检索结果-南通市图书馆

Preparation of Core/Shell Electrically Conductive Fibers by Efficient Coating Carbon Nanotubes on Polyester

在线全文

学校读者我要写书评

暂无评论

Advanced Fiber materials 2021年第3期3卷 180-191页

作者： liangyu ma Yi Nie Yanrong Liu Feng Huo Lu Bai Qian Li Suojiang Zhang College of Chemistry and Chemical Engineering Henan UniversityHenan 475000China Beijing Key Laboratory of Ionic Liquids Clean Process CAS Key Laboratory of Green Process and EngineeringState Key Laboratory of Multiphase Complex SystemsInstitute of Process EngineeringChinese Academy of SciencesBeijing 100190China Zhengzhou Institute of Emerging Industrial Technology Henan 450040China

Conducting fibers with improved properties and functionalities are needed for diverse applications.Here we report the fabrica-tion of core/shell conductive Dacron fibers by dip-coating method through originating from multi-walled carbon nanotubes(MWCNTs)coated on polyester fibers.The annealing process was conducted to enhance interaction between the conductive shell and polyester core as well as within the MWCNTs network.The properties of two kinds of MWCNTs dispersions and the electrical properties of conductive fibers were studied,respectively.The results show that both MWCNTs-polyurethane resin(MWCNTs-WPU)dispersion and MWCNTs-acrylic resin(MWCNTs-PAA)dispersion present a typical characteristic of pseudo-plastic fluid and an excellent wetting ability to polyester fibers.The ultimate tensile stress and elongation at break for the MWCNTs-PAA coated fiber are 261 MPa and 25.43%.The ultimate tensile stress and the elongation at break are both increasing with the increasing of MWCNTs contents,due to the strong interface bonding ability between the conduc-tive shell and polyester core and strengthen the MWCNTs network.The electrical resistance of the obtained fibers can be controlled in the range from 732 to 30Ω/cm by changing MWCNTs content,dipping times and annealing temperature.It was found that it is able to light a LED.All results suggest that the conductive fibers embody a good synergy effect of carbon nanotubes and polymers.Therefore,the fabricated conductive fibers have a widely prospect for being applied in the field of flexible electronics.

关键词： MWCNTs dispersion Conductive polyester fiber Electrical resistance Interface

Recent advances of MXene as promising catalysts for electrochemical nitrogen reduction reaction

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Chinese Chemical Letters 2020年第4期31卷 953-960页

作者： Jie Sun Wenhan Kong Zhaoyong Jin Yaqian Han liangyu ma Xiaoteng Ding Yusheng Niu Yuanhong Xu College of materials Science and Engineering College of Life SciencesQingdao UniversityQingdao 266071China

Electrochemical reduction of N2,as an eco-friendly alternative,not only allows the use of protons in water as a source of hydrogen under mild conditions but also can be driven by renewable electric energy.The major challenge is to identify high-efficiency electrocatalysts.MXene is a new class of 2D transition metal carbides,nitrides,and carbonitrides that have received significant attention in electrocatalysis.The investigations on MXene in electrocatalytic nitrogen fixation are rapidly proceeding,and some breakthroughs have emerged ve ry recently due to MXenes’satisfacto ry catalytic activity.Here,the recent progress concerning the MXene-based catalysts for electrochemical N2 reduction reaction(NRR)is highlighted.In regards to giving guidelines for exploring more efficient MXene-based catalysts for the NRR,the fabrication and surface modification of MXene are discussed.Besides,the shortcomings and challenges of current research are summarized and the future research directions are prospected.

关键词： Nitrogen reduction reaction Electrocatalysis MXene Nanomaterials Two-dimensional nanomaterials

用SSA优化CNN-LSTM-SEnet预测模型实现风电机组故障预警

在线全文

学校读者我要写书评

暂无评论

电力科学与工程 2024年第6期40卷 1-10页

作者：马良玉吕若萌华北电力大学自动化系河北保定071003

风电机组数据采集与监控系统的原始高维数据存在大量异常点和噪声点,且故障预警对性能预测模型的精度要求很高。为此,建立了一种基于混合神经网络预测模型的风电机组故障预警方法。为获取高质量的建模数据,采用快速密度峰值聚类和孤立... 详细信息

风电机组数据采集与监控系统的原始高维数据存在大量异常点和噪声点,且故障预警对性能预测模型的精度要求很高。为此,建立了一种基于混合神经网络预测模型的风电机组故障预警方法。为获取高质量的建模数据,采用快速密度峰值聚类和孤立森林算法对原始数据进行多步清洗。利用麻雀搜索算法优化的卷积神经网络–长短期记忆网络–压缩激励网络混合模型,建立了能够有效提取潜在特征信息、高精度的风机正常工况性能预测模型。为实现故障可靠预警、降低误报率,通过滑动窗口法构建预警指标并结合核密度估计法计算其阈值。采用真实故障历史数据进行实验,验证了方法的有效性。

