PM_(10) air quality prediction machine learning LSTM
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Applying machine learning for cars’semi-active air suspension under soft and rigid roads 被引量:1
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作者 Xu Shaoyong Zhang Jianrun Nguyen Van Liem 《Journal of Southeast University(English Edition)》 EI CAS 2022年第3期300-308,共9页
To improve the ride quality and enhance the control efficiency of cars’semi-active air suspensions(SASs)under various surfaces of soft and rigid roads,a machine learning(ML)method is proposed based on the optimized r... To improve the ride quality and enhance the control efficiency of cars’semi-active air suspensions(SASs)under various surfaces of soft and rigid roads,a machine learning(ML)method is proposed based on the optimized rules of the fuzzy control(FC)method and car dynamic model for application in SASs.The root-mean-square(RMS)acceleration of the driver’s seat and car’s pitch angle are chosen as the objective functions.The results indicate that a soft surface obviously influences a car’s ride quality,particularly when it is traveling at a high-velocity range of over 72 km/h.Using the ML method,the car’s ride quality is improved as compared to those of FC and without control under different simulation conditions.In particular,compared with those cars without control,the RMS acceleration of the driver’s seat and car’s pitch angle using the ML method are respectively reduced by 30.20% and 19.95% on the soft road and 34.36% and 21.66% on the rigid road.In addition,to optimize the ML efficiency,its learning data need to be updated under all various operating conditions of cars. 展开更多
关键词 semi-active air suspension ride quality machine learning fuzzy control genetic algorithm
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Entropy Assessment on Environmental Influence of Condense Heat in Recovery System in Air-Conditioning Refrigerating Machine 被引量:2
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作者 TANG Wen-wu1,2,WANG Han-qing1,2(1.School of Energy Science and Engineering,Central South University,Changsha,Hunan 410083,China 2.School of Civil Engineering,Hunan University of Technology,Zhuzhou,Hunan 412008,China) 《湖南大学学报(自然科学版)》 EI CAS CSCD 北大核心 2009年第S1期96-101,共6页
This paper presented an entropy evaluation method for the influences of condense heat recovery system on the environment.Aiming at the damage of the condense heat to the environment,an entropy of resource loss and an ... This paper presented an entropy evaluation method for the influences of condense heat recovery system on the environment.Aiming at the damage of the condense heat to the environment,an entropy of resource loss and an emission entropy from the condense heat recovery system in the air conditioning refrigerating machine were introduced.For the evaluation of the entropies,we developed a new algorithm for the parameter identification,called the composite influence coefficient,based on the Least Squares Support Vector Machine method.By simulation,the numerical experiments shows that the Least Squares Support Vector Machine method is one of the powerful methods for the parameter identification to compute the damage entropy of the condense heat,with the largest training error being-0.025(the relative error being-3.56%),and the biggest test error being 0.015(the relative error being 2.5%). 展开更多
