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| 1 | Evaluation of the parameters affecting the roughness coefficient of sewer pipes with rigid and loose boundary conditions via kernel based approaches显示文摘One of the important issues in water transport and sewer systems is determining the flow resistance and roughness coefficient.An accurate estimation of the roughness coefficient is a substantial issue in the design and operation of hydraulic structures such as sewer pipes,the calculation of water depth and flow velocity,and the accurate characterization of energy losses.The current study,applies two kernel based approaches[Support Vector Machine(SVM)and Gaussian Process Regression(GPR)]to develop roughness coefficient models for sewer pipes.In the modeling process,two types of sewer bed conditions were considered:loose bed and rigid bed.In order to develop the models,different input combinations were considered under three scenarios(Scenario 1:based on hydraulic characteristics,Scenarios 2 and 3:based on hydraulic and sediment characteristics with and without considering sediment concentration as input).The results proved the capability of the kernel based approaches in prediction of the roughness coefficient and it was found that for prediction of this parameter in sewer pipes Scenario 3 performed better than Scenarios 1 and 2.Also,the sensitivity analysis results showed that Dgr(Dimensionless particle number)for a rigid bed and wb/y(ratio of deposited bed width,wb,to flow depth,y)for a loose bed had the most significant impact on the modeling process. | Kiyoumars Roushangar Roghayeh Ghasempour Sanam Biukaghazadeh | 2020 | International Journal of Sediment Research2020，35，2： | 2 |
| 2 | 基于ANFIS模型的盘式制动器制动最高温度预测显示文摘本文介绍了一种盘式制动器制动最高温度预测方法。基于自适应神经模糊推理系统的原理给出了预测模型建模的流程,采用非线性函数对影响ANFIS模型预测结果的影响因素进行了探究,指出了各个影响因素对预测结果的影响。基于上述理论基础,建立了盘式制动器制动最高温度预测模型,通过优化影响预测精度的参数,搭建了用于制动器最高温度预测的系统。预测结果表明:该系统可以很好的对不同结构尺寸的盘式制动器制动最高温度进行预测。 | 季景方 | 2018 | 南方农机2018，49，21： | 2 |
| 3 | 基于小波去噪和PCA-ANFIS的SCR脱硝系统建模显示文摘针对选择性催化还原(SCR)脱硝系统非线性、时变和大滞后的特点,本文提出基于小波去噪和主成分分析、自适应神经模糊推理系统(PCA-ANFIS)而建立的SCR脱硝系统预测模型。通过分析不同阈值选取原则及不同小波基和分解层数的去噪效果,选取最适合系统数据去噪的rigrsure原则、软阈值函数、Sym10小波3层分解方式,对数据进行去噪处理,并利用主成分分析法进行数据降维。然后基于减法聚类构建ANFIS模型的初始网络结构,采用混合算法优化模型参数。最后利用某燃煤机组实际运行数据对模型进行验证,并与BP神经网络模型预测结果进行对比。结果表明,基于小波去噪和PCA-ANFIS的SCR脱硝系统模型具有较好的拟合精度和泛化能力。 | 张晓雯 向文国 陈时熠 刘全军 徐龙飞 | 2021 | 热力发电2021，50，6： | 1 |
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