关键词：故障预警快速密度峰值聚类孤立森林麻雀搜索算法混合神经网络风电机组

基于LightGBM-VIF-MIC-SFS的风电机组故障诊断输入特征选择方法

在线全文

学校读者我要写书评

暂无评论

热力发电 2024年第1期53卷 154-164页

作者：马良玉程东炎梁书源耿妍竹段新会华北电力大学自动化系河北保定071003 保定华仿科技股份有限公司河北保定071000

针对风电机组数据采集与监视控制(SCADA)系统数据维数较高、特征冗余、特征相关性高导致风电机组的故障诊断过程存在误差大、分类正确率低的问题,提出一种基于LightGBM-VIF-MIC-SFS的三段式特征选择方法。首先,根据LightGBM实现对所有... 详细信息

针对风电机组数据采集与监视控制(SCADA)系统数据维数较高、特征冗余、特征相关性高导致风电机组的故障诊断过程存在误差大、分类正确率低的问题,提出一种基于LightGBM-VIF-MIC-SFS的三段式特征选择方法。首先,根据LightGBM实现对所有特征的重要性计算,确定初步特征空间;其次,根据方差膨胀因子(VIF)和最大信息系数(MIC)构建相关性判别阵,据此评估一次筛选中重要性相近的特征,舍弃相似性高的输入特征;最后,使用序列前向搜索法对特征进行第3次处理,逐个输入前2次特征选择获得的特征,保留能提升系统性能的特征,从而实现最终特征的选取。在完成了模型的建立后,使用风电场真实SCADA系统数据进行性能评估,将所提方法与2种对比算法在6个数据集上进行对比,结果显示所提出的LightGBM-VIF-MIC-SFS相较2种对比特征选择算法有显著优势。对所提方法内部的3个模块进行了消融实验,有效验证了所提特征选取方法内部各个模块的有效性以及基于所提方法得到的最优特征空间的合理性及准确性。

关键词：风电机组特征选择 LightGBM 方差膨胀因子最大信息系数序列前向搜索

生命体征智能采录系统在监护患者护理中的应用研究

在线全文

学校读者我要写书评

暂无评论

中国医疗设备 2024年第6期39卷 68-74页

作者：李民马良玉陈宇娟陈勇王丽华南通市第一人民医院信息科江苏南通226000 南通市第一人民医院设备科江苏南通226000 南通市第一人民医院护理部江苏南通226000

目的探索生命体征智能采录系统在监护患者护理中的应用效果,用以解决原有手工抄录生命体征数据带来的效率低下、误差率高问题,推进全院一体化生命体征数据监测中心的建立。方法利用设备集成模块,将各类生命体征测量设备统一接入中心监... 详细信息

目的探索生命体征智能采录系统在监护患者护理中的应用效果,用以解决原有手工抄录生命体征数据带来的效率低下、误差率高问题,推进全院一体化生命体征数据监测中心的建立。方法利用设备集成模块,将各类生命体征测量设备统一接入中心监护系统,中心监护系统将接收到的生命体征数据进行统一化、标准化和智能化处理,再通过数据网关转发数据至医院各业务系统,完成生命体征智能采录系统的设计,实现医疗设备应用过程中,采集数据的自动化传输、智能化存储和标准化应用。选取5个普通病房和5个重症监护室的生命体征数据采集时间及采录准确性、护理人员满意度作为评价指标对生命体征智能采录系统的效果进行分析。结果生命体征智能采录系统应用后,普通病房及重症病房各科室的生命体征数据采录时间均较系统应用前显著降低,每天平均节省时间分别为14.14~72.59 h、17.00~81.70 h;数据采录准确性和护理人员满意度均高于系统应用前,且差异均有统计学意义(P<0.05)。结论生命体征智能采录系统的应用,实现了生命体征测量设备互联、数据互通、生命体征数据采集流程的数字化,提升了护理安全和护理质量,可帮助医生动态监测患者病情变化并作出准确诊断。

关键词：生命体征智能采录系统监护患者护理生命体征数据数字化采集智能化存储

Inverse Control for the Coordination System of Supercritical Power Unit Based on Dynamic Fuzzy Neural Network Modeling

在线全文

学校读者我要写书评

暂无评论

Inverse Control for the Coordination System of Supercritical...

Intelligent Compensation for the Set Values of PID Controllers to Improve Boiler Superheated Steam Temperature Control

第三十九届中国控制会议

作者： liangyu ma Jiayi Zheng School of Control and Computer Engineering North China Electric Power University

In order to improve the coordinated control quality of a supercritical power unit, a coordinated system inverse control scheme is proposed. First, the inverse models for the unit load and main steam pressure are established offline with dynamic fuzzy neural network（DFNN） using historical sample data got from the full-scope simulator of a 600-MW supercritical power unit. Then the real-time DFNN inverse control scheme which combines offline training and online model correction is designed and programmed with maTLAB. Finally, control tests are carried out through real-time two-way data exchange with the simulator. The simulation results show that the load response speed under large variable load conditions is greatly improved with the DFNN inverse control scheme compared to the original PID control, which is benefit for the supercritical unit in giving full play to its in-depth peak load and frequency regulation capabilities.