关键词 air-CONDITIONING refrigerating machinE condense HEAT ENTROPY evaluation LS-SVM
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Effects of machining conditions on specific surface of PM<sub>2.5</sub>emitted during metal cutting
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作者 Abdelhakim Djebara Victor Songmene Ali Bahloul 《Health》 2013年第10期36-43,共8页
Indoor air quality has become an important matter for health and safety. Most manufacturing processes generate aerosols. In the metal cutting industry, dry machining processes are accompanied by dust emission (fogs, f... Indoor air quality has become an important matter for health and safety. Most manufacturing processes generate aerosols. In the metal cutting industry, dry machining processes are accompanied by dust emission (fogs, fine chips and metallic dust in both micrometers and nanometers scales) that has impacts on workers’ health. This research work aimed to understand and reduce the harmful impacts of the machining process on the occupational safety. In this study, an experimental investigation was carried out on fine and ultrafine metallic dust emission during slot milling of 2024-T351, 6061-T6 and 7075-T6 aluminum alloy in dry conditions. It was confirmed that the cutting conditions influence significantly the specific surface area of ultrafine particles. It was also found that the cutting speed is a determinant factor for specific surface area of ultrafine particles and control during the slot milling process. 展开更多
关键词 Aluminum ALLOYS air Quality DUST Emission DRY machining PM2.5 Specific Surface
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Monitoring and Prediction of Indoor Air Quality for Enhanced Occupational Health
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作者 Adela POP(Puscasiu) Alexandra Fanca +1 位作者 Dan Ioan Gota Honoriu Valean 《Intelligent Automation & Soft Computing》 SCIE 2023年第1期925-940,共16页
The amount of moisture in the air is represented by relative humidity(RH);an ideal level of humidity in the interior environment is between 40%and 60%at temperatures between 18°and 20°Celsius.When the RH fal... The amount of moisture in the air is represented by relative humidity(RH);an ideal level of humidity in the interior environment is between 40%and 60%at temperatures between 18°and 20°Celsius.When the RH falls below this level,the environment becomes dry,which can cause skin dryness,irritation,and discomfort at low temperatures.When the humidity level rises above 60%,a wet atmosphere develops,which encourages the growth of mold and mites.Asthma and allergy symptoms may occur as a result.Human health is harmed by excessive