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纺织面料热阻和湿阻的回归测量法 被引量:5
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作者 陈益松 张聪聪 《东华大学学报(自然科学版)》 CAS CSCD 北大核心 2018年第5期742-748,共7页
为进一步减小纺织面料热阻及湿阻的测量误差,提出面料热阻和湿阻的回归测量法,该方法通过测量1~4层面料的总热阻和总湿阻,经线性回归直接得到面料热阻和湿阻及其上方空气层的热阻和湿阻,解决了传统测量法使用空板空气层热阻或湿阻代替... 为进一步减小纺织面料热阻及湿阻的测量误差,提出面料热阻和湿阻的回归测量法,该方法通过测量1~4层面料的总热阻和总湿阻,经线性回归直接得到面料热阻和湿阻及其上方空气层的热阻和湿阻,解决了传统测量法使用空板空气层热阻或湿阻代替面料上方空气层热阻或湿阻带入系统误差的问题。试验结果表明,回归测量法在热阻测量的准确性方面改善尤为明显,在湿阻测量方面的改善相对较小,还需进一步研究。 展开更多
关键词 纺织面料 热阻 湿阻 回归测量
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考虑轨面设备的无绝缘轨道电路道砟电阻回归测量方法 被引量:7
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作者 赵林海 江浩 +1 位作者 孟景辉 高利民 《中国铁道科学》 EI CAS CSCD 北大核心 2021年第2期154-163,共10页
针对无绝缘轨道电路中因补偿电容和调谐区单元等轨面设备影响而难以准确测量道砟电阻的问题,提出由轨面电流幅值测量、参数拟合和回归计算3部分组成的道砟电阻回归测量方法。根据传输线理论,构建轨面电流幅值包络模型,分析道砟电阻、补... 针对无绝缘轨道电路中因补偿电容和调谐区单元等轨面设备影响而难以准确测量道砟电阻的问题,提出由轨面电流幅值测量、参数拟合和回归计算3部分组成的道砟电阻回归测量方法。根据传输线理论,构建轨面电流幅值包络模型,分析道砟电阻、补偿电容和调谐区设备对轨面电流幅值的影响规律;对轨面电流幅值进行指数拟合,得到不同道砟电阻所对应的衰减因子,构建衰减因子与道砟电阻的回归计算式;基于人工方式实地测量的轨面电流幅值,回归计算得到道砟电阻;通过轨道电路半实物仿真实验平台,对该测量方法分别进行功能验证和性能验证。结果表明:功能验证中,仅测量指定3个位置点的轨面电流,即可较为准确地估算出道砟电阻,绝对误差为0.08Ω⋅km,相对误差为4.04%;性能验证中,计算得到道砟电阻的最大绝对误差仅为0.157Ω⋅km,对应的相对误差为7.9%,且测量结果受补偿电容和调谐区设备故障的影响较小。 展开更多
关键词 铁路信号系统 电气化铁路 无绝缘轨道电路 道砟电阻 轨面电流幅值包络 回归测量
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基于时间近邻拉氏正则的多工况软测量回归 被引量:6
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作者 徐志强 任密蜂 +2 位作者 程兰 李荣 阎高伟 《仪器仪表学报》 EI CAS CSCD 北大核心 2021年第11期279-287,共9页
针对流程工业中,工况改变导致传统软测量模型预测精度下降的问题,考虑到工业数据连续性、序列性、多重共线性、数据量庞大等特殊性对模型建立的影响,提出一种基于时间近邻拉普拉斯正则的多工况软测量回归模型框架。针对工业数据的多重... 针对流程工业中,工况改变导致传统软测量模型预测精度下降的问题,考虑到工业数据连续性、序列性、多重共线性、数据量庞大等特殊性对模型建立的影响,提出一种基于时间近邻拉普拉斯正则的多工况软测量回归模型框架。针对工业数据的多重共线性,回归框架采用非线性迭代偏最小二乘方法,同时引入域适应正则项改善工况变化对模型的影响,在此基础上,提出时间近邻拉普拉斯正则项,能够在映射过程中保持住数据的序列结构,并且大幅度减少模型训练时间以满足工业实时性要求。实验部分以三聚氰胺聚合过程多工况数据集为例,对本文模型的预测有效性以及减少训练时间的有效性进行了实验和分析。结果表明,与传统方法偏最小二乘回归相比,当目标工况为工况1到工况4时,本文方法使平均均方根误差分别降低了30.3%、31.4%、29.3%和24.1%。且相较于传统全连接法,时间近邻法构建拉普拉斯正则项能够使得四个工况上模型训练时间分别降低14.11、1.01、26.43和0.71 s,表明该模型的预测准确性和训练时间均得到有效改善. 展开更多
关键词 流程工业 过程数据 时间近邻拉普拉斯正则 多工况 测量回归模型
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Mapping methods for output-based objective speech quality assessment using data mining 被引量:2
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作者 王晶 赵胜辉 +1 位作者 谢湘 匡镜明 《Journal of Central South University》 SCIE EI CAS 2014年第5期1919-1926,共8页
Objective speech quality is difficult to be measured without the input reference speech.Mapping methods using data mining are investigated and designed to improve the output-based speech quality assessment algorithm.T... Objective speech quality is difficult to be measured without the input reference speech.Mapping methods using data mining are investigated and designed to improve the output-based speech quality assessment algorithm.The degraded speech is firstly separated into three classes(unvoiced,voiced and silence),and then the consistency measurement between the degraded speech signal and the pre-trained reference model for each class is calculated and mapped to an objective speech quality score using data mining.Fuzzy Gaussian mixture model(GMM)is used to generate the artificial reference model trained on perceptual linear predictive(PLP)features.The mean opinion score(MOS)mapping methods including multivariate non-linear regression(MNLR),fuzzy neural network(FNN)and support vector regression(SVR)are designed and compared with the standard ITU-T P.563 method.Experimental results show that the assessment methods with data mining perform better than ITU-T P.563.Moreover,FNN and SVR are more efficient than MNLR,and FNN performs best with 14.50% increase in the correlation coefficient and 32.76% decrease in the root-mean-square MOS error. 展开更多