关键词： Supercritical unit dynamic fuzzy neural network inverse model coordinated control on-line correction

来源： cnki会议评论

在线全文

cnki会议

学校读者我要写书评

暂无评论

Intelligent Compensation for the Set Values of PID Controlle...

基于支持向量数据描述和XGBoost的风电机组异常工况预警研究

第三十九届中国控制会议

作者： liangyu ma Wenjie Liu Tingting Chen Qianqian Li School of Control and Computer Engineering North China Electric Power University

Superheated steam temperature（SST） is a very important control parameter during the boiler operation. Because of the non-linearity, large-inertia and long-delay characteristics of the superheater system, cascade PID control scheme is widely adopted for water-spray desuperheating system control. Even so, when the unit load changes greatly and frequently in participating in peak load regulation under automatic generation control（AGC）, with uncertain disturbances including changing coal quality, various furnace wind distribution, the cascade PID control with fixed parameters is often unable to achieve desired SST control effect. However, online tuning of PID parameters is time-consuming, laborious and difficult to realize in actual operation. Therefore, a dynamic optimization compensation strategy, which includes both feedforward compensation based on neural network SST prediction model and control error feedback compensation, is designed for the set values of PID controllers on the top level of the steam temperature control loop, on the premise of not changing the original steam temperature control logic and PID parameters. Control simulation experiments under different load changing conditions are carried out with the full-scope simulator of a 600-MW supercritical power unit. It is shown that, compared with the original control, the control quality of the SST is obviously improved with the intelligent set value optimization compensation scheme.

关键词： Supercritical power units superheated steam temperature neural network modeling PID set value compensation intelligent-optimization

来源： cnki会议评论

在线全文

cnki会议

学校读者我要写书评

暂无评论

电工技术学报 2022年第13期37卷 3241-3249页

作者：马良玉程善珍华北电力大学控制与计算机工程学院保定071003

该文提出一种基于支持向量数据描述(SVDD)和XGBoost模型的风电机组异常工况预警方法。从机组监控与数据采集系统(SCADA)数据中选择与转速和发电功率密切相关的特征变量,利用SVDD算法对建模数据进行预处理,采用XGBoost建立风机正常性能... 详细信息

该文提出一种基于支持向量数据描述(SVDD)和XGBoost模型的风电机组异常工况预警方法。从机组监控与数据采集系统(SCADA)数据中选择与转速和发电功率密切相关的特征变量,利用SVDD算法对建模数据进行预处理,采用XGBoost建立风机正常性能预测模型。以所建预测模型为基础,构建时间滑动窗计算性能评价指标,并根据统计学的区间估计理论合理确定风机性能异常预警指标阈值。采用某风电场1.5MW风电机组SCADA系统记录的若干真实故障案例,开展异常工况预警仿真试验。结果表明:基于SVDD和XGBoost的风机异常工况预警方法,可以有效地清洗数据,及时识别风电机组异常状态,对于提高风电机组运行的安全性具有较好的工程实用意义。

关键词：风电机组支持向量数据描述 XGBoost 性能预测模型异常工况预警

基于CFSFDP与LightGBM的风电机组异常状态预警研究

在线全文

学校读者我要写书评

暂无评论

太阳能学报 2023年第5期44卷 401-406页

作者：马良玉袁乃正华北电力大学自动化系保定071003

提出一种基于快速密度峰值聚类(CFSFDP)和LightGBM模型结合的风电机组异常状态监测方法。首先采用CFSFDP算法对风电机组监控与数据采集(SCADA)数据中的异常工况数据与噪声数据进行清洗;之后利用贝叶斯优化算法搜索LightGBM的最优超参数... 详细信息

提出一种基于快速密度峰值聚类(CFSFDP)和LightGBM模型结合的风电机组异常状态监测方法。首先采用CFSFDP算法对风电机组监控与数据采集(SCADA)数据中的异常工况数据与噪声数据进行清洗;之后利用贝叶斯优化算法搜索LightGBM的最优超参数并建立风电机组正常运行工况预测模型。针对风速随机性的特点,利用时移滑动窗口方法构建异常状态识别指标,并结合核密度估计法计算指标阈值以实现异常工况预警。应用某风场的真实历史故障数据进行实验,结果表明LightGBM预警模型能在故障发生前对风电机组的异常工况进行及时正确的预警,验证了方法的有效性。

关键词：风电机组 CFSFDP LightGBM 核密度估计异常工况早期预警