humidity or a lack thereof.Dehumidifiers can be used to provide an optimal level of humidity and a stable and pleasant atmosphere;certain models disinfect and purify the water,reducing the spread of bacteria.The design and implementation of a client-server indoor and outdoor air quality monitoring application are presented in this paper.The Netatmo station was used to acquire the data needed in the application.The client is an Android application that allows the user to monitor air quality over a period of their choosing.For a good monitoring process,the Netatmo modules were used to collect data from both environments(indoor:temperature(T),RH,carbon dioxide(CO_(2)),atmospheric pressure(Pa),noise and outdoor:T and RH).The data is stored in a database,using MySQL.The Android application allows the user to view the evolution of the measured parameters in the form of graphs.Also,the paper presents a prediction model of RH using Azure Machine Learning Studio(Azure ML Studio).The model is evaluated using metrics:Mean Absolute Error(MAE),Root Mean Squared Error(RMSE),Relative Absolute Error(RAE),Relative Squared Error(RSE)and Coefficient of Determination(CoD). 展开更多
关键词 machine learning indoor air quality humidity carbon dioxide relative humidity
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Minimization of Air Consumption and Potential Savings of Textile Denim Fabric Manufacturing Process
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作者 Md. Enamul Haque Md. Bokthier Rahman +2 位作者 Waliul Kafi Md. Suja Uddin Kaiser Abhijit Dey 《Journal of Textile Science and Technology》 2023年第1期69-83,共15页
One of the most important aspects of Bangladesh’s textile industry is denim. Bangladesh now has a new opportunity thanks to the global demand for denim among fashion industry professionals. Entrepreneurs from Banglad... One of the most important aspects of Bangladesh’s textile industry is denim. Bangladesh now has a new opportunity thanks to the global demand for denim among fashion industry professionals. Entrepreneurs from Bangladesh provide denim products to well-known international merchants all over the world. The worldwide denim market is predicted to expand by roughly 8% through the year 2020. We must raise the standard of denim if we are to keep up with the expanding industry. In contrast to projectile and rapier systems, air-jet weaving machines nowadays can weave practically all types of yarns without any issues and at higher rates. Due to this, air-jet looms are an excellent substitute for other weft insertion techniques. This kind of device still has one significant flaw, though, and that is the enormous power consumption brought on by the creation of compressed air. Researchers and manufacturers of air-jet looms have therefore worked very hard to find a solution to this issue and achieve a huge reduction in air consumption without compromising loom performance or fabric quality. Therefore, the purpose of this project is to look into ways to decrease air consumption and reduce auxiliary