关键词 objective speech quality data mining multivariate non-linear regression fuzzy neural network support vector regression
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Prediction of dust fall concentrations in urban atmospheric environment through support vector regression 被引量:2
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作者 焦胜 曾光明 +3 位作者 何理 黄国和 卢宏玮 高青 《Journal of Central South University》 SCIE EI CAS 2010年第2期307-315,共9页
Support vector regression (SVR) method is a novel type of learning machine algorithms, which is seldom applied to the development of urban atmospheric quality models under multiple socio-economic factors. This study... Support vector regression (SVR) method is a novel type of learning machine algorithms, which is seldom applied to the development of urban atmospheric quality models under multiple socio-economic factors. This study presents four SVR models by selecting linear, radial basis, spline, and polynomial functions as kernels, respectively for the prediction of urban dust fall levels. The inputs of the models are identified as industrial coal consumption, population density, traffic flow coefficient, and shopping density coefficient. The training and testing results show that the SVR model with radial basis kernel performs better than the other three both in the training and testing processes. In addition, a number of scenario analyses reveal that the most suitable parameters (insensitive loss function e, the parameter to reduce the influence of error C, and discrete level or average distribution of parameters σ) are 0.001, 0.5, and 2 000, respectively. 展开更多
关键词 support vector regression urban air quality dust fall soeio-economic factors radial basis function
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Finite element model updating for large span spatial steel structure considering uncertainties 被引量:4
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作者 滕军 朱焰煌 +2 位作者 周峰 李惠 欧进萍 《Journal of Central South University》 SCIE EI CAS 2010年第4期857-862,共6页
In order to establish the baseline finite element model for structural health monitoring,a new method of model updating was proposed after analyzing the uncertainties of measured data and the error of finite element m... In order to establish the baseline finite element model for structural health monitoring,a new method of model updating was proposed after analyzing the uncertainties of measured data and the error of finite element model.In the new method,the finite element model was replaced by the multi-output support vector regression machine(MSVR).The interval variables of the measured frequency were sampled by Latin hypercube sampling method.The samples of frequency were regarded as the inputs of the trained MSVR.The outputs of MSVR were the target values of design parameters.The steel structure of National Aquatic Center for Beijing Olympic Games was introduced as a case for finite element model updating.The results show that the proposed method can avoid solving the problem of complicated calculation.Both the estimated values and associated uncertainties of the structure parameters can be obtained by the method.The static and dynamic characteristics of the updated finite element model are in good agreement with the measured data. 展开更多
关键词 model updating UNCERTAINTY interval analysis multi-output support vector regression large span spatial steel structure
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