selvedge waste without any decrease in loom performance and fabric quality on existing air-jet weaving looms which reduce the manufacturing costs with process improvement. Just updating the air pressure allowed a weaving mill to reduce air usage by 11 cfm. So, with just almost no cost, a company with 100 looms could save $0.15 M each year, on compressed air. Two new methods for decreasing process costs on air jet looms have also been developed by this project work. 展开更多
关键词 DENIM Woven Textiles Weaving machine air Consumption Cost Reduction Waste Reduction Potential Savings
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Minimizing Air Entrainment in the Shot Sleeve during Injection Stage of the HPDC Machine
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作者 Korti Abdel Illah Nabil Korti Mohamed Choukri Abboudi Said 《材料科学与工程(中英文A版)》 2013年第3期153-161,共9页
关键词 注射过程 压铸机 拍摄 空气 液态金属流 夹带 三维数值模型 动量守恒方程
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基于机器学习方法的空气质量预测与影响因素识别 被引量:2
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作者 李佳成 梁龙跃 《计算机技术与发展》 2024年第1期164-170,共7页
空气质量指数(AQI)的精准预测及影响因素识别,对空气污染防护和治理具有重要现实意义。选取北京市2014年第一季度至2022年第二季度AQI作为研究对象,探究六大污染物、五个气象因子和十四个经济变量对空气质量影响。选用DT,RF,GBDT和XGBo... 空气质量指数(AQI)的精准预测及影响因素识别,对空气污染防护和治理具有重要现实意义。选取北京市2014年第一季度至2022年第二季度AQI作为研究对象,探究六大污染物、五个气象因子和十四个经济变量对空气质量影响。选用DT,RF,GBDT和XGBoost模型对AQI进行预测,并使用稳定性选择方法定量分析各个变量对AQI的贡献。结果表明:四种模型方法均有良好的预测效果,其中XGBoost和RF的预测效果最优;六大污染物中PM2.5,PM10浓度和气象因素中的风速和气压对AQI影响较大;十四个经济变量对AQI的影响差异较大,其中城镇居民人均可支配收入、第三产业GDP和规模以上工业总产值等对AQI影响较大,而第一产业GDP和公路货物运输量等影响较小。 展开更多
关键词 空气质量 影响因素 定量分析 机器学习 稳定性选择
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数据驱动的重污染天气应急限产方案智能决策
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作者 陈富赞 焦扬 +1 位作者 李敏强 田津 《系统工程学报》 CSCD 北大核心 2024年第1期1-15,共15页
对工业排放源企业限产是重污染天气应急响应的有效手段.现行限产模式并未考虑扩散条件对限产效果的影响及大气环境治理与经济发展的矛盾.面向特定重污染天气预警,开发数据驱动最优限产方案智能决策方法(DID),用极限学习机预测特定扩散... 对工业排放源企业限产是重污染天气应急响应的有效手段.现行限产模式并未考虑扩散条件对限产效果的影响及大气环境治理与经济发展的矛盾.面向特定重污染天气预警,开发数据驱动最优限产方案智能决策方法(DID),用极限学习机预测特定扩散条件下限产方案的减排效果,并将决策者的空气质量改善目标及经济效益目标作为约束对限产方案进行优化.真实数据集上的实验结果表明,在达到同样空气质量目标时,DID方案的经济效益和平均生产比例均高于现行预案;在实现相同经济产值时,DID方案的空气质量改善程度也显著高于现行预案.DID方法能够提高重污染天气应对的精准性及科学性,管理者可以根据当前工作重心设置不同的大气治理及经济发展目标,实现生态环境与区域经济的协调发展.研究结论可为各级政府提高重污染天气应急响应水平提供理论与方法支持,具有一定推广及应用价值. 展开更多
关键词 决策支持 大气污染 减排 极限学习机 优化
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三种机器学习模型用于空气质量等级预测的比较研究——以保定市为例
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作者 刘婕 郝舒欣 +2 位作者 万红燕 刘悦 徐东群 《环境卫生学杂志》 2024年第3期264-269,272,共7页
目的 利用支持向量机(support vector machine, SVM)、随机森林(random forest, RF)和多层感知器(multilayer perceptron, MLP)三种机器学习方法分别构建保定市未来三日空气质量等级预测模型,通过对参数调优和预测结果比较选择三种模型... 目的 利用支持向量机(support vector machine, SVM)、随机森林(random forest, RF)和多层感知器(multilayer perceptron, MLP)三种机器学习方法分别构建保定市未来三日空气质量等级预测模型,通过对参数调优和预测结果比较选择三种模型中的最佳模型。方法 基于保定市2014—2022年的空气污染物日均浓度监测数据和同期气象数据,采用SVM、RF和MLP三种机器学习模型,利用前四日数据为未来三日分别构建了每日的空气质量等级预测模型并评估特征变量的重要性。对模型参数进行调优,采取十折交叉验证法进行验证,通过准确率和AUC等指标来评估模型性能。结果 SVM模型未来三日准确率分别为69.8%、63.5%、62.3%,AUC分别为77.4、70.8、70.7;RF模型未来三日准确率分别为75.9%、68.2%、67.1%,AUC分别为0.84、0.74、0.72;MLP模型未来三日准确率分别为73.2%、66.4%、65.7%,AUC为0.83、0.74、0.73,综合对比RF模型表现最优;空气质量特征变量重要性高于气象因素特征变量。结论 通过对比研究,RF机器学习模型能够相对有效地预测未来一日空气污染等级,并提供空气质量等级预警。 展开更多
关键词 机器学习 空气污染 支持向量机 随机森林 多层感知器
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基于神经网络的自动空气制动系统仿真研究
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作者 成庶 周昕怡 +2 位作者 于天剑 林磊 王佳 《中南大学学报(自然科学版)》 EI CAS CSCD 北大核心 2024年第4期1591-1601,共11页
通过分析空气制动试验数据研究10 000 t重载列车空气制动系统的空气传递特性,提取影响空气制动系统关键部件(列车管、副风缸和制动缸)的神经网络特征,建立一种基于神经网络的自动空气制动系统仿真模型。将模型作为制动激励输入纵向动力... 通过分析空气制动试验数据研究10 000 t重载列车空气制动系统的空气传递特性,提取影响空气制动系统关键部件(列车管、副风缸和制动缸)的神经网络特征,建立一种基于神经网络的自动空气制动系统仿真模型。将模型作为制动激励输入纵向动力学模型,并将纵向动力学模型的预测结果与浩吉西峡东站—襄州北站铁路10 000 t重载列车的实际运行结果进行对比验证。研究结果表明:10 000 t重载列车的制动和缓解信号从机车向远端车辆传递,随着传递距离增加,传递速度几乎不变,但传递强度有所衰减;基于神经网络的制动系统仿真模型能预测10 000 t重载列车常用制动减压50 kPa工况的列车管、副风缸和制动缸风压变化,预测精度高达99.9%,在相同计算步长下,计算效率较传统的流体力学仿真模型提升了2 938倍,具有广阔的工程应用前景。 展开更多
关键词 重载列车 制动系统 机器学习 神经网络 数值仿真
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塑性磨料气射流仿真与试验研究
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作者 易茜 赵洋洋 王燕萍 《塑料工业》 CAS CSCD 北大核心 2024年第5期169-174,共6页
塑性磨料气射流加工(PAJM)是一种先进的减材加工技术,是采用热固性塑性磨料代替传统硬磨料而发展的新型技术,该技术可有效去除表面涂层,且不损伤基材。基材无损主要是通过控制塑性磨料对基材的冲蚀应力,使得塑性磨料的冲蚀应力小于基材... 塑性磨料气射流加工(PAJM)是一种先进的减材加工技术,是采用热固性塑性磨料代替传统硬磨料而发展的新型技术,该技术可有效去除表面涂层,且不损伤基材。基材无损主要是通过控制塑性磨料对基材的冲蚀应力,使得塑性磨料的冲蚀应力小于基材纤维的极限强度或纤维与树脂的结合强度。本研究采用有限元仿真和试验相结合的方法,对颗粒速度进行理论分析、计算流体动力学仿真模拟和试验研究,研究不同气体压力下的颗粒速度,计算结果与试验数据吻合较好。结果表明,随着磨料颗粒离开喷嘴,在距离喷嘴出口6.2 dN内(dN为喷嘴内径),颗粒速度增加;相反,距离喷嘴出口6.2 dN外,颗粒速度逐渐减小。当磨料粒径由20~30目变为40~50目时,最大颗粒速度由164.365 m/s增加到228.402 m/s。随着磨料粒径的减小,颗粒速度增加,且发散角增加。相比而言,数值模型能更好的预测塑性磨料的颗粒速度和分布。该研究突出了控制颗粒粒径和支座距离对射流场颗粒速度和发散角的影响。为控制颗粒对基材的冲蚀应力,避免基材损伤提供理论参考。 展开更多
关键词 气射流加工 塑性磨料 颗粒速度 有限元仿真 数值模拟
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基于气隙磁密差信号峭度因子的永磁同步电机局部退磁故障诊断
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作者 丁石川 吴振兴 +2 位作者 李亚 杭俊 何旺 《中国电机工程学报》 EI CSCD 北大核心 2024年第14期5747-5755,I0026,共10页
该文提出一种新型基于气隙磁密信号差峭度因子的永磁同步电机局部退磁故障诊断方法。首先,建立永磁同步电机等效磁路模型,根据简化模型分析局部退磁故障后各极主磁路下径向气隙磁密的变化规律。然后,将预存健康电机空载状态下气隙磁密... 该文提出一种新型基于气隙磁密信号差峭度因子的永磁同步电机局部退磁故障诊断方法。首先,建立永磁同步电机等效磁路模型,根据简化模型分析局部退磁故障后各极主磁路下径向气隙磁密的变化规律。然后,将预存健康电机空载状态下气隙磁密信号与测量的在线状态下气隙磁密信号作差并计算其峭度值,作为故障特征值。取健康电机空载状态下气隙磁密信号与不同工况下气隙磁密信号差峭度值的最大值作为参考值,根据故障特征值与参考值的大小诊断出局部退磁。最后,仿真和实验结果均验证提出的局部退磁故障诊断方法的有效性。 展开更多
关键词 永磁同步电机(PMSM) 气隙磁密 等效磁路 峭度因子 局部退磁故障
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喷气涡流纺纱机的发展现状及趋势
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作者 丁文胜 李姝佳 +3 位作者 赵婷婷 景慎全 叶晋浦 程隆棣 《棉纺织技术》 CAS 2024年第9期90-93,共4页
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... 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。 展开更多
关键词 喷气涡流纺纱机 花式纱 包芯纱 细号纱 差异化原料
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基于DBN和BES-LSSVM的矿用压风机异常状态识别方法
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作者 李敬兆 王克定 +2 位作者 王国锋 郑鑫 石晴 《流体机械》 CSCD 北大核心 2024年第3期89-97,共9页
针对矿用压风机这类分布式系统的异常类别复杂、识别精度低等问题,提出了一种基于深度置信网络(DBN)和最小二乘支持向量机(LSSVM)的异常状态识别方法。首先,分析压风机组成系统及其运行机理,确定常见的异常状态类型;其次,采用DBN无监督... 针对矿用压风机这类分布式系统的异常类别复杂、识别精度低等问题,提出了一种基于深度置信网络(DBN)和最小二乘支持向量机(LSSVM)的异常状态识别方法。首先,分析压风机组成系统及其运行机理,确定常见的异常状态类型;其次,采用DBN无监督学习方式充分挖掘监测数据中异常特征并快速提取;然后,利用秃鹰搜索算法(BES)优化LSSVM的超参数,构建最优的BES-LSSVM分类模型;最后,将DBN提取的异常特征作为BES-LSSVM模型的输入,对矿用压风机异常状态进行识别。试验验证与对比分析结果表明,相较于GA,PSO,GWO算法,BES算法的求解精度和收敛速度均有所提高,同时DBN-BES-LSSVM模型在测试集上平均识别精度达到94.65%,较PCA-LSSVM模型、DBN模型和DBN-LSSVM模型的识别精度分别提高了10.53%,5.84%和3.76%,验证了DBN-BES-LSSVM模型在矿用压风机异常特征提取以及特征识别方面的优越性。 展开更多
关键词 矿用压风机 深度置信网络 秃鹰搜索算法 最小二乘支持向量机 异常识别
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基于机器视觉的小白杏热风干燥控制系统设计
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作者 杨嘉鹏 王帅帅 +1 位作者 黄卉 刘子绰 《农业工程》 2024年第3期91-96,共6页
设计了一种基于机器视觉的小白杏热风干燥控制系统,该系统使用计算机视觉技术,对小白杏进行检测和分类,并自动控制热风干燥过程中的温度、风速。系统采用基于神经网络的目标检测算法,对小白杏进行检测和分割,利用图像处理技术提取小白... 设计了一种基于机器视觉的小白杏热风干燥控制系统,该系统使用计算机视觉技术,对小白杏进行检测和分类,并自动控制热风干燥过程中的温度、风速。系统采用基于神经网络的目标检测算法,对小白杏进行检测和分割,利用图像处理技术提取小白杏的特征,并使用YOLOv7对小白杏进行分类。通过调整热风干燥的温度、风速和时间等参数,实现了对小白杏干燥过程中的品质保护,可保持小白杏品质和口感,提升杏干经济价值,该系统具有一定的应用前景。 展开更多
关键词 机器视觉 热风干燥 小白杏 目标检测 YOLOv7
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关于紫外线循环风空气消毒机医院感染管理策略的探讨
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作者 朱玉静 《中国卫生标准管理》 2024年第15期161-164,共4页
目的探讨紫外线循环风空气消毒机在医院中的应用与效果。方法选取日照市中医医院内2020年1月—2023年6月3间面积、布局与设施完全一样的诊室作为空气消毒试验场地,分别采用移动紫外线消毒车、臭氧空气消毒机与紫外线循环风空气消毒机进... 目的探讨紫外线循环风空气消毒机在医院中的应用与效果。方法选取日照市中医医院内2020年1月—2023年6月3间面积、布局与设施完全一样的诊室作为空气消毒试验场地,分别采用移动紫外线消毒车、臭氧空气消毒机与紫外线循环风空气消毒机进行杀菌消毒处理,对上述2种消毒方式的消杀结果展开比较。结果消毒前,3间诊室细菌菌落数比较,差异无统计学意义(P>0.05);消毒后,甲、乙、丙3间诊室的细菌菌落数分别为(153.43±31.42)cfu/m^(3)、(120.43±19.34)cfu/m^(3)、(80.52±14.24)cfu/m^(3),相比消毒前更低(P<0.05);同时,消毒后,丙诊室细菌菌落数低于甲诊室、乙诊室(P<0.05);消毒1、2 h后,甲、乙、丙3间诊室细菌菌落数均显著高于本组消毒0.5 h后,且甲诊室、乙诊室消毒1、2 h后细菌菌落数显著高于丙诊室同时间点细菌菌落数,两两比较,差异有统计学意义(P<0.05)。结论同传统紫外线灯与臭氧空气消毒机相比,紫外线循环风空气消毒机有着更好地消毒效果,且安全性较好,操作简单。 展开更多
关键词 紫外线 空气消毒机 医院感染 臭氧空气消毒机 细菌菌落数 消毒车
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造纸机干燥部排风系统节能优化设计研究
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作者 王毅哲 王禹程 《造纸科学与技术》 2024年第4期42-44,共3页
为实现针对造纸机干燥部排风系统的节能优化,降低造纸企业的生产经营成本,收集了某造纸企业提供的干燥部信息管理系统数据并建立了服务于排风系统节能优化的弹性网络模型。通过该模型对干燥部排风系统内各个排风机的排风温湿度进行预测... 为实现针对造纸机干燥部排风系统的节能优化,降低造纸企业的生产经营成本,收集了某造纸企业提供的干燥部信息管理系统数据并建立了服务于排风系统节能优化的弹性网络模型。通过该模型对干燥部排风系统内各个排风机的排风温湿度进行预测,发现弹性网络模型对排风机排风温湿度的预测效果显著优于支持向量回归模型。因此,基于弹性网络的节能优化模型应用于110 g/m^(2)红杉纸、90 g/m^(2)挂面箱板纸、120 g/m^(2)挂面箱板纸等3条产品线的排风系统参数调节工作中。根据生产应用结果发现,3条纸张产品生产线经过参数调节后的吨绝干纸干燥成本依次下降了0.72%、0.68%、1.07%,体现出了较为理想的节能优化效果。 展开更多
关键词 造纸机 排风温湿度 弹性网络模型 干燥成本
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气浮空气循环机密封轴向气动力研究
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作者 肖云峰 邓瑞洋 +2 位作者 孔祥雨 张樱瀚 孙鑫悦 《润滑与密封》 CAS CSCD 北大核心 2024年第6期43-49,共7页
针对传统空气循环机双侧高速转子端的轴向力理论经验公式精度偏低,数值计算耗时长的问题,建立包含双侧高速转子的窄缝间隙计算域几何模型,并通过试验校核了模型的准确性。对含双侧高速转子的窄缝间隙内部流场进行数值计算,探讨窄缝间隙... 针对传统空气循环机双侧高速转子端的轴向力理论经验公式精度偏低,数值计算耗时长的问题,建立包含双侧高速转子的窄缝间隙计算域几何模型,并通过试验校核了模型的准确性。对含双侧高速转子的窄缝间隙内部流场进行数值计算,探讨窄缝间隙对轴向气动力的作用规律,并对传统机械轴向气动力的计算公式进行修正,提出适用于气浮ACM轴向气动力计算公式。结果表明:提出的公式在压气机端的最小误差由原来的59%降低到13%,最大误差由原来的261%降低到75%,在涡轮端最小误差由原来的63%降低到2%,最大误差由原来的98%降低到8%,表明修正后轴向气动力公式减小了轴向力的计算误差,因此提出的公式更加适用于气浮空气循环机的轴向气动力计算。 展开更多
关键词 空气循环机 轴向力 窄缝间隙 轮背密封 动压箔片轴承
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